IoT protocols and algorithms: foundations for optimal choices in connected projects

IoT protocols and algorithms: foundations for optimal choices in connected projects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IoT protocols and algorithms: foundations for optimal choices in connected projects Saidou Haman, Djorwe Temoa, Pr Kolyang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7530677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Internet of Things (IoT) is growing rapidly and transforming many areas such as healthcare, home automation, automotive and industry. This expansion is based on the interconnection of heterogeneous devices with limited capabilities (energy, memory, computing) via adapted protocols and algorithms. This paper highlights the state of the art in IoT protocols and algorithms used in IoT gateways, in order to better understand the challenges associated with network heterogeneity and varied application requirements. IoT protocols can be classified into three main families: (i) communication protocols (RFID, NFC, BLE, Wi-Fi, ZigBee, LoRa, Sigfox, NB-IoT, LTE-M, EC-GSM, etc.) according to their range and energy consumption; (ii) application protocols (MQTT, XMPP, CoAP, WebSocket) developed specifically for constrained environments; and (iii) other essential protocols, including security protocols (TLS, DTLS) and service discovery protocols (mDNS, DNS-SD). The choice of protocol depends heavily on the type of application, the required throughput and energy constraints. On the algorithmic side, research focuses mainly on optimal resource allocation and energy optimisation in IoT gateways. Various studies have proposed: · user-centric network selection algorithms (Abdellatif et al., Liu et al.), · mechanisms for assigning services to interfaces (Angelakis et al.), · and energy optimisation solutions with adaptive selection of wireless technologies (Di Mu et al.). The aim of our article is to provide an in-depth overview of the state of the art in protocols and algorithms used in connected objects, using summary tables. The growing integration of the IoT into our daily lives requires a better understanding of these technologies in order to use them effectively and improve them in line with real needs. Internet of Things (IoT) Communication protocols Optimisation algorithms IoT gateways Protocols Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The internet has evolved in ways we could never have imagined. In the early days, progress was slow. Today, innovation is happening at a rapid pace. Modern life has been transformed by information and communication technologies (ICT), particularly the internet and the services it offers: social networks, e-commerce, e-learning, webinars, to name but a few. In this context, it is worth mentioning the advent of the IoT (Internet of Things). According to the International Telecommunication Union , the Internet of Things (IoT) is a " global infrastructure for the information society, enabling advanced services by interconnecting objects ( physical or virtual) through existing or evolving interoperable information and communication technologies ". In reality, the definition of the latter is not set in stone. From a conceptual point of view, the Internet of Things is defined as a network of intelligent objects of all kinds designed to support humans in their daily activities through their detection, computing and communication capabilities. These are referred to as connected objects. This new technology is based on the combination of embedded devices and intelligent software connected to the internet, offering a multitude of applications and services ranging from transport and supervision to personal well-being. The IoT is growing rapidly, benefiting from the creation of cloud computing and its autonomy. Nowadays, IoT equipment can be found in almost every field, such as home automation, healthcare, elderly care, automotive, industrial automation, traffic management, etc. The evolution of this technology has been remarkably rapid. In 2003, the world population was close to 6 billion and there were half a billion devices connected to the Internet. The idea of the Internet of Things emerged in 2009, boosted by the advent of smartphones, and the number of devices connected to the Internet reached 12.5 billion in 2010, while the world population was 6.8 billion. Today, the IoT is gaining momentum and, looking to the future, experts estimate that the number of sensors connected to the Internet could increase by several million or even several billion as everything that exists becomes connected (animals, lamps, houses, people, shoes, trees, etc.). However, it should be noted that IoT devices are heterogeneous and highly restrictive. These devices vary in terms of computing power, storage capacity, communication protocols, networking capabilities, battery life, etc. In [ 02 ], for example, Andrea Zanella et al conducted a study on an urban IoT system as part of the Padova Smart City project, which involves several IoT applications with different requirements in terms of throughput, battery life, tolerated latency, memory, etc. In fact, there is a variety of IoT devices with multiple interfaces. In a system of connected objects, intermediate devices with multiple interfaces are used, implementing protocols associated with algorithms to enable data exchange between IoT devices from different networks. However, given the multitude of interfaces available to gateways, IoT devices can transmit their flows randomly. This mechanism can cause network congestion, as several IoT devices may decide to transmit their data on a given interface. In addition, the choice of an inappropriate interface by an IoT device can also be costly in terms of autonomy, security, memory resources, etc. To remedy this problem, a control mechanism must be implemented in the gateways. The main objective of this mechanism will be to assign a data flow from an application to an interface that is considered available and optimal for that application. In addition, this assignment should be made intelligently and dynamically rather than statically. This mechanism for the intelligent management and allocation of shared resources in an IoT network is based mainly on implemented protocols and algorithms. In this chapter, we will review the various works carried out in relation to IoT protocols and algorithms used in intermediate devices in order to understand how the problem of object and network heterogeneity is addressed for better data exchange. The work carried out in this field can be classified into two main categories. 2. State of the art in IoT protocols and algorithms We will first discuss Internet of Things (IoT) protocols, then present artificial intelligence (AI)-based algorithms that enable dynamic allocation of shared resources, in order to provide further details. A. IoT protocols. As mentioned above, the intermediate device of IoT devices (also known as an IoT gateway) enables data exchange between different IoT nodes from heterogeneous networks on the one hand, and connects them to the public network (the Internet) on the other. In order to perform this function, it supports a variety of protocols. Depending on their relevance to the application used, we can classify these into three main categories: communication protocols, application protocols and other influential protocols. However, not all of these protocols need to be grouped together to provide a given IoT application. Communication Protocol In general, a communication protocol is defined as a set of rules that define how communication between a transmitter and a receiver takes place. In the concept of connected objects, these transmitters and receivers are IoT devices that communicate with the cloud server on the one hand and with each other via the IoT gateway on the other. Thus, a communication protocol enables IoT devices to exchange data in a regulated manner. This exchange can be carried out over a more or less significant port. Short-range communication protocols. We will start with short-range communication protocols. In a short-range network, IoT devices communicate in a closed environment, and these networks allow data to be transferred over short distances. RFID, or Radio Frequency Identification, is a technology that allows data to be memorised, stored or recorded on a medium and retrieved remotely [03]. This technology is often used in tags or badges for identification purposes. NFC (Near Field Communication) is a promising short-range wireless communication technology that facilitates the use of mobile phones by billions of people around the world, offering various services ranging from payment and loyalty applications to access keys for offices and homes [04]. According to [05], NFC technology enables various contactless applications in the fields of transport, health and commerce. Medium-range communication networks Bluetooth Low-Energy (BLE) or Bluetooth Smart is a communication protocol that uses short-range radio with minimal power to operate for a long time compared to conventional Bluetooth. Its coverage range is approximately 100 metres, while its transmission power is between 0.01 mW and 10 mW. With these characteristics, BLE is an ideal candidate for IoT applications. Wi-Fi technology is widely used as a standardised wireless communication protocol on computer networks [06]. It is a communication protocol that offers great freedom, mobility and ease of connection and real-time information exchange. Wi-Fi technology uses an unlicensed spectrum in the 2.4 GHz and 5 GHz bands to provide connectivity between IoT devices and a local Internet access point. Zigbee is a wireless personal area network (WPAN) that stands out from its two main competitors (Wi-Fi and Bluetooth) due to its simplicity of implementation and low energy consumption. ZigBee is used to create networks requiring low data transfer rates (lower than Bluetooth and Wi-Fi). Long-range, low-speed communications networks We have looked at short- and medium-range communication protocols in the previous sections. We will now logically move on to the protocols used for long-range IoT device communication. These protocols meet the need to connect IoT devices to the internet. They use radio waves, which are very effective in rural areas. However, urban areas with tall buildings, trees and houses are more difficult for radio waves to traverse. In this category, we distinguish between two main families. The first family consists of low-speed, long-range networks, also known as LPWAN (Low-Power Wide-Area Network). The second family is that of cellular networks. Low-Power Wide-Area Network LPWAN stands for Low Power Wide Area Network. Low-power wireless technology (LPWAN) is the answer to the need for a system that connects low-power devices to the internet for low-speed data capture [07]. The Long Range (LoRa) network is becoming one of the most promising low-power wide-area networks (LPWANs) because it enables energy-constrained devices spread over large areas to establish affordable connectivity [08]. However, LoRa is an ideal protocol for IoT devices. It enables long-range transmission and is deployed without a licence, allowing private and public operators to deploy their own networks without monthly subscription fees. It operates on two ISM bands below 1 GHz. In Europe, the first band ranges from 868 MHz to 868.6 MHz, while the second is centred on 433 MHz. As these bandwidths are narrow, data rates are below 37.5 kbit/s (LoRa) and 50 kbit/s (FSK) for transmission [SWH17]. LoRa can also operate on the 2.4 GHz ISM frequency band. The maximum data rate is 200 kbit/s in LoRa and 2 Mbit/s in (G)FSK/MSK. This frequency band would allow higher data rates and eliminate the duty cycle. The duty cycle is a limitation on the use of the 433 MHz and 868 MHz ISM bands. Sigfox is a low-energy, long-range communication protocol. It was developed in 2006 by a French company (Sigfox) for the Internet of Things (IoT). It is LoRa's main competitor. It enables IoT devices to communicate with each other at very low data rates and with very low energy consumption. However, Sigfox is a proprietary network, which requires users to sign up with Sigfox and continue to pay the company to maintain deployment. It is currently available in 72 countries. LoRa and Sigfox are not the only LPWAN technologies. There are others, such as DASH7 and Ingenu. They all have their own specific features. For example, DASH7, which is also an LPWAN protocol, has the particularity of supporting tree-type topologies in addition to star topologies. Ingenu is one of the few LPWAN solutions operating on the 2.4 GHz ISM frequency band. It has a range of up to 15 km and a data rate of up to 8 kbit/s. A comprehensive analysis of this protocol is provided in [09] by Maxime Mroue. In his work, the author identified the obstacles that have been overcome, including packet rejection rates, packet collisions, etc. Cellular networks A cellular network, also known as a mobile phone network, is a network that allows the simultaneous use of a large number of IoT devices, whether mobile (even when travelling at high speed) or static. LTE-MTC is a version of LTE (Long Term Evolution) technology designed for IoT. It is also known as LTE-M or eMTC. In order to increase the "lifespan" of IoT devices, LTE-M operates on a 1.4 MHz frequency band in its version 13. However, it can reach 1 Mbit/s. In its version 14 (LTE-M2), it uses a 5 MHz frequency band. This ultimately gives us speeds of around 3 MHz. LTE-M uses the 900 MHz LTE band [PR16]. Its range is greater than that of conventional LTE. It reaches up to 5 km in urban areas and 17 km in rural areas. The Narrowband IoT (NB-IoT) communication protocol is also based on the LTE (Long Term Evolution) protocol. NB-IoT minimises the functionality of the LTE protocol and improves it according to the needs of IoT applications [10]. NB-IoT operates with a very narrow frequency band of only 180 kHz. Data rates are below 250 kbit/s [Che+17]. The payload can reach 1600 KB [Mek+18]. NB-IoT uses the 900 MHz LTE band. Its range can be up to 8 km in urban areas and 25 km in rural areas. The EC-GSM-IoT protocol is an extension of EDGE for IoT. It is also known as EC-GSM. It is defined in version 13 of LTE. EC-GSM operates on a reduced frequency band compared to EDGE frequency bands. As EDGE network infrastructure is already in place, EC-GSM uses EDGE as its basis. However, a few adjustments have been made to optimise it for IoT devices. [11] provides some concepts regarding the contributions of the EC-GSM-IoT protocol. Application protocols The advent of the Internet of Things has placed remarkable pressure on the Internet. As a result, new protocols have been implemented, including application protocols such as MQTT, XMPP, DDS and CoAP. Traditional protocols such as HTTP are not suitable due to the significant constraints in terms of energy consumption and computing capacity. In this section, we will present the application protocols specially developed for IoT devices. These are organised into two groups: Publish/Subscribe protocols (MQTT, XMPP) and client/server protocols (COAP, WebSocket). Publish/Subscribe protocols Publish-subscribe is a messaging model in which senders, known as publishers, publish a message (associated with a category) without specifying recipients in advance. On the other side, potential recipients, known as subscribers, subscribe to the categories that interest them and only receive the corresponding messages via a broker called EMQ, without knowing if there are other publishers. Fig. 2 illustrates the publish-subscribe process. There are several types of protocols that use the publish-subscribe model. However, in this section, we will present MQTT and the XMPP protocol. The MQTT protocol MQTT is a messaging protocol that was introduced by Andy Stanford-Clark of IBM and Arlen Nipper of Arcom (now Eurotech) in 1999 and was standardised in 2013 at OASIS [13]. It is based on a client/server architecture where sensors act as clients and then connect to a server called a broker. It is a lightweight and easy-to-implement protocol. It is widely used in IoT device connectivity and M2M (machine-to-machine communication). It uses the publish-subscribe model to offer transition flexibility and simplicity of implementation, as illustrated in the following figure. The MQTT protocol consists of three main components: a subscriber, a publisher, and a broker. As shown in the architecture above, publishers publish topics of interest. An interested device registers as a subscriber for specific topics in order to be notified by the broker (EMQ) of topics of interest published by publishers. The latter act as generators of interesting data. Publishers transmit information to interested entities (subscribers) via the broker (EMQ). In addition, the EMQ ensures security by verifying the authorisation of publishers and subscribers. The MQTT protocol is used in several applications such as monitoring and energy metering. It is also capable of providing routing for small, low-power, low-memory devices in vulnerable, low-bandwidth networks. Therefore, the MQTT protocol remains an ideal messaging protocol for IoT devices. However, MQTT is not supported by NDN, so an MQTT gateway to CCN has been proposed to enable interoperability between CCN IoT devices and the current Internet [15]. A detailed study of the MQTT protocol is carried out by Dipa Soni et al in [16]. The XMMP protocol XMPP is a communication protocol based on XML (Extensible Markup Language). XMPP is used for MOM (Message-Oriented Middleware), which is software designed to send and receive messages between different systems. The XMPP protocol, originally called Jabber, was created in 1999 by the Jabber open-source community. XMPP allows applications to communicate with each other regardless of the operating system they use by sending instant messages over the Internet. It also allows instant messaging applications to obtain authentication, access control, confidentiality measurement, hop-by-hop and end-to-end encryption, and compatibility with other protocols. In [17], Heng Wang et al. proposed a publish/subscribe mechanism based on the XMPP protocol. In their study, the authors focused on resource-constrained IoT devices in order to perform periodic data exchange. The following figure illustrates the architecture of the XMMP protocol. Client/server protocols. The client/server protocol is a model for transactions between multiple programmes or processes via a network. In this model, there are two main players: a client, which sends requests to the server, and a server, which waits for requests from clients, processes them, and respond . There are several types of client/server protocols. In this section, we will present the protocols most commonly used by IoT applications, namely the COAP protocol and the WebSocket protocol. COAP protocol CoAP (Constrained Application Protocol) is a web transfer protocol designed to run on constrained devices, which are generally IoT devices. It is based on the REST architecture. REST represents a simpler way to exchange data between clients and servers via HTTP [19]. The CoAP protocol is a lightweight version of HTTP, which relies on UDP. This makes it more suitable for IoT applications. It should also be noted that the CoAP protocol modifies certain HTTP features to meet IoT requirements such as low energy consumption. Consequently, Carsten Bormann et al presented the COAP protocol as the protocol par excellence for constrained nodes and networks, including IoT, in [20]. WebSocket protocol Unlike the previously presented protocol (CoAP), the WebSocket protocol is a TCP-based network protocol. It is a protocol implemented in the IoT Hub, which makes it easy to connect a web application to the Hub. When a Hub is created, two networks are also created by default. These are the MQTT and WebSocket networks. MQTT is used for its seamless message exchange between constrained devices (such as IoT devices) and the hub. WebSocket allows MQTT messages to be sent and received directly in a browser, so that web applications can connect directly to the Hub without the need for middleware and APIs. Other important protocols DNS protocols DNS protocols were originally designed for resource-rich devices. However, their enhanced versions have been adapted for IoT applications. The scalability of the IoT requires an efficient system for resource management. This system will enable the registration and discovery of resources and services in a self-configuring, efficient and dynamic manner. Thus, the most dominant protocols in this area are Multicast DNS (mDNS) and DNS Service Discovery (DNS-SD), which can discover the resources and services offered by IoT devices. Security protocols New IoT mechanisms cannot be secured by conventional security protocols. Internet security protocols are designed to work on traditional devices without significant constraints on computing and storage resources, such as computers. Furthermore, the emergence of new protocols and architectures to support the IoT points to new security issues, and this concern should be addressed at all layers of the IoT, from the application to the infrastructure layers, including securing data within resource-constrained devices. B. Algorithms used for dynamic allocation of shared resources (IoT gateway). In a system where several IoT devices are connected to each other to exchange data between themselves on the one hand and with the remote server on the other, there are inevitably different communication, application and security protocols, etc. In such an exchange, the connected devices do not necessarily have the same characteristics in terms of communication capacity, memory capacity, energy capacity, etc. This is referred to as IoT device heterogeneity. For optimal exchange at both the device and network levels, intermediate equipment serving as an IoT gateway is deployed. IoT gateway An IoT gateway establishes a bridge between IoT devices, sensors and the cloud to enable data exchange. It systematically connects "the field", consisting mainly of sensors, to the remote server or cloud. However, the heterogeneous nature of sensors and networks means that gateways must support different types of interfaces (e.g. Power Line Communication (PLC), Bluetooth, cellular or Wi-Fi) and network protocols [21]. Depending on the nature of the sensors or networks, the configuration of an IoT gateway must also change to ensure interconnection. However, the current problem with an IoT gateway is that the configuration enabling interconnection between heterogeneous networks is not performed automatically and intelligently. Furthermore, this gateway, considered a common resource between different devices, is not dynamically and optimally allocated to them. A variety of research on the IoT gateway has been carried out. In [22], Remy Leone presented an intelligent gateway for constrained wireless sensor networks. He proposed an IoT gateway capable of estimating the energy consumed by the various nodes assigned to it without having to use control messages that are costly in terms of bandwidth and energy consumption. In this approach, the IoT gateway has complete control over the sensors assigned to it. In this work, Remy Leone's main goal was to add new features to the gateway of an LLN with a view to optimising the use of the LLN's limited resources and improving knowledge of its operating status. Similarly, the IoT gateway proposed by Remy Leone makes it possible to determine the optimal resource validity times to be used within an application cache. A solution for automatic quality of service management and security in an Internet of Things ecosystem is presented in [23]. In his work, Ahmad Khalil proposes an IoT gateway integrating two main mechanisms. The first mechanism is based on differentiated treatment of incoming and outgoing traffic in the IoT environment. It allows each type of traffic to be handled according to different QoS parameters. This mechanism also gives the IoT gateway autonomy through two important functions: self-configuration and self-optimisation. The second mechanism allows control over the access of IoT devices to IoT gateways. Like quality of service management, this access control is also provided automatically by the IoT gateways. In [24], Qian Zhu et al presented an IoT gateway based on Zigbee and GPRS protocols depending on the scenario. Depending on the requirements of telecommunications operators and the type of application used, the IoT gateway presented allows for automatic protocol conversion. The Zigbee protocol is used to connect sensors to the gateway. It is a medium-range network. GPRS is used to connect the IoT gateway to the cloud for processing or storing the data collected by the sensors. In [25], Shang Guoqiang et al proposed an intelligent IoT gateway that enables end-to-end connectivity between heterogeneous networks (Zigbee, wireless local area network, wired network, etc.). It is a configurable gateway that has three important advantages. First, the gateway has an architecture where communication protocols can be customised and plugged in according to different networks. Second, the gateway has unified external interfaces suitable for flexible software development. Finally, it allows different sensor data to be translated into a uniform format. It should also be noted that the gateway offers greater flexibility and scalability compared to similar research. A reliable, self-configuring IoT gateway is being tested by Byungseok Kang et al in [26]. Traditional systems do not take IoT devices into account. As a result, with the evolution of the IoT, new intermediate devices specially designed to ensure compatibility between the IoT and existing systems will be needed. Furthermore, current IoT gateways operate in semi-automatic mode. In other words, when a new IoT device is to be added to a gateway, the user installs it manually and then also configures the gateway to register the new device. In their work, Byungseok Kang et al. placed particular emphasis on a reliable IoT gateway where, when a new device is to be added, the IoT gateway automatically detects it and registers it itself. In [27], Apostolos Papageorgiou et al proposed a solution enabling automatic configuration based on an interpretable configuration. This solution is based on algorithms for calculating suggested configurations. Five algorithms are proposed, while an in-depth evaluation reveals which of these algorithms are the most effective and should be used in different operating scenarios in order to achieve a high level of compliance with the operator's objectives. A careful analysis of the work presented above shows that an IoT gateway fulfils several functions: resource optimisation, protocol translation, security management (encryption, decryption), local data processing, optimal data exchange between heterogeneous devices, adjusting the heterogeneity between these various networks, data transmission to the cloud for in-depth processing and storage, etc. However, these functions are made possible by the algorithms they implement. Algorithms implemented in IoT gateways IoT devices and the networks they are part of have several different characteristics, as highlighted in the introduction. This heterogeneity creates significant challenges when networking or allocating shared resources. Indeed, IoT gateways implement algorithms for optimal resource allocation. These algorithms have several objectives: optimal management of congestion in the gateway, optimal management of data and communication security, optimal management of application memory resources, etc. Thus, IoT gateways are effective if and only if they implement appropriate algorithms. In this section, we will review related work on algorithms implemented in IoT gateways. . Algorithms for optimal interface allocation IoT devices and wireless networks have multiple characteristics, as mentioned in section A.1. This heterogeneity makes M2M communication very complex. In an IoT gateway with a multitude of interfaces, for example, an application's data flows must pass through an interface that is considered optimal and available. Selecting the appropriate interface for a given application is crucial. It serves to avoid several undesirable effects in the network, such as congestion, data loss, long queues, poor quality of service, etc. The selection of this interface is ensured by appropriate algorithms. In this section, we will present the work carried out in the literature that has proposed algorithms for selecting interfaces in an IoT gateway for optimal use of resources. In [28], an effective mechanism for network selection on heterogeneous wireless networks is proposed. In this work, Alaa Awad et al. placed particular emphasis on user preferences regarding the use of heterogeneous wireless networks in order to improve system performance and support reliable connectivity. User preferences can be summarised in three parameters: latency, cost and energy consumption. In this system, the user is at the centre of the gateway's decision-making processes. Another network selection mechanism based on user preferences is proposed by Liu et al. in [29]. In this work, the authors presented a user-centric QoE-focused algorithm. The algorithm uses the handover mechanism based on the MEDIA Independent Handover (MIH) framework designed by the IEEE 802.21 group. MIH allows the selection of an appropriate network from among those available and then ensures data transfer. Indeed, the main objective of the proposed algorithm is to select an appropriate network based on user preferences in order to maintain acceptable quality of experience (QoE). When selecting a network, cost and QoE are the most important constraints. The operation of this algorithm is described in Figure 6. In this operation, the QoE-Estimators module first collects network and service parameters. These parameters will be used to predict the quality of experience in terms of MOS (Mean Opinion Score). On the other hand, user preferences are also collected. Once all network and service parameters and user preferences have been collected, the predicted MOS value is sent to a handover decision algorithm. After analysis and decision-making by the latter, a message is transmitted to the MIH Function module. This module is responsible for the handover. In [30], Vangelis Angelakis et al presented a mechanism called SIA (Service-to-Interface Assignment). As its name suggests, this mechanism aims to assign one or more services to a given interface. In this work, they considered multi-interface IoT equipment to highlight the problem of efficient resource allocation in a network. Given that IoT equipment is required to provide a wide variety of services with different requirements, the mechanism proposed by Vangelis Angelakis et al. uses two different algorithms to optimally solve the interface assignment problem. The first algorithm takes into account the most demanding services and immediately allocates interfaces to them. The second algorithm first calculates the existing resource shares and then allocates them to services by random selection. This mechanism is shown in Figure x, which illustrates a case where two services are requesting interfaces. The first service is represented by the colour yellow, while the second uses the colour orange . The shared resource receiving these requests has three interfaces (i1, i2 and i3). Each interface offers a separate resource (k1 or k2 or k3). Initially, the yellow service requests interface i1, while the orange service requests interface i2. Faced with these requests, the gateway must make a decision. To do this, the yellow service's request is allocated to interfaces i2 and i3, while the orange service's request is allocated to i1 and i3. Finally, a simulation result is obtained, providing an overview of the distribution of services across different interfaces for both cases. The figure below illustrates how this algorithm works. . Energy consumption optimisation algorithms As mentioned in the previous sections, IoT devices are very restrictive in terms of computing capacity, memory resources and energy. In order to provide an optimal solution to the latter, several studies have been carried out in the literature. In this section, we will review the work proposing energy consumption optimisation algorithms in IoT networks. In [31], Di Mu et al proposed an adaptive radio and transmission power selection algorithm that provides several wireless technologies. This system supports several wireless protocols such as WiFi, Zigbee, and Bluetooth. The mechanism consists of selecting the appropriate network for traffic and network conditions while minimising energy consumption. It is known as ARTPoS (Adaptive Radio and Transmission Power Selection system). In its architecture, ARTPoS incorporates a selection module that chooses the most optimal radio interface. This choice is made based on the bit rate and residual capacity of the network. In addition, ARTPoS incorporates a second module whose role is to control the radio interfaces selected by the first module. In this module, energy consumption is calculated for the selected interfaces. The results obtained at the end of these calculations are analysed by the first module for a final decision. At the end of their work, the authors obtained an algorithm that significantly reduces energy consumption while maintaining the desired link reliability. The ARTPoS architecture is shown in the figure below. In [32], Huai-Lei Fu et al developed a control mechanism for real-time, low-energy M2M communication. In a surveillance application, for example, sensors transmit detected data to an IoT gateway. This transmission must be done in real time. In order to maintain this exchange over a long period of time, IoT nodes and gateways must first and foremost have sufficient autonomy. 3. The problem Despite the diversity of protocols and algorithms available, there is no universal solution suitable for all IoT scenarios. As Verma, Singh & Prasad (2022) pointed out in their review of IoT protocols applied to connected health, each application imposes specific constraints in terms of security, latency and energy efficiency. The central issue therefore remains: how can we design generic, adaptive and secure protocols and algorithms capable of meeting the varied needs of heterogeneous IoT environments, while minimising complexity and energy consumption? 4. Methodology It consists of conducting an in-depth study of the various protocols and algorithms, referring to the following elements such as frequency, range and handover, as mentioned above, before producing two (02) summary tables so that everyone knows where to go. We will therefore produce two summary tables, which we will present to you as results. 5. Results Our manuscript consists of two main parts: Part A, devoted to protocols, and Part B, devoted to algorithms. The results we present are the outcome of an in-depth study of these two areas, with the aim of providing researchers with additional details and insights. In Part A of our article, we reviewed studies conducted on IoT protocols. Although IoT devices are very demanding in terms of computing power, memory capacity, energy capacity and, above all, networking capacity, there is a wide range of protocols designed to meet these requirements. In fact, no one protocol is better than another. The choice of protocol depends on the type of application to be implemented. In a traffic control application, for example, since the equipment is battery-powered, energy consumption must be as low as possible to enable data to be sent quickly to drivers. This data is used by drivers to make better decisions in the event of an accident on a road, for example. In this scenario, it is useful to use a protocol characterised by low energy consumption, such as the Zigbee or LoRa protocol. On the other hand, in an agricultural application designed to send images of plant leaves to a remote server for in-depth analysis, for example, a protocol offering high bandwidth is ideal. In this application, sending images of plant leaves requires much more bandwidth than sending a simple humidity reading, for example. For this reason, it would be preferable to use a protocol such as Wi-Fi rather than Zigbee or LoRa. The table below presents a comparative study of the different IoT protocols. Many protocols have been discussed, but we have selected and indexed those that are most commonly used. Table 1: Comparative table of protocols Parameters WiFi Bluetooth LR-WPAN LoRa Standard IEEE 802.11 a/c/b/d/g/n IEEE 802.15.1 IEEE 802.15.4 LoRaWAN Frequency Band 5 GHz – 60 GHz 2.4 GHz 868/915 MHz / 2.4 GHz 868/900 MHz Data Rate 1 Mb/s – 1 Gb/s 1 – 24 Mb/s 40 – 250 Kb/s 0.3 – 50 Kb/s Coverage 20 – 100 m 8 – 10 m 10 – 20 m < 30 km Energy Consumption High Medium (Bluetooth): Low Low Low Cost High Low Low High In Part B of our article, we have reviewed some of the work proposing algorithms for optimal interface allocation in an IoT network, as well as those enabling optimal energy consumption. The following table contains a comparative study of this work and its limitations. Table 2: Comparative table of algorithms Authors Contributions Limitations A. A. Abdellatif, C.-F. Chiasserini & A. Mohamed • Considers user preferences and network characteristics • Optimizes IoT device performance • No exact formulation of delays (transfer, processing, handover) • No automatic handover to optimal interface Liu, Sun L. & Ifeachor E. • Considers Quality of Experience (QoE) • Handover based on MEDIA Independent Handover framework • Long/complex procedure → high energy consumption V. Angelakis, I. Avgouleas, N. Pappas, E. Fitzgerald & D. Yuan • Uses two algorithms simultaneously for better optimization • High memory cost • No consideration of user preferences • No real-world deployment Di Mu, Y. Ge, M. Sha, S. Paul, N. Ravichandra & S. Chowdhury • Provides solutions for mobile apps via multiple wireless technologies • Selects network according to traffic & conditions while minimizing energy consumption • Considers data loss • Does not consider deadlines of received flows 6. Conclusion We have presented the various protocols and algorithms in detail, highlighting numerous criteria such as frequency, bandwidth, range and areas of application. Our aim is to highlight these solutions used in connected objects, through two tables drawn up following an in-depth study. However, it should be noted that their implementation still faces access problems, particularly related to network heterogeneity, latency constraints, congestion, and security and reliability issues. These limitations become particularly critical in sensitive contexts such as war situations, where connectivity is unstable and infrastructure is often damaged, but where connected objects can play a crucial role in locating victims, providing medical monitoring or coordinating relief efforts. Similarly, during natural disasters such as floods or earthquakes, the IoT can help save lives by ensuring early risk detection, rapid transmission of alerts and real-time monitoring of disaster areas, despite access and deployment constraints. We believe that after reading our article, you will be able to make informed choices for your IoT-related projects. Furthermore, in the era of artificial intelligence, which is currently at the forefront of the news, these protocols and algorithms can be enriched and improved through intelligent integration. Declarations Author Contribution Saidou HAMAN: ResearcherDjorwe Temoa: Support Kolyang : Mentor References Tafazolli, R. (ed.): Technologies for the Wireless Future, Volume 2: Wireless World Research Forum (WWRF), vol. 2. Wiley (2006) Cenedese, A., Zanella, A., Vangelista, L., Zorzi, M.: Padova smart city: An urban internet of things experimentation. In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 (pp. 1–6). IEEE. (2014), June JDN: RFID: définition, fonctionnement, lien avec le NFC. [En ligne] url https://www.journaldunet.fr/web-tech/dictionnaire-de-l-iot/1440692-rfid-definition-fonctionnement-lien-avec-le-nfc/ . (visité le visité le 30/08/2025) Coskun, V., Ozdenizci, B., Ok, K.: A survey on near field communication (NFC) technology. Wireless Pers. Commun. 71 (3), 2259–2294 (2013) Yacine, Z.E.N.A.G.: La technologie NFC - Principes de fonctionnement et applications. [En ligne] url: https://www.techniques-ingenieur.fr/base-documentaire/technologies-de-l-information-th9/internet-des-objets-42612210/la-technologie-nfc-s8650/ . (visité le visité le 30/08/2025) Housbane, S., Serhier, Z., Othmani, M.B.: INTEGRATION DU WIFI DANS LE DOMAINE DE LA SANTE. Revue Marocaine de Santé Publique, 1 (1). (2014) Pierre Kassaa: L’internet des Objets (IoT) et les réseaux haut et bas débit, est-ce un outil essentiel pour une ville intelligente et moins Polluée? [En ligne] url: https://www.scribd.com/document/510428644/Kassaa-Pierre . (visité le visité le 30/008/2025) Zhou, Q., Zheng, K., Hou, L., Xing, J., Xu, R.: Design and implementation of open LoRa for IoT. Ieee Access. 7 , 100649–100657 (2019) Mroue, M.: Développement de réseaux radio d'objets connectés pour les Villes Intelligentes: amélioration de la Qualité de Service du réseau LoRa (Doctoral dissertation, UNIVERSITE DE NANTES). (2019) Mekki, K., Bajic, E., Chaxel, F., Meyer, F.: A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express. 5 (1), 1–7 (2019) Finnegan, J., Brown, S.: A comparative survey of LPWA networking. arXiv preprint arXiv:1802.04222. (2018) Ardagna, C.A., Damiani, E., Schütte, J., Stephanow, P.: A case for IoT security assurance. In: Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, pp. 175–192. Springer Singapore, Singapore (2017) Kobo, H.I., Abu-Mahfouz, A.M., Hancke, G.P.: A survey on software-defined wireless sensor networks: Challenges and design requirements. IEEE access. 5 , 1872–1899 (2017) Al-Masri, E., Kalyanam, K.R., Batts, J., Kim, J., Singh, S., Vo, T., Yan, C.: Investigating messaging protocols for the Internet of Things (IoT). IEEE Access. 8 , 94880–94911 (2020) Saadaoui, F.Z., Maizate, A., Ouzzif, M.: État d'art sur les protocoles en temps réel pour l'internet des objets sous le réseau NDN. In Colloque sur les Objets et systèmes Connectés. (2019), June Soni, D., Makwana, A.: A survey on mqtt: a protocol of internet of things (iot). In International conference on telecommunication, power analysis and computing techniques (ICTPACT-2017) (Vol. 20, No. April, 2017). (2017), April Wang, H., Xiong, D., Wang, P., Liu, Y.: A lightweight XMPP publish/subscribe scheme for resource-constrained IoT devices. IEEE Access. 5 , 16393–16405 (2017) Element14: The XMPP Protocol Benefits, Architecture and Applications. [En ligne] url: https://community.element14.com/learn/learning-center/the-tech-connection/w/documents/4149/the-xmpp-protocol-benefits-architecture-and-applications . (visité le 30/08/2025) Zhao, M., Li, H., Li, Y., Fang, L., Chen, P.: Non-orthogonal multi-carrier technology for space-based Internet of Things applications. In International Conference on Communications and Networking in China (pp. 37–45). Cham: Springer International Publishing. (2017), October Bormann, C., Castellani, A.P., Shelby, Z.: Coap: An application protocol for billions of tiny internet nodes. IEEE Internet Comput. 16 (2), 62–67 (2012) Leone, R.: Passerelle intelligente pour réseaux de capteurs sans fil contraints (Doctoral dissertation, Télécom ParisTech). (2016) Leone, R.: Passerelle intelligente pour réseaux de capteurs sans fil contraints (Doctoral dissertation, Télécom ParisTech). (2016) Khalil, A.: Gestion autonome de la qualité de service et de la sécurité dans un environnement Internet des objets (Doctoral dissertation, Université Bourgogne Franche-Comté). (2019) Zhu, Q., Wang, R., Chen, Q., Liu, Y., Qin, W.: Iot gateway: Bridgingwireless sensor networks into internet of things. In 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (pp. 347–352). Ieee. (2010), December Guoqiang, S., Yanming, C., Chao, Z., Yanxu, Z.: Design and implementation of a smart IoT gateway. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing (pp. 720–723). IEEE. (2013), August Kang, B., Choo, H.: An experimental study of a reliable IoT gateway. Ict Express. 4 (3), 130–133 (2018) Papageorgiou, A., Zahn, M., Kovacs, E.: Auto-configuration system and algorithms for big data-enabled internet-of-things platforms. In: 2014 IEEE International Congress on Big Data, pp. 490–497. IEEE (2014, June) Abdellatif, A., Mohamed, A., Chiasserini, C.F.: User-Centric Network Selection in Multi-RAT Systems. In IEEE WCNC 2016 Workshop on International Workshop on Mobile Edge Computing and IoT. IEEE. (2016) Liu, L., Sun, L., Ifeachor, E.: User-centric QoE-driven vertical handover framework in heterogeneous wireless networks. In 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1–8). IEEE. (2016), October Angelakis, V., Avgouleas, I., Pappas, N., Fitzgerald, E., Yuan, D.: Allocation of heterogeneous resources of an IoT device to flexible services. IEEE Internet Things J. 3 (5), 691–700 (2016) Mu, D., Ge, Y., Sha, M., Paul, S., Ravichandra, N., Chowdhury, S.: Adaptive radio and transmission power selection for Internet of Things. In 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS) (pp. 1–10). IEEE. (2017), June Fu, H.L., Chen, H.C., Lin, P., Fang, Y.: Energy-efficient reporting mechanisms for multi-type real-time monitoring in machine-to-machine communications networks. In 2012 Proceedings IEEE INFOCOM (pp. 136–144). IEEE. (2012), March Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7530677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515860017,"identity":"7b45e93a-4987-4397-8fa4-7fa076b88221","order_by":0,"name":"Saidou Haman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACxgYJBoYDYNYDmNgBYrUwG0BEmAlpYWCAaWGTgGnBC+TbDzfe+Nh2R163/eyxig8MdYnbpfsPHvjBYBeNS4vBmcRmy5ltzwy3nclLuzmD4XDizjmHGQ72MCTnNuDSwpDYJs3bdphx24Ecs9u8/w4kbriRzHAY6FScWuT7H4K12G87/8as+A/QYQS1MNyA2JK47UaOGdDfzIS1GNx42Gw549zh5G033hhL9jAcNgb6xeBgjwFuv8j3pz+88aHssO228zmGH34w1Mlul258/OFHhR1uh2HaC44dA6LVw7WMglEwCkbBKEAAAKBEZOr3NZfZAAAAAElFTkSuQmCC","orcid":"","institution":"National Advanced School of Posts, Telecommunications and Information and Communication Technologies (SUP’PTIC) of Yaoundé","correspondingAuthor":true,"prefix":"","firstName":"Saidou","middleName":"","lastName":"Haman","suffix":""},{"id":515860018,"identity":"28189b4d-4187-458c-ad9d-f3c678a73053","order_by":1,"name":"Djorwe Temoa","email":"","orcid":"","institution":"The University of Maroua","correspondingAuthor":false,"prefix":"","firstName":"Djorwe","middleName":"","lastName":"Temoa","suffix":""},{"id":515860019,"identity":"69ff0468-6d7e-4868-981d-dfa4981c319e","order_by":2,"name":"Pr Kolyang","email":"","orcid":"","institution":"The University of Maroua","correspondingAuthor":false,"prefix":"","firstName":"Pr","middleName":"","lastName":"Kolyang","suffix":""}],"badges":[],"createdAt":"2025-09-04 00:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7530677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7530677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92110536,"identity":"aa68de7c-a7a8-4c08-a02f-a8315689a001","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2401916,"visible":true,"origin":"","legend":"","description":"","filename":"IoTprotocolsandalgorithmsoptimalchoices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/261113b6552baf820d4b7915.docx"},{"id":92110514,"identity":"7555cfd1-63f8-4906-ba3b-1127ffd43b82","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3599,"visible":true,"origin":"","legend":"","description":"","filename":"6c65e4a082f24a52b94c4168efbf89df.json","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/93e2b05b6e3428ede59cbcdd.json"},{"id":92110515,"identity":"336eedaa-979d-4786-80b6-27a4f4d9a7e4","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93534,"visible":true,"origin":"","legend":"","description":"","filename":"6c65e4a082f24a52b94c4168efbf89df1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/bd9662f89c9be94c95e66e7b.xml"},{"id":92111509,"identity":"2d9701c9-76d6-4014-aa32-2194e3fdf4bf","added_by":"auto","created_at":"2025-09-24 18:31:01","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1694574,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/4a36ab283075b36ee8197d54.png"},{"id":92110513,"identity":"c8d6ad7e-7e02-4279-b81c-2af4e4f1726c","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8068,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/b0dd1aaf2c0e8414b5755038.png"},{"id":92111510,"identity":"e8bc52fc-3fe7-48c3-9050-bfe32358392a","added_by":"auto","created_at":"2025-09-24 18:31:01","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85488,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/9b45f4e2fd8498aff37905fd.png"},{"id":92110750,"identity":"335a57bb-4990-4d44-baab-ef76a4ee710e","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87347,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/f92d8ecadc32533ed5999285.png"},{"id":92110527,"identity":"2028b02e-b244-4731-b07b-3be5cbc0eb2b","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110398,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/683ae03a14a12ae036da70e6.png"},{"id":92110521,"identity":"4ee1af53-21d6-4e2e-8fa8-c8fe3cc2eaf8","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113368,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/4d2d74ad9f15ad9a80580bec.png"},{"id":92110531,"identity":"b40d4737-6888-456b-88bd-8125c304cff2","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":252845,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/ef42cae31c13f2d9a4c40192.png"},{"id":92110753,"identity":"62f823cc-b461-4415-86f9-c87dd004464b","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66955,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/04a956639f6208c7798c3936.png"},{"id":92110752,"identity":"c84618b3-9767-4b5f-8ae2-7897545423a6","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6397,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/7f68bb641be19b89513b981e.png"},{"id":92110755,"identity":"68585c8e-e80a-4b02-aae2-905b0af065d5","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28425,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/da45b82a9f105ee2eff98ace.png"},{"id":92110517,"identity":"11db6caa-3b33-4278-85bc-d1d4904afd6c","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22372,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/46cc47d52b1e565caeadb4be.png"},{"id":92111511,"identity":"d9bd6e36-5188-4778-b13c-1c1b9fb44df6","added_by":"auto","created_at":"2025-09-24 18:31:01","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43632,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/eb4ebec2aec06c337abf1ca7.png"},{"id":92110530,"identity":"e4aab044-5dde-4ad9-89d3-3dba40dddef1","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24355,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/54cd2da7f562faf257edf479.png"},{"id":92110532,"identity":"fc23513f-4ada-4094-a9ba-804171ac3187","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98794,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/aa22d6c6f8533f7546ad90fe.png"},{"id":92110534,"identity":"b906cb4e-f98d-42cf-ae82-75b50905ac5b","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91434,"visible":true,"origin":"","legend":"","description":"","filename":"6c65e4a082f24a52b94c4168efbf89df1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/a3e64f4b0aa9451755b401c1.xml"},{"id":92110535,"identity":"fd126d46-5e46-44e6-8446-9651d935be4d","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101802,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/a3ee26e93710f2ede1255654.html"},{"id":92110510,"identity":"ddb99fff-9a8d-4051-8c78-b770895306ff","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":487977,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of connected objects [01]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/dbe106cf038b695328e79a69.png"},{"id":92110746,"identity":"a39fdb0a-694a-40b8-8f4e-302a9a74e588","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63649,"visible":true,"origin":"","legend":"\u003cp\u003ePublication/subscription process used by MQTT [12].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/2dcb62a46fea9d956e11c772.png"},{"id":92110511,"identity":"73e42348-9023-4e00-ace2-54edc309f538","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125301,"visible":true,"origin":"","legend":"\u003cp\u003eMQTT architecture [14].\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/ae593263c6871bbad8f7d56b.png"},{"id":92110747,"identity":"d6d24733-99ac-4cad-bdac-3d174cf549a9","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142656,"visible":true,"origin":"","legend":"\u003cp\u003eArchitecture of the XMMP protocol [18]\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/d92aef33557232c2539fa240.png"},{"id":92110523,"identity":"86ecfd22-4ef7-4ed7-8021-e6d191768b71","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165613,"visible":true,"origin":"","legend":"\u003cp\u003eUser-centred handover mechanism [29].\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/b3d1aea4e23609de82a3f024.png"},{"id":92110751,"identity":"d038a419-1e5c-4eab-a708-d8b72a1cabaf","added_by":"auto","created_at":"2025-09-24 18:23:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":262590,"visible":true,"origin":"","legend":"\u003cp\u003eOperating mechanism of the SIA algorithm [30].\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/f4fa6d451e7e9a5379d68a55.png"},{"id":92110526,"identity":"4f3afc29-85e0-40f4-8b6e-74e54633fb5a","added_by":"auto","created_at":"2025-09-24 18:15:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":308705,"visible":true,"origin":"","legend":"\u003cp\u003eARTPoS architecture [22]\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/967a0767d8764d8e8c4eec34.png"},{"id":109480117,"identity":"fafc681d-0e2b-4426-bd3a-eff7ce96a381","added_by":"auto","created_at":"2026-05-18 14:56:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1525602,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7530677/v1/2006b111-8197-4f24-aab1-5a8cbf0a2816.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IoT protocols and algorithms: foundations for optimal choices in connected projects","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe internet has evolved in ways we could never have imagined. In the early days, progress was slow. Today, innovation is happening at a rapid pace. Modern life has been transformed by information and communication technologies (ICT), particularly the internet and the services it offers: social networks, e-commerce, e-learning, webinars, to name but a few. In this context, it is worth mentioning the advent of the IoT (Internet of Things). According to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ethe International Telecommunication Union\u003c/span\u003e, the Internet of Things (IoT) is a \"\u003cem\u003eglobal infrastructure for the information society, enabling advanced services by interconnecting objects (\u003c/em\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ephysical\u003c/span\u003e \u003cem\u003eor virtual) through existing or evolving interoperable information and communication technologies\u003c/em\u003e\". In reality, the definition of the latter is not set in stone. From a conceptual point of view, the Internet of Things is defined as a network of intelligent objects of all kinds designed to support humans in their daily activities through their detection, computing and communication capabilities. These are referred to as connected objects. This new technology is based on the combination of embedded devices and intelligent software connected to the internet, offering a multitude of applications and services ranging from transport and supervision to personal well-being. The IoT is growing rapidly, benefiting from the creation of cloud computing and its autonomy. Nowadays, IoT equipment can be found in almost every field, such as home automation, healthcare, elderly care, automotive, industrial automation, traffic management, etc. The evolution of this technology has been remarkably rapid. In 2003, the world population was close to 6\u0026nbsp;billion and there were half a billion devices connected to the Internet. The idea of the Internet of Things emerged in 2009, boosted by the advent of smartphones, and the number of devices connected to the Internet reached 12.5\u0026nbsp;billion in 2010, while the world population was 6.8\u0026nbsp;billion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eToday, the IoT is gaining momentum and, looking to the future, experts estimate that the number of sensors connected to the Internet could increase by several million or even several billion as everything that exists becomes connected (animals, lamps, houses, people, shoes, trees, etc.).\u003c/p\u003e\u003cp\u003eHowever, it should be noted that IoT devices are heterogeneous and highly restrictive. These devices vary in terms of computing power, storage capacity, communication protocols, networking capabilities, battery life, etc. In [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e02\u003c/span\u003e], for example, Andrea Zanella et al conducted a study on an urban IoT system as part of the Padova Smart City project, which involves several IoT applications with different requirements in terms of throughput, battery life, tolerated latency, memory, etc. In fact, there is a variety of IoT devices with multiple interfaces. In a system of connected objects, intermediate devices with multiple interfaces are used, implementing protocols associated with algorithms to enable data exchange between IoT devices from different networks. However, given the multitude of interfaces available to gateways, IoT devices can transmit their flows randomly. This mechanism can cause network congestion, as several IoT devices may decide to transmit their data on a given interface. In addition, the choice of an inappropriate interface by an IoT device can also be costly in terms of autonomy, security, memory resources, etc. To remedy this problem, a control mechanism must be implemented in the gateways. The main objective of this mechanism will be to assign a data flow from an application to an interface that is considered available and optimal for that application. In addition, this assignment should be made intelligently and dynamically rather than statically. This mechanism for the intelligent management and allocation of shared resources in an IoT network is based mainly on implemented protocols and algorithms. In this chapter, we will review the various works carried out in relation to IoT protocols and algorithms used in intermediate devices in order to understand how the problem of object and network heterogeneity is addressed for better data exchange. The work carried out in this field can be classified into two main categories.\u003c/p\u003e"},{"header":"2. State of the art in IoT protocols and algorithms","content":"\u003cp\u003eWe will first discuss Internet of Things (IoT) protocols, then present artificial intelligence (AI)-based algorithms that enable dynamic allocation of shared resources, in order to provide further details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; A. \u0026nbsp;IoT protocols.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned above, the intermediate device of IoT devices (also known as an IoT gateway) enables data exchange\u0026nbsp;between different IoT nodes from heterogeneous networks on the one hand, and connects them to the public network (the Internet) on the other. In order to perform this function, it supports a variety of protocols. Depending on their relevance to the application used, we can classify these into three main categories: communication protocols, application protocols and other influential protocols. However, not all of these protocols need to be grouped together to provide a given IoT application.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCommunication Protocol\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn general, a communication protocol is defined as a set of rules that define how communication between a transmitter and a receiver takes place. In the concept of connected objects, these transmitters and receivers are IoT devices that communicate with the cloud server on the one hand and with each other via the IoT gateway on the other. Thus, a communication protocol enables IoT devices to exchange data in a regulated manner. This exchange can be carried out over a more or less significant port.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eShort-range\u0026nbsp;communication protocols.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe will start with short-range communication protocols. In a short-range network, IoT devices communicate in a closed environment, and these networks allow data to be transferred over short distances.\u003c/p\u003e\n\u003cp\u003eRFID,\u0026nbsp;or Radio Frequency Identification, is a technology that allows data to be memorised, stored or recorded on a medium and retrieved remotely [03]. This technology is often used in tags or badges for identification purposes.\u003c/p\u003e\n\u003cp\u003eNFC (Near Field Communication) is a promising short-range wireless communication technology that facilitates the use of mobile phones by billions of people around the world, offering various services ranging from payment and loyalty applications to access keys for offices and homes [04]. According to [05], NFC technology enables various contactless\u0026nbsp;applications in the fields of transport, health and commerce.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMedium-range communication networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBluetooth Low-Energy (BLE) or Bluetooth Smart is a communication protocol that uses short-range radio with minimal power to operate for a long time compared to conventional Bluetooth. Its coverage range is approximately 100 metres, while its transmission power is between 0.01 mW and 10 mW. With these characteristics, BLE is an ideal candidate for IoT applications.\u003c/p\u003e\n\u003cp\u003eWi-Fi technology is widely used as a standardised wireless communication protocol on computer networks [06]. It is a communication protocol that offers great freedom, mobility and ease of connection and real-time information exchange. Wi-Fi technology uses an unlicensed spectrum in the 2.4 GHz and 5 GHz bands to provide connectivity between IoT devices and a local Internet access point.\u003c/p\u003e\n\u003cp\u003eZigbee is a wireless personal area network (WPAN) that stands out from its two main competitors (Wi-Fi and Bluetooth) due to its simplicity of implementation and low\u0026nbsp;energy consumption. ZigBee is used to create networks requiring low data transfer rates (lower than Bluetooth and Wi-Fi).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eLong-range, low-speed communications networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe have looked at short- and medium-range communication protocols in the previous\u0026nbsp;sections. We will now logically move on to the protocols used for long-range IoT device communication. These protocols meet the need to connect IoT devices to the internet. They use radio waves, which are very effective in rural areas. However, urban areas with tall buildings, trees and houses are more difficult for radio waves to traverse. In this category, we distinguish between two main families. The first family consists of low-speed, long-range networks, also known as LPWAN (Low-Power Wide-Area Network). The second family is that of cellular networks.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eLow-Power Wide-Area Network\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLPWAN stands for Low Power Wide Area Network. Low-power wireless technology (LPWAN) is the answer to the need for a system that connects low-power devices to the internet for\u0026nbsp;low-speed\u0026nbsp;data capture\u0026nbsp;[07].\u003c/p\u003e\n\u003cp\u003eThe Long Range (LoRa) network is becoming one of the most promising low-power wide-area networks (LPWANs) because it enables energy-constrained devices spread over large areas to establish affordable connectivity [08]. However, LoRa is an ideal protocol for IoT devices. It enables long-range transmission and is deployed without a licence, allowing private and public operators to deploy their own networks without monthly subscription fees.\u0026nbsp;It operates on two ISM bands below 1 GHz. In Europe, the first band ranges from 868 MHz to 868.6 MHz,\u0026nbsp;while the second is centred on 433 MHz. As these bandwidths are narrow, data rates are below 37.5 kbit/s (LoRa) and 50 kbit/s (FSK) for transmission [SWH17]. LoRa can also operate on the 2.4 GHz ISM frequency band. The maximum data rate is 200 kbit/s in LoRa and 2 Mbit/s in (G)FSK/MSK. This frequency band would allow higher data rates and eliminate the duty cycle. The duty cycle is a limitation on the use of the 433 MHz and 868 MHz ISM bands.\u003c/p\u003e\n\u003cp\u003eSigfox is a low-energy, long-range communication protocol. It was developed in 2006 by a French company (Sigfox) for the Internet of Things (IoT). It is LoRa\u0026apos;s main competitor. It enables IoT devices to communicate with each other at very low data rates and with very low energy consumption. However, Sigfox is a proprietary network, which requires users to sign up with Sigfox and continue to pay the company to maintain deployment. It is currently available in 72 countries.\u003c/p\u003e\n\u003cp\u003eLoRa and Sigfox are not the only LPWAN technologies. There are others, such as DASH7 and Ingenu. They all have their own specific features. For example, DASH7, which is also an LPWAN protocol, has the particularity of supporting tree-type topologies in addition to star topologies. Ingenu is one of the few LPWAN solutions operating on the 2.4 GHz ISM frequency band. It has a range of up to 15 km and a data rate of up to 8 kbit/s. A comprehensive analysis of this protocol is provided in [09] by \u0026nbsp;Maxime Mroue. In his work, the author identified the obstacles that have been overcome, including packet rejection rates, packet collisions, etc.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCellular networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA cellular network, also known as a mobile phone network, is a network that allows the simultaneous use of a large number\u0026nbsp;of IoT devices, whether mobile (even when travelling at high speed) or static.\u003c/p\u003e\n\u003cp\u003eLTE-MTC is a version of LTE (Long Term Evolution) technology designed for IoT. It is also known as LTE-M or eMTC. In order to increase the \u0026quot;lifespan\u0026quot; of IoT devices, LTE-M operates on a 1.4 MHz frequency band in its version 13. However, it can reach 1 Mbit/s. In its version 14 (LTE-M2), it uses a 5 MHz frequency band. This ultimately gives us speeds of around 3 MHz. LTE-M uses the 900 MHz LTE band [PR16]. Its range is greater than that of conventional LTE. It reaches up to 5 km in urban areas and 17 km in rural areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Narrowband IoT (NB-IoT) communication protocol is also based on the LTE (Long Term Evolution) protocol. NB-IoT minimises the functionality of the LTE protocol and improves it according to the needs of IoT applications [10]. NB-IoT operates with a very narrow frequency band of only 180 kHz. Data rates are below 250 kbit/s [Che+17]. The payload can reach 1600 KB [Mek+18]. NB-IoT uses the 900 MHz LTE band. Its range can be up to 8 km in urban areas and 25 km in rural\u0026nbsp;areas.\u003c/p\u003e\n\u003cp\u003eThe EC-GSM-IoT protocol is an extension of EDGE for IoT. It is also known as EC-GSM. \u0026nbsp;It is defined in version 13 of LTE. EC-GSM operates on a reduced frequency band compared to EDGE frequency bands. As EDGE network infrastructure is already in place, EC-GSM uses EDGE as its basis. However, a few adjustments have been made to optimise it for IoT devices. [11] provides some concepts regarding the contributions of the EC-GSM-IoT protocol.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eApplication protocols\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe advent of the Internet of Things has placed remarkable pressure on the Internet. As a result, new protocols have been implemented, including application protocols\u0026nbsp;such as MQTT, XMPP, DDS and CoAP. Traditional protocols such as HTTP are not suitable due to the significant constraints in terms of energy consumption and computing capacity. In this section, we will present the application protocols specially developed for IoT devices. These are organised into two groups: Publish/Subscribe protocols (MQTT, XMPP) and client/server protocols (COAP, WebSocket).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePublish/Subscribe protocols\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePublish-subscribe is a messaging model in which senders, known as publishers, publish a message (associated with a category) without specifying recipients in advance. On the other side, potential recipients, known as subscribers, subscribe to the categories that interest them and only receive the corresponding messages via a broker called EMQ, without knowing if there are other publishers. Fig. 2 illustrates the publish-subscribe process.\u003c/p\u003e\n\u003cp\u003eThere are several types of protocols that use the publish-subscribe model. However, in this section, we will present MQTT and the XMPP protocol.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe MQTT protocol\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMQTT is a messaging protocol that was introduced by Andy Stanford-Clark of IBM and Arlen Nipper of Arcom (now Eurotech) in 1999 and was standardised in 2013 at OASIS [13]. It is based on a client/server architecture where sensors act as clients and then connect to a server called a broker. It is a lightweight and easy-to-implement protocol. It is widely used in IoT device connectivity and M2M (machine-to-machine communication). It uses the publish-subscribe model to offer transition flexibility and simplicity of implementation, as illustrated in the following figure.\u003c/p\u003e\n\u003cp\u003eThe MQTT protocol consists of three\u0026nbsp;main components: a subscriber, a publisher, and a broker. As shown in the architecture above, publishers publish topics of interest. An interested device registers as a subscriber for specific topics in order to be notified by the broker (EMQ) of topics of interest published by publishers. The latter act as generators of interesting data. Publishers transmit information to interested entities (subscribers) via the broker (EMQ). In addition, the EMQ ensures security by verifying the authorisation of publishers and subscribers. The MQTT protocol is used in several applications such as monitoring and energy metering. It is also capable of providing routing for small, low-power, low-memory devices in vulnerable, low-bandwidth networks. Therefore, the MQTT protocol remains an ideal messaging protocol for IoT devices. However, MQTT is not supported by NDN, so an MQTT gateway to CCN has been proposed to enable interoperability between CCN IoT devices and the current Internet [15]. A detailed study of the MQTT protocol is carried out by Dipa Soni et al in [16].\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe XMMP protocol\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eXMPP is a communication protocol based on XML (Extensible Markup Language). XMPP is used for MOM (Message-Oriented Middleware), which is software designed to send and receive messages between different systems. The XMPP protocol, originally called Jabber, was created in 1999 by the Jabber open-source community. XMPP allows applications to communicate with each other regardless of the operating system they use by sending instant messages over the Internet. It also allows instant messaging applications to obtain authentication, access control, confidentiality measurement, hop-by-hop and end-to-end encryption, and compatibility with other protocols. In [17], Heng Wang et al. proposed a publish/subscribe mechanism based on the XMPP protocol. In their study, the authors focused on resource-constrained IoT devices in order to perform periodic data exchange. The following figure illustrates the architecture of the XMMP protocol.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eClient/server protocols.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe client/server protocol is a model for transactions between multiple programmes or processes via a network. In this model, there are two main players: a client, which sends requests to the server, and a server, which waits for requests from clients, processes them, and respond . There are several types of client/server protocols. In this section, we will present the protocols most commonly used by IoT applications, namely the COAP protocol and the\u0026nbsp;WebSocket\u0026nbsp;protocol.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCOAP protocol\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCoAP (Constrained Application Protocol) is a web transfer protocol designed to run on constrained devices, which are generally IoT devices. It is based on the REST architecture. REST represents a simpler way to exchange data between clients and servers via HTTP [19]. The CoAP protocol is a lightweight version of HTTP, which relies on UDP. This makes it more suitable for IoT applications. It should also be noted that the CoAP protocol modifies certain HTTP features to meet IoT requirements such as low energy consumption. Consequently, Carsten Bormann et al presented the COAP protocol as the protocol par excellence for constrained nodes and networks, including IoT, in [20].\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWebSocket protocol\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUnlike the previously presented protocol (CoAP), the WebSocket protocol is a TCP-based network protocol. It is a protocol implemented in the IoT Hub, which makes it easy to connect a web application to the Hub. When a Hub is created, two networks are also created by default. These are the MQTT and WebSocket networks. MQTT is used for \u0026nbsp;its seamless message exchange between constrained devices (such as IoT devices) and the hub. WebSocket allows MQTT messages to be sent and received directly in a browser, so that web applications can connect directly to the Hub without the need for middleware and APIs.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOther important protocols\u003c/li\u003e\n \u003cli\u003eDNS protocols\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDNS protocols were originally designed for resource-rich devices. However, their enhanced versions have been\u0026nbsp;adapted for IoT applications. The scalability of the IoT requires an efficient system for resource management. This system will enable the registration and discovery of resources and services in a self-configuring, efficient and dynamic manner. Thus, the most dominant protocols in this area are Multicast DNS (mDNS) and DNS Service Discovery (DNS-SD), which can discover the resources and services offered by IoT devices.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSecurity protocols\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNew IoT mechanisms cannot be secured by conventional security protocols. Internet security protocols are designed to work on traditional devices without significant constraints on computing and storage resources, such as computers. Furthermore, the emergence of new protocols and architectures to support the IoT points to new security issues, and this concern should be addressed at all layers of the IoT, from the application to the infrastructure layers, including securing data within resource-constrained devices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Algorithms used for dynamic allocation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eshared resources (IoT gateway).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a system where several IoT devices are connected to each other to exchange data between themselves on the one hand and with the remote server on the other, there are inevitably different communication, application and security protocols, etc. In such an exchange, the connected devices do not necessarily have the same characteristics in terms of communication capacity, memory capacity, energy capacity, etc. This is referred to as IoT device heterogeneity. For optimal exchange at both the device and network levels, intermediate equipment serving as an IoT gateway is deployed.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIoT gateway\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn IoT gateway establishes a bridge between IoT devices, sensors and the cloud to enable data exchange. It systematically connects \u0026quot;the field\u0026quot;, consisting mainly of sensors, to the remote server or cloud. However, the heterogeneous nature of sensors and networks means that gateways must support different types of interfaces (e.g. Power Line Communication (PLC), Bluetooth, cellular or Wi-Fi) and network protocols [21]. Depending on the nature of the sensors or networks, the configuration of an IoT gateway must also change to ensure interconnection. However, the current problem with an IoT gateway is that the configuration enabling interconnection between heterogeneous networks is not performed automatically and intelligently. Furthermore, this gateway, considered a common resource between different devices, is not dynamically and optimally allocated to them. A variety of research on the IoT gateway has been carried out.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn [22], Remy Leone presented an intelligent gateway for constrained wireless sensor networks. He proposed an IoT gateway capable of estimating the energy consumed by the various nodes assigned to it without having to use control messages that are costly in terms of bandwidth and energy consumption. In this approach, the IoT gateway has complete control over the sensors assigned to it. In this work, Remy Leone\u0026apos;s main goal was to add new features to the gateway of an LLN with a view to optimising the use of the LLN\u0026apos;s limited resources and improving knowledge of its operating status. Similarly, the IoT gateway proposed by Remy Leone makes it possible to determine the optimal resource validity times to be used within an application cache.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA solution for automatic quality of service management and security in an Internet of Things ecosystem is presented in [23]. In his work, Ahmad Khalil proposes an IoT gateway integrating two main mechanisms. The first mechanism is based on differentiated treatment of incoming and outgoing traffic in the IoT environment. It allows each type of traffic to be handled according to different QoS parameters. This mechanism also gives the IoT gateway autonomy through two important functions: self-configuration and self-optimisation. The second mechanism allows control over the access of IoT devices to IoT gateways. Like quality of service management, this access control is also provided automatically by the IoT gateways.\u003c/p\u003e\n\u003cp\u003eIn [24], Qian Zhu et al presented an IoT gateway based on Zigbee and GPRS protocols depending on the scenario. Depending on the requirements of telecommunications operators and the type of application used, the IoT gateway presented allows for automatic protocol conversion. The Zigbee protocol is used to connect sensors to the gateway. It is a medium-range network. GPRS is used to connect the IoT gateway to the cloud for processing or storing the data collected by the sensors. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn [25], \u003ca href=\"https://ieeexplore.ieee.org/author/37086738461\"\u003eShang\u0026nbsp;\u003c/a\u003eGuoqiang et al proposed an intelligent IoT gateway that enables end-to-end connectivity between heterogeneous networks (Zigbee, wireless local area network, wired network, etc.). It is a configurable gateway that has three important advantages. First, the gateway has an architecture where communication protocols can be customised and plugged in according to different networks. Second, the gateway has unified external interfaces suitable for flexible software development. Finally, it allows different sensor data to be translated into a uniform format. It should also be noted that the gateway offers greater flexibility and scalability compared to similar research.\u003c/p\u003e\n\u003cp\u003eA reliable, self-configuring IoT gateway is being tested by Byungseok Kang et al in [26]. Traditional systems do not take IoT devices into account. As a result, with the evolution of the IoT, new intermediate devices specially designed to ensure compatibility between the IoT and existing systems will be needed. Furthermore, current IoT gateways operate in semi-automatic mode. In other words, when a new IoT device is to be added to a gateway, the user installs it manually and then also configures the gateway to register the new device. In their work, \u0026nbsp;Byungseok Kang et al. placed particular emphasis on a reliable IoT gateway where, when a new device is to be added, the IoT gateway automatically detects it and registers it itself.\u003c/p\u003e\n\u003cp\u003eIn [27], Apostolos Papageorgiou et al proposed a solution enabling automatic configuration based on an interpretable configuration. This solution is based on algorithms for calculating suggested configurations. Five algorithms are proposed, while an in-depth evaluation reveals which of these algorithms are the most effective and should be used in different operating scenarios in order to achieve a high level of compliance with the operator\u0026apos;s objectives.\u003c/p\u003e\n\u003cp\u003eA careful analysis of the work presented above shows that an IoT gateway fulfils several functions: resource optimisation, protocol translation, security management (encryption, decryption), local data processing, optimal data exchange between heterogeneous devices, adjusting the heterogeneity between these various networks, data transmission to the cloud for in-depth processing and storage, etc. However, these functions are made possible by the algorithms they implement.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAlgorithms implemented in IoT gateways\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIoT devices and the networks they are part of have several different characteristics, as highlighted in the introduction. This heterogeneity creates significant challenges when networking or allocating shared resources. Indeed, IoT gateways implement algorithms for optimal resource allocation. These algorithms have several objectives: optimal management of congestion in the gateway, optimal management of data and communication security, optimal management of application memory resources, etc. Thus, IoT gateways are effective if and only if they implement appropriate algorithms. In this section, we will review related work on algorithms implemented in IoT gateways.\u003c/p\u003e\n\u003cp\u003e. \u0026nbsp;Algorithms for optimal interface allocation\u003c/p\u003e\n\u003cp\u003eIoT devices and wireless networks have multiple characteristics, as mentioned in section A.1. This heterogeneity makes M2M communication very complex. In an IoT gateway with a multitude of interfaces, for example, an application\u0026apos;s data flows must pass through an interface that is considered optimal and available. Selecting the appropriate interface for a given application is crucial. It serves to avoid several undesirable effects in the network, such as congestion, data loss, long queues, poor quality of service, etc. The selection of this interface is ensured by appropriate algorithms. In this section, we will present the work carried out in the literature that has proposed algorithms for selecting interfaces in an IoT gateway for optimal use of resources.\u003c/p\u003e\n\u003cp\u003eIn [28], an effective mechanism for network selection on heterogeneous wireless networks is proposed. In this work, Alaa Awad et al. placed particular emphasis on user preferences regarding the use of heterogeneous wireless networks in order to improve system performance and support reliable connectivity. User preferences can be summarised in three parameters: latency, cost and energy consumption. In this system, the user is at the centre of the gateway\u0026apos;s decision-making processes.\u003c/p\u003e\n\u003cp\u003eAnother network selection mechanism based on user preferences is proposed by Liu et al. in [29]. In this work, the authors presented a user-centric QoE-focused algorithm. The algorithm uses the handover mechanism based on the MEDIA Independent Handover (MIH) framework designed by the IEEE 802.21 group. \u0026nbsp; MIH allows the selection of an appropriate network from among those available and then ensures data transfer. Indeed, the main objective of the proposed algorithm is to select an appropriate network based on user preferences in order to maintain acceptable quality of experience (QoE). When selecting a network, cost and QoE are the most important constraints. The operation of this algorithm is described in Figure 6. In this operation, the QoE-Estimators module first collects network and service parameters. These parameters will be used to predict the quality of experience in terms of MOS (Mean Opinion Score). On the other hand, user preferences are also collected. Once all network and service parameters and user preferences have been collected, the predicted MOS value is sent to a handover decision algorithm. After analysis and decision-making by the latter, a message is transmitted to the MIH Function module. This module is responsible for the handover.\u003c/p\u003e\n\u003cp\u003eIn [30], Vangelis Angelakis et al presented a mechanism called SIA (Service-to-Interface Assignment). As its name suggests, this mechanism aims to assign one or more services to a given interface. In this work, they considered multi-interface IoT equipment to highlight the problem of efficient resource allocation in a network. Given that IoT equipment is required to provide a wide variety of services with different requirements, the mechanism proposed by Vangelis Angelakis et al. uses two different algorithms to optimally solve the interface assignment problem. The first algorithm takes into account the most demanding services and immediately allocates interfaces to them. The second algorithm first calculates the existing resource shares and then allocates them to services by random selection. This mechanism is shown in Figure x, which illustrates a case where two services are requesting interfaces. The first service is represented by the colour yellow, while the second uses the colour orange . The shared resource receiving these requests has three interfaces (i1, i2 and i3). Each interface offers a separate resource (k1 or k2 or k3). Initially, the yellow service requests interface i1, while the orange service requests interface i2. Faced with these requests, the gateway must make a decision. To do this, the yellow service\u0026apos;s request is allocated to interfaces i2 and i3, while the orange service\u0026apos;s request is allocated to i1 and i3. Finally, a simulation result is obtained, providing an overview of the distribution of services across different interfaces for both cases. The figure below illustrates how this algorithm works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEnergy consumption\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;optimisation algorithms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned in the previous sections, IoT devices are very restrictive in terms of computing capacity, memory resources and energy. In order to provide an optimal solution to the latter, several studies have been carried out in the literature. In this section, we will review the work proposing energy consumption optimisation algorithms in IoT networks.\u003c/p\u003e\n\u003cp\u003eIn [31], Di Mu et al proposed an adaptive radio and transmission power selection algorithm that provides several wireless technologies. This system supports several wireless protocols such as WiFi, Zigbee, and Bluetooth. The mechanism consists of selecting the appropriate network for traffic and network conditions while minimising energy consumption. It is known as ARTPoS (Adaptive Radio and Transmission Power Selection system). In its architecture, ARTPoS incorporates a selection module that chooses the most optimal radio interface. This choice is made based on the bit rate and residual capacity of the network. In addition, ARTPoS incorporates a second module whose role is to control the radio interfaces selected by the first module. In this module, energy consumption is calculated for the selected interfaces. The results obtained at the end of these calculations are analysed by the first module for a final decision. At the end of their work, the authors obtained an algorithm that significantly reduces energy consumption while maintaining the desired link reliability. The ARTPoS architecture is shown in the figure below.\u003c/p\u003e\n\u003cp\u003eIn [32], Huai-Lei Fu et al developed a control mechanism for real-time, low-energy M2M communication. In a surveillance application, for example, sensors transmit detected data to an IoT gateway. This transmission must be done in real time. In order to maintain this exchange over a long period of time, IoT nodes and gateways must first and foremost have sufficient autonomy.\u003c/p\u003e"},{"header":"3. The problem","content":"\u003cp\u003eDespite the diversity of protocols and algorithms available, there is no universal solution suitable for all IoT scenarios. As Verma, Singh \u0026amp; Prasad (2022) pointed out in their review of IoT protocols applied to connected health, each application imposes specific constraints in terms of security, latency and energy efficiency. The central issue therefore remains: \u003cem\u003ehow can we design generic, adaptive and secure protocols and algorithms capable of meeting the varied needs of heterogeneous IoT environments, while minimising complexity and energy consumption?\u003c/em\u003e\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eIt consists of conducting an in-depth study of the various protocols and algorithms, referring to the following elements such as frequency, range and handover, as mentioned above, before producing two (02) summary tables so that everyone knows where to go. We will therefore produce two summary tables, which we will present to you as results.\u003c/p\u003e"},{"header":"5. Results","content":"\u003cp\u003eOur manuscript consists of two main parts: Part A, devoted to protocols, and Part B, devoted to algorithms. The results we present are the outcome of an in-depth study of these two areas, with the aim of providing researchers with additional details and insights.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIn Part A of our article, we reviewed studies conducted on IoT protocols. Although IoT devices are very demanding in terms of computing power, memory capacity, energy capacity and, above all, networking capacity, there is a wide range of protocols designed to meet these requirements. In fact, no one protocol is better than another. The choice of protocol depends on the type of application to be implemented. In a traffic control application, for example, since the equipment is battery-powered, energy consumption must be as low as possible to enable data to be sent quickly to drivers. This data is used by drivers to make better decisions in the event of an accident on a road, for example. In this scenario, it is useful to use a protocol characterised by low energy consumption, such as the Zigbee or LoRa protocol. On the other hand, in an agricultural application designed to send images of plant leaves to a remote server for in-depth analysis, for example, a protocol offering high bandwidth is ideal. In this application, sending images of plant leaves requires much more bandwidth than sending a simple humidity reading, for example. For this reason, it would be preferable to use a protocol such as Wi-Fi rather than Zigbee or LoRa. The table below presents a comparative study of the different IoT protocols.\u0026nbsp;Many protocols have been discussed, but we have selected and indexed those that are most commonly used.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 1: Comparative table of protocols\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eWiFi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBluetooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLR-WPAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLoRa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eIEEE 802.11 a/c/b/d/g/n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eIEEE 802.15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eIEEE 802.15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLoRaWAN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eFrequency Band\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5 GHz \u0026ndash; 60 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2.4 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e868/915 MHz / 2.4 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e868/900 MHz\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eData Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1 Mb/s \u0026ndash; 1 Gb/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1 \u0026ndash; 24 Mb/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e40 \u0026ndash; 250 Kb/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e0.3 \u0026ndash; 50 Kb/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e20 \u0026ndash; 100 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e8 \u0026ndash; 10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e10 \u0026ndash; 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt; 30 km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eEnergy Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMedium (Bluetooth): Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n \u003cli\u003eIn Part B of our article, we have reviewed some of the work proposing algorithms for optimal interface allocation in an IoT network, as well as those enabling optimal energy consumption. The following table contains a comparative study of this work and its limitations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 2: Comparative table of algorithms\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eContributions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eLimitations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eA. A. Abdellatif, C.-F. Chiasserini \u0026amp; A. Mohamed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Considers user preferences and network characteristics\u003cbr\u003e\u0026nbsp;\u0026bull; Optimizes IoT device performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; No exact formulation of delays (transfer, processing, handover)\u003cbr\u003e\u0026nbsp;\u0026bull; No automatic handover to optimal interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eLiu, Sun L. \u0026amp; Ifeachor E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Considers Quality of Experience (QoE)\u003cbr\u003e\u0026nbsp;\u0026bull; Handover based on MEDIA Independent Handover framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Long/complex procedure \u0026rarr; high energy consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eV. Angelakis, I. Avgouleas, N. Pappas, E. Fitzgerald \u0026amp; D. Yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Uses two algorithms simultaneously for better optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; High memory cost\u003cbr\u003e\u0026nbsp;\u0026bull; No consideration of user preferences\u003cbr\u003e\u0026nbsp;\u0026bull; No real-world deployment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eDi Mu, Y. Ge, M. Sha, S. Paul, N. Ravichandra \u0026amp; S. Chowdhury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Provides solutions for mobile apps via multiple wireless technologies\u003cbr\u003e\u0026nbsp;\u0026bull; Selects network according to traffic \u0026amp; conditions while minimizing energy consumption\u003cbr\u003e\u0026nbsp;\u0026bull; Considers data loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026bull; Does not consider deadlines of received flows\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eWe have presented the various protocols and algorithms in detail, highlighting numerous criteria such as frequency, bandwidth, range and areas of application. Our aim is to highlight these solutions used in connected objects, through two tables drawn up following an in-depth study. However, it should be noted that their implementation still faces access problems, particularly related to network heterogeneity, latency constraints, congestion, and security and reliability issues.\u003c/p\u003e\u003cp\u003eThese limitations become particularly critical in sensitive contexts such as war situations, where connectivity is unstable and infrastructure is often damaged, but where connected objects can play a crucial role in locating victims, providing medical monitoring or coordinating relief efforts. Similarly, during natural disasters such as floods or earthquakes, the IoT can help save lives by ensuring early risk detection, rapid transmission of alerts and real-time monitoring of disaster areas, despite access and deployment constraints.\u003c/p\u003e\u003cp\u003eWe believe that after reading our article, you will be able to make informed choices for your IoT-related projects. Furthermore, in the era of artificial intelligence, which is currently at the forefront of the news, these protocols and algorithms can be enriched and improved through intelligent integration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSaidou HAMAN: ResearcherDjorwe Temoa: Support Kolyang : Mentor\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTafazolli, R. (ed.): Technologies for the Wireless Future, Volume 2: Wireless World Research Forum (WWRF), vol. 2. Wiley (2006)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCenedese, A., Zanella, A., Vangelista, L., Zorzi, M.: Padova smart city: An urban internet of things experimentation. In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 (pp. 1\u0026ndash;6). IEEE. (2014), June\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJDN: RFID: d\u0026eacute;finition, fonctionnement, lien avec le NFC. [En ligne] url \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.journaldunet.fr/web-tech/dictionnaire-de-l-iot/1440692-rfid-definition-fonctionnement-lien-avec-le-nfc/\u003c/span\u003e\u003cspan address=\"https://www.journaldunet.fr/web-tech/dictionnaire-de-l-iot/1440692-rfid-definition-fonctionnement-lien-avec-le-nfc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (visit\u0026eacute; le visit\u0026eacute; le 30/08/2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoskun, V., Ozdenizci, B., Ok, K.: A survey on near field communication (NFC) technology. Wireless Pers. Commun. \u003cb\u003e71\u003c/b\u003e(3), 2259\u0026ndash;2294 (2013)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYacine, Z.E.N.A.G.: La technologie NFC - Principes de fonctionnement et applications. [En ligne] url: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.techniques-ingenieur.fr/base-documentaire/technologies-de-l-information-th9/internet-des-objets-42612210/la-technologie-nfc-s8650/\u003c/span\u003e\u003cspan address=\"https://www.techniques-ingenieur.fr/base-documentaire/technologies-de-l-information-th9/internet-des-objets-42612210/la-technologie-nfc-s8650/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (visit\u0026eacute; le visit\u0026eacute; le 30/08/2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHousbane, S., Serhier, Z., Othmani, M.B.: INTEGRATION DU WIFI DANS LE DOMAINE DE LA SANTE. Revue Marocaine de Sant\u0026eacute; Publique, \u003cb\u003e1\u003c/b\u003e(1). (2014)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePierre Kassaa: L\u0026rsquo;internet des Objets (IoT) et les r\u0026eacute;seaux haut et bas d\u0026eacute;bit, est-ce un outil essentiel pour une ville intelligente et moins Pollu\u0026eacute;e? [En ligne] url: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scribd.com/document/510428644/Kassaa-Pierre\u003c/span\u003e\u003cspan address=\"https://www.scribd.com/document/510428644/Kassaa-Pierre\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (visit\u0026eacute; le visit\u0026eacute; le 30/008/2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, Q., Zheng, K., Hou, L., Xing, J., Xu, R.: Design and implementation of open LoRa for IoT. Ieee Access. \u003cb\u003e7\u003c/b\u003e, 100649\u0026ndash;100657 (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMroue, M.: D\u0026eacute;veloppement de r\u0026eacute;seaux radio d'objets connect\u0026eacute;s pour les Villes Intelligentes: am\u0026eacute;lioration de la Qualit\u0026eacute; de Service du r\u0026eacute;seau LoRa (Doctoral dissertation, UNIVERSITE DE NANTES). (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMekki, K., Bajic, E., Chaxel, F., Meyer, F.: A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express. \u003cb\u003e5\u003c/b\u003e(1), 1\u0026ndash;7 (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinnegan, J., Brown, S.: A comparative survey of LPWA networking. arXiv preprint arXiv:1802.04222. (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArdagna, C.A., Damiani, E., Sch\u0026uuml;tte, J., Stephanow, P.: A case for IoT security assurance. In: Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, pp. 175\u0026ndash;192. Springer Singapore, Singapore (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobo, H.I., Abu-Mahfouz, A.M., Hancke, G.P.: A survey on software-defined wireless sensor networks: Challenges and design requirements. IEEE access. \u003cb\u003e5\u003c/b\u003e, 1872\u0026ndash;1899 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Masri, E., Kalyanam, K.R., Batts, J., Kim, J., Singh, S., Vo, T., Yan, C.: Investigating messaging protocols for the Internet of Things (IoT). IEEE Access. \u003cb\u003e8\u003c/b\u003e, 94880\u0026ndash;94911 (2020)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaadaoui, F.Z., Maizate, A., Ouzzif, M.: \u0026Eacute;tat d'art sur les protocoles en temps r\u0026eacute;el pour l'internet des objets sous le r\u0026eacute;seau NDN. In Colloque sur les Objets et syst\u0026egrave;mes Connect\u0026eacute;s. (2019), June\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoni, D., Makwana, A.: A survey on mqtt: a protocol of internet of things (iot). In International conference on telecommunication, power analysis and computing techniques (ICTPACT-2017) (Vol. 20, No. April, 2017). (2017), April\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H., Xiong, D., Wang, P., Liu, Y.: A lightweight XMPP publish/subscribe scheme for resource-constrained IoT devices. IEEE Access. \u003cb\u003e5\u003c/b\u003e, 16393\u0026ndash;16405 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElement14: The XMPP Protocol Benefits, Architecture and Applications. [En ligne] url: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://community.element14.com/learn/learning-center/the-tech-connection/w/documents/4149/the-xmpp-protocol-benefits-architecture-and-applications\u003c/span\u003e\u003cspan address=\"https://community.element14.com/learn/learning-center/the-tech-connection/w/documents/4149/the-xmpp-protocol-benefits-architecture-and-applications\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (visit\u0026eacute; le 30/08/2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, M., Li, H., Li, Y., Fang, L., Chen, P.: Non-orthogonal multi-carrier technology for space-based Internet of Things applications. In International Conference on Communications and Networking in China (pp. 37\u0026ndash;45). Cham: Springer International Publishing. (2017), October\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBormann, C., Castellani, A.P., Shelby, Z.: Coap: An application protocol for billions of tiny internet nodes. IEEE Internet Comput. \u003cb\u003e16\u003c/b\u003e(2), 62\u0026ndash;67 (2012)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeone, R.: Passerelle intelligente pour r\u0026eacute;seaux de capteurs sans fil contraints (Doctoral dissertation, T\u0026eacute;l\u0026eacute;com ParisTech). (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeone, R.: Passerelle intelligente pour r\u0026eacute;seaux de capteurs sans fil contraints (Doctoral dissertation, T\u0026eacute;l\u0026eacute;com ParisTech). (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhalil, A.: Gestion autonome de la qualit\u0026eacute; de service et de la s\u0026eacute;curit\u0026eacute; dans un environnement Internet des objets (Doctoral dissertation, Universit\u0026eacute; Bourgogne Franche-Comt\u0026eacute;). (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu, Q., Wang, R., Chen, Q., Liu, Y., Qin, W.: Iot gateway: Bridgingwireless sensor networks into internet of things. In 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (pp. 347\u0026ndash;352). Ieee. (2010), December\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuoqiang, S., Yanming, C., Chao, Z., Yanxu, Z.: Design and implementation of a smart IoT gateway. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing (pp. 720\u0026ndash;723). IEEE. (2013), August\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang, B., Choo, H.: An experimental study of a reliable IoT gateway. Ict Express. \u003cb\u003e4\u003c/b\u003e(3), 130\u0026ndash;133 (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapageorgiou, A., Zahn, M., Kovacs, E.: Auto-configuration system and algorithms for big data-enabled internet-of-things platforms. In: 2014 IEEE International Congress on Big Data, pp. 490\u0026ndash;497. IEEE (2014, June)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdellatif, A., Mohamed, A., Chiasserini, C.F.: User-Centric Network Selection in Multi-RAT Systems. In IEEE WCNC 2016 Workshop on International Workshop on Mobile Edge Computing and IoT. IEEE. (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, L., Sun, L., Ifeachor, E.: User-centric QoE-driven vertical handover framework in heterogeneous wireless networks. In 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1\u0026ndash;8). IEEE. (2016), October\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngelakis, V., Avgouleas, I., Pappas, N., Fitzgerald, E., Yuan, D.: Allocation of heterogeneous resources of an IoT device to flexible services. IEEE Internet Things J. \u003cb\u003e3\u003c/b\u003e(5), 691\u0026ndash;700 (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMu, D., Ge, Y., Sha, M., Paul, S., Ravichandra, N., Chowdhury, S.: Adaptive radio and transmission power selection for Internet of Things. In 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS) (pp. 1\u0026ndash;10). IEEE. (2017), June\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu, H.L., Chen, H.C., Lin, P., Fang, Y.: Energy-efficient reporting mechanisms for multi-type real-time monitoring in machine-to-machine communications networks. In 2012 Proceedings IEEE INFOCOM (pp. 136\u0026ndash;144). IEEE. (2012), March\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Things (IoT), Communication protocols, Optimisation algorithms, IoT gateways, Protocols","lastPublishedDoi":"10.21203/rs.3.rs-7530677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7530677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Internet of Things (IoT) is growing rapidly and transforming many areas such as healthcare, home automation, automotive and industry. This expansion is based on the interconnection of heterogeneous devices with limited capabilities (energy, memory, computing) via adapted protocols and algorithms. This paper highlights the state of the art in IoT protocols and algorithms used in IoT gateways, in order to better understand the challenges associated with network heterogeneity and varied application requirements.\u003c/p\u003e\n\u003cp\u003eIoT protocols can be classified into three main families: (i) \u003cstrong\u003ecommunication protocols \u003c/strong\u003e(RFID, NFC, BLE, Wi-Fi, ZigBee, LoRa, Sigfox, NB-IoT, LTE-M, EC-GSM, etc.) according to their range and energy consumption; (ii) \u003cstrong\u003eapplication protocols \u003c/strong\u003e(MQTT, XMPP, CoAP, WebSocket) developed specifically for constrained environments; and (iii) \u003cstrong\u003eother essential protocols, \u003c/strong\u003eincluding security protocols (TLS, DTLS) and service discovery protocols (mDNS, DNS-SD). The choice of protocol depends heavily on the type of application, the required throughput and energy constraints.\u003c/p\u003e\n\u003cp\u003eOn the algorithmic side, research focuses mainly on \u003cstrong\u003eoptimal resource allocation \u003c/strong\u003eand \u003cstrong\u003eenergy optimisation \u003c/strong\u003ein IoT gateways. Various studies have proposed:\u003c/p\u003e\n\u003cp\u003e· user-centric network selection algorithms (Abdellatif et al., Liu et al.),\u003c/p\u003e\n\u003cp\u003e· mechanisms for assigning services to interfaces (Angelakis et al.),\u003c/p\u003e\n\u003cp\u003e· and energy optimisation solutions with adaptive selection of wireless technologies (Di Mu et al.).\u003c/p\u003e\n\u003cp\u003eThe aim of our article is to provide an in-depth overview of the state of the art in protocols and algorithms used in connected objects, using summary tables. The growing integration of the IoT into our daily lives requires a better understanding of these technologies in order to use them effectively and improve them in line with real needs.\u003c/p\u003e","manuscriptTitle":"IoT protocols and algorithms: foundations for optimal choices in connected projects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 18:14:56","doi":"10.21203/rs.3.rs-7530677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c3ddec9-1740-45ec-8782-92875998acb0","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T14:43:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T05:07:58+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"10465420230491475173543028127229272364","date":"2026-05-11T08:40:54+00:00","index":64,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:55:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 18:14:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7530677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7530677","identity":"rs-7530677","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}