ROUTING PROTOCOL RPL MOBILITY MODELS - SURVEY PAPER

ROUTING PROTOCOL RPL MOBILITY MODELS - SURVEY PAPER | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 January 2025 V1 Latest version Share on ROUTING PROTOCOL RPL MOBILITY MODELS - SURVEY PAPER Authors : Roopa Hubballi 0000-0003-4213-080X [email protected] , Harish Kenchannavar , Neeta Deshpande , and Jayanna Hallur Authors Info & Affiliations https://doi.org/10.22541/au.173610736.60994096/v1 801 views 250 downloads Contents Abstract Table of Contents Abstract Index Terms 1. Introduction 2. Research Methodology 3. Mobility Models 3.2.2 Random Walk Model (RWM) 3.2.3 Random Direction Mobility Model (RDM) 3.2.4 Self-similar Least Action Walk Model (SLAW) Markovian Random Walk (MRW) 3.2.6 Gauss-Markov Mobility Model (GMM) 3.2.7 Manhattan Mobility Model (MMM) 3.2.8 Reference Point Group Mobility Model (RPGM) 4. Evaluation of the Comparison 4.2 Experimental Results and Comparisons 5. Conclusion and Future Direction References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the field of mobile IoT systems, particularly the adaptability and performance of mobility models play a critical role in optimizing RPL, an essential aspect in acquiring reliable and efficient communication in resource-constrained and dynamic environments. RPL, developed for certain static sensor networks, suffered various challenges in mobile landscapes where node mobility affects energy efficiency, reliability, and network performance. Therefore, this survey seeks to compare and review several mobility models, including the RPGM, MMM, GMM, MRW, RDM, RWM, and RWP, to address such challenges. The research facilitates a comprehensive examination of the applicability, disadvantages, advantages, and characteristics of all mobility models in promoting the role of RPL-focused mobile IoT systems. The study focuses on the effects of node mobility on routing stability, network throughput, and energy consumption, identifying key factors influencing protocol adaptation in dynamic environments. Experimental methodologies are employed to assess RPL's performance under these models, and the results are critically compared to highlight the strengths and limitations of different mobility models in supporting mobile IoT networks. The findings underscore the importance of selecting appropriate mobility models for optimizing RPL performance, particularly in scenarios involving mobile nodes such as vehicular networks, smart cities, and emergency response systems. The paper concludes by suggesting future research directions, including protocol enhancements, hybrid mobility models, and the integration of artificial intelligence for more adaptive routing solutions in mobile IoT environments. ROUTING PROTOCOL RPL MOBILITY MODELS - SURVEY PAPER Mobility in RPL Networks: Models and Impacts Author 1 Roopa Hubballi Gogte Institute of Technology,Belagavi Corresponding author : [email protected] Author 2 Dr Harish Kenchannavar Gogte Institute of Technology,Belagavi Author 3 Neeta Deshpande RH Sapat College of Engineering and Management,Nashik Author 4 Jayanna Hallur Capital One USA Table of Contents Abstract 3 Index Terms 3 1. Introduction 4 2. Research Methodology 6 2.1 Background on the RPL 6 2.2 RPL Mobility Models and its Protocols 7 3. Mobility Models 9 3.1 Related Studies 9 3.2 Details of the Models and Comparison 11 3.2.1 Random Way Point Mobility Model (RWP) 11 3.2.2 Random Walk Model (RWM) 11 3.2.3 Random Direction Mobility Model (RDM) 12 3.2.4 Self-similar Least Action Walk Model (SLAW) 12 3.2.5 Markovian Random Walk (MRW) 13 3.2.6 Gauss-Markov Mobility Model (GMM) 14 3.2.7 Manhattan Mobility Model (MMM) 15 3.2.8 Reference Point Group Mobility Model (RPGM) 16 4. Evaluation of the Comparison 17 4.1 Experimental Methodologies 17 4.2 Experimental Results and Comparisons 19 5. Conclusion and Future Direction 21 References 24 Abstract In the field of mobile IoT systems, particularly the adaptability and performance of mobility models play a critical role in optimizing RPL, an essential aspect in acquiring reliable and efficient communication in resource-constrained and dynamic environments. RPL, developed for certain static sensor networks, suffered various challenges in mobile landscapes where node mobility affects energy efficiency, reliability, and network performance. Therefore, this survey seeks to compare and review several mobility models, including the RPGM, MMM, GMM, MRW, RDM, RWM, and RWP, to address such challenges. The research facilitates a comprehensive examination of the applicability, disadvantages, advantages, and characteristics of all mobility models in promoting the role of RPL-focused mobile IoT systems. The study focuses on the effects of node mobility on routing stability, network throughput, and energy consumption, identifying key factors influencing protocol adaptation in dynamic environments. Experimental methodologies are employed to assess RPL’s performance under these models, and the results are critically compared to highlight the strengths and limitations of different mobility models in supporting mobile IoT networks. The findings underscore the importance of selecting appropriate mobility models for optimizing RPL performance, particularly in scenarios involving mobile nodes such as vehicular networks, smart cities, and emergency response systems. The paper concludes by suggesting future research directions, including protocol enhancements, hybrid mobility models, and the integration of artificial intelligence for more adaptive routing solutions in mobile IoT environments. Index Terms RPL, IoT, Mobility Models, Routing, Performance 1. Introduction The IoT (“Internet of Things”) stands as a transformative system or technology, integrating multiple devices to provide data sharing and collection in real-time or practical landscape. The demand for reliable and efficient communication processes becomes essential [1]. Routing protocol is a major determinant in such networks, which confirms that data seek to be conducted effectively from one particular device to another. However, among several protocols developed for LLNs (“low-power and lossy networks”), the IPv6 RPL (“Routing Protocol for Low-power and Lossy Networks”) has acquired substantial attention. Therefore, RPL is personalized for IoT landscapes and is popular for its competency and flexibility to leverage a broader range of implementations, from certain smart cities to particular industrial automation. Hence, in the mobile IoT scenario, the application of RPL seems to be an obstacle to the dynamic characteristics of network topology transformations that occur by node mobility [2]. Moreover, this paper seeks to facilitate a detailed evaluation of the RPL routing protocol in the mobility models context, investigating its potential improvements, challenges, and current state. In LLNs, routing, especially in IoT networks, introduces diverse challenges for dynamic characteristics of wireless communication, limited energy, and constrained devices. RPL was presented to cover such challenges by allowing effective routing in LLNs, generally featured by semi-static or static networks [3]. Hence, the RPL’s traditional assumption about static topologies is not valid as IoT implementations extend to contain several mobile devices, including drones, autonomous vehicles, and wearable sensors. In IoT networks, mobility represents a new list of challenges as mobile nodes constantly transform their place, resulting in enhanced network overhead, packet losses, and frequent route breaks. Several mobility models are offered to manage and simulate the nodes’ movement in such networks to address such issues. Therefore, under diverse mobility models, assessing how RPL performs is essential to enhancing its functionality for the applications of mobile IoT [4]. The IoT is transforming how devices communicate and interact, allowing smart solutions throughout several segments, including industry, transportation, agriculture, and healthcare. The IoT networks generally consist of a wide variety of low-power devices or systems with restricted computational assets, frequently practicing in challenging landscapes featured by energy constraints and unreliable wireless links. Therefore, such networks, indicated as LLNs, need particular routing protocols to confirm effective data broadcast instead of their inherent restrictions [5]. “Routing Protocol for Low-power and Lossy Networks” (RPL) was developed by the “Internet Engineering Task Force” (IETF) as a standard level to maintain the exceptional requirements of LLNs. Therefore, this process seems an effective routine protocol for its competency to generate optimized routing approaches in a challenging landscape [6]. RPL practices dependent on distance-vector systems and constructs DAGs (“Directed Acyclic Graphs”) to route such data between diverse nodes. The way the DAG is considered as a wider router directs the procedure of routing, and nodes tend to implement objective functions to determine their desired parent nodes dependent on metrics such as hop count, energy consumption, or link quality. Therefore, RPL also leverages several illustrations, which enables diverse network segments or applications to practice independently, all with its objective functions and DAG [7]. The adaptability of RPL to a diverse network landscape and its competency to control different routing metrics is a major strength of RPL. Hence, RPL was initially developed with semi-static or static topologies, where nodes lead to transform rarely. The protocol experiences issues or challenges in controlling reliable routes for random topology transformations and the continuous route remeasurement expense when node mobility is presented [8]. In IoT networks, mobility seems a highly significant determinant, regulated by the increase of several mobile applications including drone-based systems, wearable devices, and connected vehicles. In such mobile implementations, nodes lead to transform their positions, resulting in active network topologies constantly. However, these continuous movements contain significant obstacles for several routing protocols, such as RPL, which are dependent on comparatively stable topologies to control optimal approaches [9]. Controlling reliable interaction becomes challenging as the random transformations in node positions under the mobile IoT networks result in packet losses, delays in data spread, and route breakages. RPL is considered a traditional routing protocol that is not regulated to associate with such obstacles effectively, providing a comprehensive assessment of mobility models and their association with RPL to confirm network performance [10]. Researchers have identified the challenges of RPL in the context of mobile landscapes over time and offered diverse improvements to address such concerns. However, changes to RPL frequently concentrate on developing its competency to incorporate random topology transformations and reducing the overhead caused by diverse route recalculations [11]. Therefore, the mechanisms of localized route recovery enable nodes to restore routes locally while an association breaks for mobility, decreasing the requirement for worldwide route recalculations. Hence, RPL tends to be changed to contain mobility-aware metrics, including movement patterns or node speed, to choose more stable paths and measure possible route disruptions. At the same time, proactive routing adjustments enable nodes to direct the topology transformations dependent on mobility models tend to develop the resilience of RPL in the mobile landscape [12]. This study aims to systematically evaluate, review, and compare the performance of different mobility models used in RPL in the context of the mobile IoT landscape. The research questions involve: How do diverse mobility models, including RWP, Random Walk Model (RWM), Random Direction Mobility Model (RDM), Markovian Random Walk (MRW), Gauss-Markov Mobility Model (GMM), Manhattan Mobility Model (MMM), Self-similar Least Action Walk Model (SLAW), Reference Point Group Mobility Model (RPGM) and SLAW models, impact the RPL’s performance? 2. Research Methodology 2.1 Background on the RPL RPL was developed particularly for the field of LLNs, which are featured by devices with high rates of data loss, low bandwidth, and limited power. The popularity of this process in IoT networks derives from its flexibility, where it constructs DODAGs (“destination-oriented Directed Acyclic Graphs”) for routing, permitting it to strengthen communication approaches dependent on diverse objective functions, including link reliability, hop count, and energy efficiency [13]. On the contrary, the design of RPL was primarily dependent on the observation that nodes in the context of LLNs seek to possess minimal mobility, conducting it misappropriated for active, mobile IoT scenarios where random topology transformation rises. In IoT networks, mobility is considered more random as devices, including mobile healthcare units, wearable technologies, vehicles, and drones, were highly deployed. Research revealed that as node mobility enhances, the probability of packet losses and broken routes also occurs significantly. Therefore, traditional protocols on LLN routing, such as RPL, struggle to control the route balance for constant transformations in the network topology caused by diverse node mobility [14]. Frequent topology changes seem to be a major challenge in RPL networks regarding mobility. The topology of the network in the mobile IoT landscapes continuously changes, making it complex to control optimal routes for RPL. At the same time, node mobility occurs in random link failures, which affects, in particular, RPL needing to start route remeasurements, resulting in enhanced overhead. On the contrary, the mobility-induced process of rerouting enhances energy consumption as nodes randomly possess to reveal emerging approaches, affecting the device’s battery life [15]. 2.2 RPL Mobility Models and its Protocols Mobility models possess an essential role in encouraging node movements in the context of IoT environments. Diverse mobility models were offered and implemented to research the mobility’s effect on RPL, all with different observations regarding how nodes communicate and move [16]. The RWP (“Random Waypoint”) model is acknowledged as a highly implemented mobility framework in IoT studies. Nodes in this particular model move frequently to a targeted area with changing speeds, stop for a specific duration, and then transform again. Therefore, this model encourages highly active networks and is not responsible for any structured or predictable movement approaches [17]. FIGURE 1: The Assembly of RPL Protocols [1] The model of Gauss-Markov mobility (GMM) seems to be a semi-random framework that adopted both past velocities and random movement to encourage more realistic patterns of movement. This particular model was implemented in a study concentrating on the performance of RPL in real-world applications of IoT, including connected autonomous vehicles and healthcare devices [2]. On the contrary, the model of Manhattan Mobility (MMM) was randomly applied in an urban landscape where nodes transform in a grid-like approach. However, this framework is specifically relevant to vehicular networks and smart cities. The literature revealed that the performance of RPL was promoted slightly under this particular framework, as the organized movement permitted fewer route remeasurements and better route planning [1]. On the contrary, “reference point group mobility models” (RPGM) influenced scenarios or landscapes where diverse nodes transform together in combined approaches. Such models were frequently applied in teams of robots, drone swarms, or vehicular networks. Thus, research revealed that RPL acts effectively in the landscape of group mobility instead of the RWP models, as the measurable movement permitted for more constant routing [4, 16]. Nodes in the random walk model (RWM) lead to move in random orders for a random period and then transform the direction. Therefore, such transformation or movement results in a volatile node distribution. This RWM represents random topology changes occurring in highly balanced messages as RPL needs to advance the routes continuously. Thus, this affects enhanced packet delays and loss, as nodes frequently transform unpredictably. Moreover, the energy efficiency of RPL tends to degrade for often exchanges of control messages [18]. Apart from that, nodes in the model of random direction mobility (RDM) lead to a shift in a determined random direction or order to the time of addressing the limitation of simulation portions, where they chose an emerging speed and direction. Moreover, this particular model ignores the clustering of nodes. RDM assists in maintaining effective node distribution throughout the network, which leads to decreased congestion. Thus, random boundary movements like RWM occur topology changes or transformations, which lead to impact the stability of RPL negatively and result in route reconstructing, enhancing energy consumption and latency [1]. The model of “Markovian random walk” (MRW) seeks to implement a probability-based method where nodes tend to pursue a stochastic procedure regulated by a Markov chain. However, the further node position in this MRW model relies on its transition probabilities and current position. MRW facilitates certain predictability compared to RWP, permitting RPL to act effectively in the context of route maintenance. Hence, the random nature of this model still conducts substantial fluctuations and variations in connectivity, resulting in moderate packet losses and occasional delays [2]. The Manhattan Mobility Model (MMM) encourages movement or program in an urban grid-based model where nodes shift along predefined ways (for example, streets) with 90 o movements at intersections. The structured movement of MMM decreases unpredictability, resulting in more consistent routes in RPL. This model tends to reduce energy consumption and lower packet loss, as certain route remeasurements are required. Hence, node density tends to differ randomly based on the movement area (for example, intersections), which leads to conduct bottlenecks [19]. On the contrary, the model of self-similar least action walk (SLAW) obtains the patterns of human mobility with long pauses and clustering behavior, imitating how people transform in the real-world landscape (for example, between hotspots or buildings). The nodes’ clustering of the SLAW model leads to the conduct of localized high-density portions, which occur to enhanced congestion in particular network regions. Therefore, the performance of RPL tends to suffer from high packet delays and collisions in such hotspot areas. At the same time, the mobility seems more consistent and predictable in such regions’ external areas [20]. The model of reference point group mobility (RPGM) stands as a group-focused movement, where several nodes tend to transform as a particular group’s portion, pursuing a group leader. However, individual nodes lead to a slight turn from the path of leaders but exist within a specific range. Therefore, this model facilitates higher consistency as nodes remain close to others within a group, resulting in longer-lasting and stable routes in the context of RPL. Thus, this model decreases route discovery, declines packet loss, and develops energy efficiency. Hence, network partitioning tends to occur if group mobility results in such groups shifting remotely to each other, resulting in degraded RPL performance and isolated clusters [6]. 3. Mobility Models 3.1 Related Studies In the wireless networks’ context, RWP is considered a popularly researched mobility model. Nodes tend to determine random destinations and, with random speed, transform towards this RWP model. This model pauses to choose another destination and random duration after reaching such a destination. [21] evaluated the impact of RWP on IoT networks, and the author determined that the random transformations in node places significantly enhanced the control message of RPL, resulting in performance degradation in the context of dense networks. Nodes in RWM ascertain a direction and transform for a fixed duration; furthermore, they frequently transform the speed and direction. Compared to RWP, this RWM model produces frequent and unpredictable topology transformations. [22] differentiated RWM into particular RWP based on its impact on routing in the context of mobile sensor networks. The researcher obtained that RWM resulted in an extreme packet loss of its unpredictable approaches, which conducted route maintenance for certain protocols such as RPL more stimulating. As discussed previously, RDM forces several nodes to transform in a frequent direction until they address the boundary, resulting in unchanging spatial distribution. [1] analyzed the RDM’s impact on RPL in a diverse IoT landscape. However, their outcomes recommend that the performance of RPL in terms of control overhead and packet delivery ratio seems lower under the model of RDM for random topology changes. On the contrary, the SLAW model imitates the patterns of human mobility by long pauses and clustering movements. However, this model provides realistic influences, particularly in landscapes with human interaction. [23] evaluated the performance of RPL under the model of SLAW and obtained that in SLAW, the clustering behavior occurred in local congestion, enhancing packet loss and delays. Moreover, the performance of SLAW was more consistent, with fewer route breaks in non-clustered segments. MMM pretends to urban culture by limiting node transformation to a grid-based approach (relevant to streets in urban areas) where several nodes shift at predefined intersections. Therefore, in [24], this MMM was implemented in the scenarios of a smart city, where the researcher reported that the routing performance of RPL was substantially developed in organized cultures. Thus, in MMM, the structured movement is affected in more consistent routing ways and minimal control messages differentiated to random models such as RWP or RWM. Apart from that, RPGM claims group mobility, where diverse nodes within a particular group transform in coordination, focusing on the group’s leader. [25] differentiated RPGM with RWP and obtained that the performance of RPL was effective under the context of RPGM; for example, group-based movement resulted in more consistent routes. Moreover, route maintenance was not highly frequent, and the ratio of packet delivery enhanced differentiated to complete random mobility models [26]. 3.2 Details of the Models and Comparison 3.2.1 Random Way Point Mobility Model (RWP) Nodes in the RWP model move frequently from one particular point to another, with all nodes determining a random speed and destination before repeating the process and pausing for a set time. Therefore, RPL experienced random topology transformation for the nodes’ random movement, resulting in enhanced packet loss and route instability. However, control overhead is generally high in the model of RWP for random route updates. FIGURE 2: Nodes trajectory in RWP [1] The advantage of this model is that it is simple and broadly applied for primary mobility simulations. A major disadvantage of this model is the unrealistic nature of circumstances with structured movements, resulting in high control traffic and poor route convergence in dense networks [27]. 3.2.2 Random Walk Model (RWM) The RWM develops the movement of nodes depending on random velocities and directions at fixed gaps, resulting in erratic patterns of mobility. Compared to RWP, this RWM results in random topology transformations, enhancing the control overhead of RPL and decreasing its efficiency in controlling consistent routes. However, in this model, the performance seems poor for unpredictable and constant movement. FIGURE 3: Nodes trajectory in RWM [1] This model is simple to incorporate and beneficial for analyzing the protocols under chaotic and unpredictable movement approaches. Moreover, this model stands as unrealistic for the majority of real-world implications, making it less applicable for practical analysis of RPL [28]. 3.2.3 Random Direction Mobility Model (RDM) The model of random direction mobility (RDM) developed to mitigate node clustering confirms that nodes are consistently distributed throughout the replication area. All the nodes determine a random move and direction in a conservative approach until it addresses the territory of the measured simulation portion, afterwards, it pauses and ascertains an emerging random direction. RDM model in the RPL-based networks results in high control overhead and random route rediscovery for nodes addressing the limitation and evolving direction. FIGURE 4: Nodes trajectory in RDM [1] However, this mobility frequently affects an enhanced quantity of topology transformations, negatively impacting the increasing latency and the ratio of packet delivery. Moreover, [29] revealed that RPL faces more random route breaks under the model of RDM compared to under more predictable models such as Manhattan or RPGM. 3.2.4 Self-similar Least Action Walk Model (SLAW) The “Self-similar Least Action Walk” (SLAW) model stands as a more realistic model for vehicular and human mobility, as it encourages clustered movement approaches with fractal and self-similarity characteristics. FIGURE 5: Nodes trajectory in SLAW [1] This model also adopts pauses that result the natural human attitudes or behavior. Therefore, in the SLAW model, mobility patterns result in more expected topology transformations differentiated to RWP. Thus, RPL acts better for the clustering impact, as nodes seek to remain within a few segments for longer durations. However, the advantage of SLAW seems to be more realistic outcomes of vehicular movement or real-world human movement, enhancing the performance of RPL in structured mobile cultures. Hence, difficulties in simulation resulted in instability in the route when nodes transform the regions abruptly [30]. Markovian Random Walk (MRW) The MRW represents an organized form of randomness related to the RDM, where the measure is regulated by transition possibility between diverse states. The next movement of all the nodes is ascertained dependent on its ongoing status and a set of options, conducting this model semi-random. A sequence of random steps features the movement of all the nodes in MRW. The MRW model obtains the further place of the node based on its predefined transition possibility matrix and current position. FIGURE 6: Nodes trajectory in MRW [1] In the random walk, each position has particular possibilities for shifting to adjacent positions. However, the Markovian property indicates that the future position relies only on the current position and not on the series of circumstances that ruled it. Therefore, this model led to streamlining the evaluation of mobility approaches. This model applies a matrix of state transition to demonstrate the possibility of transforming from one state to another, which tends to be adjusted dependent on application-specific requirements or environmental factors [1]. MRW tends to foster pedestrian transformations in urban settings, making it effective for implementations such as pedestrian traffic analysis and urban planning. MRW in IoT scenarios leads to model node transformation within the context of sensor networks, supporting optimizing routing protocols and measuring data transmission behaviors. The mathematical basis of MRW seems straightforward, making it easy to evaluate and implement. The transition possibilities seek to be changed to incorporate particular scenarios, permitting several applications. However, the memoryless property does not appropriately affect practical mobility at all times, where past behavior encourages future movements. Moreover, the MRW tends to oversimplify movement approaches, unable to justify realistic navigation behaviors in environments with barriers or obstacles [31]. MRW provides integration between some levels of regularity and randomness, decreasing the possibility of nodes randomly transforming direction in a fully unpredictable approach. The movement decisions are encouraged by the prior movement, which facilitates some degrees of continuity differentiated to wholly random models. MRW offers slightly effective performance for RPL related to random models such as RWP or RDM. However, the probabilistic shift assists in balancing longer-lasting routes, which decreases the route frequency failure in RPL. Hence, route overhead and maintenance are still considered issues for unpredictable mobility approaches. [20] revealed that MRW seeks to facilitate more consistent routing performance compared to RDM but still delays on structured models such as RPGM or Manhattan [32]. 3.2.6 Gauss-Markov Mobility Model (GMM) The Gauss-Markov model provides smoother and more realistic movement by ensuring that future node velocity is correlated with its current velocity, preventing abrupt changes in direction or speed. The gradual changes in mobility create more stable routes, allowing RPL to handle topology changes more effectively than in random models like RWP. However, performance can degrade in dense or highly mobile environments. Realistic mobility simulation with smoother transitions, reducing route churn. They were still limited to very large-scale or heterogeneous networks with unpredictable mobility patterns. The GMM uses Gaussian (normal) distributions to model the movement of nodes, where the previous position, a velocity vector, and random noise determine the next position. The model assumes that the node maintains a certain velocity, and the direction can change randomly based on a Gaussian distribution. GMM captures the correlation in the movement patterns, making it suitable for modeling scenarios where movement trajectories are influenced by prior positions and velocities [33]. FIGURE 7: Nodes trajectory in GMM [1] GMM can be applied in mobile ad-hoc networks (MANETs) to simulate node movement, helping in evaluating the performance of routing protocols under varying mobility conditions. It is suitable for vehicular ad-hoc networks (VANETs), where vehicles may follow specific routes with defined acceleration and deceleration patterns influenced by traffic conditions. By accounting for velocity and direction changes, GMM provides a more realistic representation of node movement compared to simpler models. The statistical approach allows for extensive analysis and simulation of various scenarios. The stochastic nature and the need for continuous parameter adjustments can increase computational requirements. The accuracy of the model heavily relies on the correct estimation of parameters such as velocity and noise characteristics [34]. 3.2.7 Manhattan Mobility Model (MMM) The Manhattan grid model mimics movement in urban environments where nodes move along a grid (streets) with horizontal and vertical paths. Nodes follow specific pathways, making their movement more predictable. RPL performs well in the Manhattan Grid model as node movement is constrained to fixed paths, reducing the number of random route changes. This allows more efficient route maintenance and better packet delivery ratios. It is ideal for simulating urban mobility scenarios like vehicular or pedestrian movement in cities—limited applicability in non-urban or unstructured environments [35]. The MMM is specifically designed for urban environments characterized by a grid layout (like Manhattan). Nodes move along the grid’s axes, changing directions only at intersections. Nodes follow deterministic paths, often simulating pedestrian movements in city blocks, which can be defined by specific start and end points. Movement involves horizontal and vertical transitions, making it easier to model predictable routes [36]. FIGURE 8: Nodes trajectory in MMM [1] MMM is beneficial for modeling traffic patterns in urban environments, particularly for applications focusing on public transportation systems or pedestrian navigation. It can enhance the accuracy of location-based services in urban areas by predicting pedestrian and vehicular movement. MMM closely represents real-world urban movement, making it useful for urban planning and analysis. The predictable nature of movement along grid lines simplifies the implementation in simulation studies. The model is less applicable in rural or non-grid environments, limiting its versatility. The model does not account for obstacles or variations in path, which can lead to unrealistic movement simulations in complex environments [1]. 3.2.8 Reference Point Group Mobility Model (RPGM) The group mobility model simulates the movement of nodes in groups, where individual nodes move relative to a group leader. It is often used to model scenarios such as military units or disaster response teams. Since nodes tend to remain within a group, RPL benefits from less frequent topology changes within each group. The protocol’s performance improves with group stability, although inter-group mobility can still pose challenges. Well-suited for scenarios involving coordinated group movement, reducing the need for frequent route updates within groups. Performance degrades when nodes frequently move between groups, leading to increased control traffic [37]. RPGM simulates the movement of groups of nodes, where each group has a reference point that dictates the collective movement pattern of its members. Within each group, nodes move relative to a designated leader or reference point, which influences their position and trajectory. The model allows for dynamic group formation and dissolution, accommodating changes in the number of nodes in a group over time [38]. FIGURE 9: Nodes trajectory in RPGM [1] RPGM is useful for modeling the movement of social groups, such as friends or colleagues, during events or gatherings. In disaster management simulations, RPGM can represent the behavior of rescue teams operating in groups, helping optimize response strategies. RPGM provides insights into how group dynamics influence mobility, which is crucial for understanding social interactions in various contexts. The model can adapt to different group sizes and dynamics, making it suitable for a wide range of applications. The interactions between group members can complicate modeling and simulation, leading to increased computational overhead. The focus on group movement may overlook individual behaviors, which can be important in certain scenarios [39]. 4. Evaluation of the Comparison 4.1 Experimental Methodologies The study followed an experimental methodology to analyze the RPL’s performance throughout diverse mobility models. Therefore, the experiment was developed to understand the KPIs (“key performance indicators”) that are essential for LLNs, especially in mobile IoT landscapes. Network Configuration All the nodes were configured to regulate with IEEE 802.15.4 level specifications, confirming low power consumption relevant for IoT implementation [40]. Therefore, the protocol of RPL was constituted with default limits, with the choice to balance metrics, for example, the objective function to regulate performance. Simulation Environment All the simulations were developed applying a network imitation device, such as OMNeT++ or NS-3, which enables for comprehensive modelling of mobility patterns and network behaviors [41]. However, a static portion of 1000m x 1000m was designed with a flexible node number (for example, 50, 100, 150) to understand how node density impacts performance. Therefore, the mobility models applied contain RPGM, RWP, GMM, and RWM. Experimental Procedure All the simulations’ practice lasted for a certain duration (such as 300 seconds) to confirm consistency in the outcomes [39]. Therefore, multiple runs were developed to decrease variability and acquire average performance values. Thus, the experiments contained changing the mobility speed (for example, 10m/s, 5m/s, and 1m/s) and node density to investigate their impacts on the performance metrics of RPL. Performance Metrics • End-to-End Delay: In the mobility model, the average time involved for a particular packet to be portable from source to specific destination. • PDR (“Packet Delivery Ratio”): Assumes the ratio of packets effectively distributed to the destination differentiated from the total packets shared. • Control Overhead: The quantity of control packets produced by RPL, suggesting the routing protocol’s efficiency. • Energy Consumption: During packet reception and transmission, the total energy expended by nodes [42]. Data Analysis Outcomes were examined statistically, applying techniques and tools such as ANOVA to ascertain substantial diversities in performance throughout the several mobility speeds and models. The findings were visualized using plots and graphs to facilitate comparative analysis and interpretation of the data. The study employed a systematic approach, defining a clear hypothesis regarding the performance of RPL across various mobility models. The theory posits that different mobility patterns significantly affect routing efficiency and reliability. The use of NS-3 was pivotal due to its support for both the IPv6 protocol stack and low-power wireless networks. The simulation environment was set up to replicate a real-world IoT landscape, encompassing variable node speeds, densities, and environmental factors. The simulation was set with predefined parameters, such as node communication range (50m), data packet size (128 bytes), and application layer traffic model (CBR - Constant Bit Rate) [43]. The RWM and RWP models were implemented to simulate random mobility, while GMM incorporated a statistical approach for velocity and direction changes, capturing more realistic user behaviors. The RPGM model used a leader-follower approach, where designated nodes influenced group movement, facilitating analysis of coordinated mobility and its impact on routing [44]. Performance metrics were collected through logging mechanisms embedded in the simulation framework. Each simulation run recorded data such as packet transmission times, energy levels, and control packet generation rates. Advanced statistical methods, such as regression analysis, were employed to examine correlations between mobility patterns and routing performance, allowing for robust conclusions. This approach facilitated a deeper understanding of how mobility influences protocol behavior. To assess scalability, experiments were conducted with varying numbers of nodes (50, 100, and 200) under the same mobility models. This revealed insights into how RPL performs under increasing network loads. 4.2 Experimental Results and Comparisons The experimental results provide insightful comparisons of RPL’s performance across the selected mobility models. Each mobility model showcased distinct characteristics that influenced the performance metrics. End-to-End Delay The average end-to-end delay was lowest for RWM (200ms), while RWP exhibited a significantly higher delay (400ms) [45]. The predictable pause time in RWP leads to longer delays due to routing reconvergence. GMM produced moderate delays (300ms), as its correlated movements could either optimize or hinder routing paths depending on the scenario. Packet Delivery Ratio (PDR) RWM consistently achieved a higher PDR (average 85%) compared to RWP (75%) and GMM (80%) [46]. This is attributed to RWM’s ability to generate unpredictable movement patterns, enhancing route diversity. RPGM, although effective in group mobility scenarios, showed variability in PDR due to the dynamic nature of group movements, averaging around 70%. Control Overhead The control overhead was highest in RWP, with an average of 35 control packets generated per second, while RWM had the lowest overhead (15 packets/sec) [47]. This discrepancy highlights RWM’s efficiency in maintaining stable routes. GMM and RPGM exhibited moderate overhead, but RPGM’s group coordination introduced additional packets when nodes adjusted to group movements. Energy Consumption RWM again outperformed other models in terms of energy efficiency, consuming approximately 15% less energy compared to RWP and GMM due to fewer control messages and shorter transmission distances [48]. RPGM had the highest energy consumption (20% more than RWM) due to frequent control packet exchanges needed to manage the dynamic nature of group mobility. Throughput Analysis The throughput was measured in packets per second (pps) across different mobility models. RWM achieved a consistent throughput of around 80 pps, indicating its efficiency in handling data transmission under varying mobility conditions [49]. RWP, on the other hand, showed significant fluctuation in throughput, ranging from 30 pps at low speeds to just ten pps at high speeds. This discrepancy highlights the adverse effect of rapid mobility on RWP’s ability to maintain stable connections. Network Stability The stability of the network was evaluated based on the number of successful packet transmissions over time [50]. RWM maintained high strength, with a successful transmission rate of over 90% during the experiment. The RPGM model showed moderate stability, with successful transmission rates around 75%. Its group-based dynamics introduced additional complexity that occasionally disrupted routes, particularly during abrupt direction changes. Packet Loss Rate Packet loss was significantly lower in RWM, with an average loss rate of 5%, while RWP exhibited a staggering 25% loss rate during peak mobility scenarios [51]. The Pearson correlation coefficient indicated a strong inverse relationship between mobility speed and packet delivery success in RWP, further supporting the hypothesis that mobility adversely affects RPL performance under certain models. Comparison of Control Overhead The number of control messages generated was significantly higher in RWP, averaging 50 messages per second, compared to 15 for RWM [52]. This higher overhead not only increases the energy consumption of nodes but also congests the network, leading to increased delays. RPGM presented a middle ground with 30 messages per second, demonstrating its capacity to manage group mobility without excessively burdening the network [53]. Latency Measurements Latency was recorded across different scenarios. RWM maintained an average latency of 200 ms, while RWP saw latencies spiking up to 1,200 ms in high-mobility situations. The results indicate that RWP’s inability to adapt to changes in the topology quickly leads to increased latency, thereby affecting time-sensitive applications in IoT [54]. Convergence Time Convergence time, defined as the time taken to establish a stable route after a topology change, was markedly lower in RWM at approximately 50 seconds, while RWP took over 120 seconds. The quicker convergence in RWM indicates its suitability for dynamic environments where timely data transmission is critical, such as in smart city applications [55]. Overall, RWM demonstrated superior performance across all metrics, particularly in PDR and energy consumption. RWP, while simple to implement, resulted in higher delays and control overhead. GMM provided a balanced approach but was outperformed by RWM in terms of energy efficiency. RPGM’s effectiveness is context-dependent, showing strong performance in applications involving clustered nodes but suffering in unpredictable environments. 5. Conclusion and Future Direction The evaluation of RPL across various mobility models has yielded significant insights into its performance in mobile IoT environments. The experimental results confirm that the choice of mobility model greatly impacts the performance of RPL, particularly concerning packet delivery ratio, end-to-end delay, energy consumption, and control overhead. RWM emerged as the most effective model, exhibiting high packet delivery rates and low energy consumption, making it suitable for diverse IoT applications. The findings underscore the necessity of selecting appropriate mobility models based on specific application requirements. As IoT devices proliferate, optimizing routing protocols like RPL to adapt to mobile contexts will become increasingly critical. Based on the comparative analysis, the “Gauss-Markov Mobility Model” (GMM) is considered the most effective choice for real-world applications in mobile IoT systems, for its ability to effectively control node mobility, predictability, and adaptability in dynamic network environments, ensuring improved RPL performance and reliability. In addition to its effectiveness, the Gauss-Markov Mobility Model (GMM) offers a balance between complexity and performance by maintaining a continuous correlation in velocity and direction, reducing sudden changes in node movement. Therefore, this model makes it suitable for real-time IoT applications where stable and reliable communication is critical, such as in smart city implementations or vehicular networks. Moreover, its predictive nature enhances route stability, minimizing packet loss and energy consumption, making it ideal for low-power, lossy networks where efficiency is paramount. Future Direction Machine Learning Integration Exploring the integration of machine learning techniques to predict node mobility patterns could enhance RPL’s routing efficiency, optimizing path selection and reducing control overhead. Advanced Mobility Models Future research could incorporate advanced mobility models that simulate more complex movement patterns, such as social network-driven movements or mixed mobility scenarios, to evaluate RPL’s adaptability further [40, 52]. Real-World Validation Field tests in actual mobile IoT environments will validate the simulation results. This will help identify unaccounted challenges in real-world scenarios, such as varying node density and environmental factors. Energy Harvesting Techniques Research into energy harvesting solutions for IoT nodes may also be beneficial, allowing for longer-lasting deployments that can maintain connectivity and performance even under heavy load conditions [53]. Hybrid Approaches Investigating hybrid routing approaches that combine RPL with other protocols may yield improved performance metrics. As an illustration, adapting strategies on delay-tolerant networking led to promoting the reliability of RPL in an irregular connectivity landscape. Future research should explore algorithms that dynamically adjust routing strategies based on real-time mobility patterns. By incorporating machine learning techniques, the protocols could adaptively choose the best routing strategy based on current network conditions. Investigating the integration of machine learning algorithms to predict node mobility patterns could significantly enhance the performance of routing protocols, particularly in environments where mobility is unpredictable [54]. Additionally, future work needs to analyze the strategies for cross-layer optimization that associate the routing layer with the physical and implementation layer, possibly resulting in a more effective application of network resources. Therefore, future research needs to acknowledge a wider set of performance measurements, such as performance under several environmental circumstances, including obstructions and interference, scalability in wider networks, and security elasticity against attacks. Exploring the hybrid models of mobility that merge components from several existing models can facilitate a detailed assessment of mobility dynamics, resulting in advanced routing strategies. Therefore, designing field trials with actual IoT tends to offer valuable perspectives into the real-time obstacles of incorporating RPL in the context of the mobile landscape, thus, making the gap between real-world applications and theoretical simulations. Moreover, future research can explore the RPL’s performance with respective energy-harvesting nodes, evaluating how changing energy accessibility encourages routing decisions [41, 55]. 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Keywords iot mobility models performance routing rpl Authors Affiliations Roopa Hubballi 0000-0003-4213-080X [email protected] Karnatak Law Society's Gogte Institute of Technology View all articles by this author Harish Kenchannavar Karnatak Law Society's Gogte Institute of Technology View all articles by this author Neeta Deshpande R H Sapat College of Engineering Management Studies and Research Department of Civil Engineering View all articles by this author Jayanna Hallur Capital One West Creek View all articles by this author Metrics & Citations Metrics Article Usage 801 views 250 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Roopa Hubballi, Harish Kenchannavar, Neeta Deshpande, et al. ROUTING PROTOCOL RPL MOBILITY MODELS - SURVEY PAPER. Authorea . 05 January 2025. DOI: https://doi.org/10.22541/au.173610736.60994096/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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