Critical Analysis of Mobility Models on Power Consumption of MANET’S AODV Protocol

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Power consumption or energy constraints play an important role in the flow of information between nodes. In the dynamic topology of MANETs, energy or battery is a huge difficulty to keep the mobile node running for a long time. Any node can move from one position to another in a consistent manner, making it difficult to predict the movement of any node in a particular order. Due to this dynamic change in topology the route establishment and its maintenance becomes complicated. Hence energy utilization of the node is one of the parameters that may change the performance of MANETs. In this paper, simulation result based on AODV routing protocol with 50 random mobile nodes under file and random waypoint mobility models concerning generic, mica-motes and micaZ energy models have been analyzed. Simulation outcomes imply that the file mobility model exhibits superior preference over the random waypoint mobility models in generic, mica-motes and micaZ energy models MANETs Routing Protocols Power Consumption Network Topology Mobility Models Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. INTRODUCTION MANET is self-organizing network which form an uninformed arrangement of transportable nodes. Herein set-up, all nodes are mobile and know how to be associated vigorously at what time essential devoid of any federal control. The life span of every itinerant node be reliant on power and therefore the utilization of every mobile node is a significant, Larsson T. and Hedman N. ( 1998 ) 1 , Djenouri, D. et al. ( 2006 ) 2 . In diverse circumstances, for instance, armed actions, disaster rescue, scientific field missions; energy protection is significant job since every node would be able to stay alive in set of connections for extensive instant if the adequate power is offered in favor of the node. Different characteristics of MANET resembling dynamic topologies, bandwidth restraint, energy restraint and link unsteadiness posed critical challenges during communications. Along with them, power utilization is the main key performance metrics for MANET and straightforwardly relates to the outfitted duration of the network. Therefore, power utilization needs to be representation and examined in MANET. Energy Models The energy consumed by the source and receiver circuitry is calculated using a radio model and energy amplifier of the transmitter within the range of energy position roles of the radio (transmits, receive, idle and sleep modes). Significant radio power models are Generic, Mica-Motes, MicaZ and user defined energy model Varshney, P.K. et al. ( 2016 ) 3 . In MANETs, WeizhiMenget. al ( 2018 ) 14 and Samundra Deep et. al ( 2020 ) 27 made a point that in the emerging technologies like Blockchain and IoT, energy consumption by the mobile nodes would be a very important issue that needs to be addressed. Mobility models The exchange of data (packets) between two nodes is referred to as routing. Discovering and maintaining routes between sources and destinations in a dynamic topology with known links and little resource consumption is the basic objective of routing. A system that minimises routing loops and guarantees dependable communication exchanges is described by a routing protocol. Mobility is a critical component of MANETs and is crucial in assessing the effectiveness of routing systems. An elementary subject engage in MANETs is the recognition of best trail linking any starting place node to target node. MANET topologies are dynamic and ever-changing making routing is a challenging procedure. In transportable modelling, the behaviours and excitement brought on by user mobility can be described using both logical and replicating models. Current levels of mobility are viewed under the imitation model as being more inclusive and significant. These models support the development of cogent explanations for various issues. The RWP mobility model, the RPG mobility model, the Manhattan mobility model, the random Gaussian Markov mobility model, the motorway mobility model, the RWM model, and the random directional mobility model are some examples of mobility models, according to Camp, Boleng, and Davis ( 2002 ) 4 . Other models include the CSM model, column mobility model, and Markovian mobility model. Many coordinated assessment studies of routing protocols have used the RWP paradigm. The two primary models are: Random Waypoint Mobility (RWM) Model The RWP mobility model chooses indiscriminate targets and momentums for every node. Subsequent to the nodes attain their picked goals, they break in proceedings for a known amount of instant and after that the method is recurring. File-based Mobility (FBM) Model The FBM model uses waypoints for every node precisely via the client in a node location file. Every waypoint is a measurement of a node’s position and direction and the instant as a result of which the node disembarks to next position. The node shifts from one waypoint to the other that in a directly streak at a stable rate. Ad-hoc On-demand Distance Vector (AODV) Routing Protocol AODV presents a rapid reworked copy to dynamic connection state, few connection errors, and little dispensation and reminiscence usage overhead. This makes it possible for contributing mobile nodes that want to create and sustain ad-hoc networks to automatically begin multi-hop routing. AODV does not cache the whole route in any network node, allowing transportable nodes to quickly find routes for new targets. AODV allows mobile nodes to react gracefully to changes in the network's architecture and damaged links. It uses sequence numbers to prevent routing loops. 2. LITERATURE SURVEY Busari S.A. et.al ( 2018 ) 22 mentioned that performance of cellular systems does not scale proportionally with increase in the employed mmWave bandwidth. Feeney ( 2001 ) 6 performed imitation learning for DSR and AODV in mobile ad-hoc network and observed that DSR achieve superior than AODV protocol in packet delivery & high mobility. Jayakumar and Gopinath ( 2008 ) 7 measured the performance of DSR and AODV. Jain and Shrivastava ( 2011 ) 8 found that AODV achieves improved average end-to-end latency in the two ray ground model, while DSR achieves higher overall performance in the path loss propagation model. Sreerama and Das ( 2011 ) 9 scrutinized with the intention of while boost the pause time DSR go one better than AODV in phrases of packet delivery ratio. Shah et al., ( 2008 ) 10 analyzed that in stumpy mobility DSR carry out fine but in case of lofty mobility AODV perform superior than DSR. Parekh and Wandra ( 2013 ) 11 conducted simulation study and results indicated that DSDV is more preferable for a network with low mobility and less number of nodes. Additionally, research revealed that AODV works better in high mobility scenarios. Nand P., Astya R and Ali A. W. ( 2016 ) 12 have utilized several mobility models & assess the AODV protocol's performance in a star topology. On a variety of parameters, including jitter, throughput, dropped packets, end-to-end delay, energy consumption in various processing modes, and percentage of time spent in various processing modes, performance analysis and evaluation has been conducted. On the QualNET 5.0, simulation has been performed; the study reveals variation in the performance on several criteria Pandey K and Swaroop A (2011) 13 analyzed the performance of AODV, DSR, ZRP and DSDV. AODV and DSR are more reliable than ZRP and DSDV. Pahuja et al., ( 2012 ) 15 have done the comparative study for DSDV, AODV, DSR, TORA’s and ZRP routing protocols. Their results indicated that the DSDV is not satisfactory for any mobile network. A TORA show is comparatively deprived when throughput and packet delivery ratio are well thought-out. Nand and Sharma ( 2011 ) 16 conducted a simulation study using Qualnet 5.0.2 simulator on AODV, DSDV and DYMO routing protocols. According to simulation data, AODV performs better in terms of packet delivery than both DSR and DYMO routing protocols. Kaur and Rai (2012) 17 provided the a summary of the current routing protocols and stated that it is tricky to decide routing protocol in favor of dissimilar state of affairs as there is exchange between a variety of protocols. Kunz, T. ( 2008 ) 18 studied different variants of OLSR protocol and compared the protocol performance for realistic and ideal versions. Wang, Y. ( 2010 ) 19 presented an energy management model in his present study. With this energy model, he used an extended version of DSR routing protocol for further energy control. Simulation results indicated so as to the suggested routing protocol with power control mode could attain superior concert and extended the lifetime of network. Maan, F. and Mazhar, N. (2011) 20 have evaluated the feat of MANET routing protocols respecting mobility models. The simulation consequences point out that Node mobility significantly affects how well routing methods perform. Wang, T. and Huang, C. ( 2012 ) 21 have evaluated that network life span rely deeply on the energy utilization of nodes. Result shows that the routing protocols have modest effect on node energy utilization based on slack traffic situation. Kafhali, S. E. and Haqiq, A. ( 2013 ) 23 have evaluated AODV, DSR and DSDV routings protocols with Random Waypoint, Mnhattan Grid and Reference Point Group mobility models were used in this study. Er-Rouidi et. al. (2016) 24 studied energy consumption of AODV,DSR,DSDV and OLSR.P. K. et al. (2016) 25 compare the AODV and DYMO code of conduct with approbation to Generic, Mica-motes and MicaZ energy models for a exacting simulation situation using QualNet simulator. Communication has been evaluated regarding generic, Mica-motes and MicaZ energy models for a exacting simulation situation. It was originate that the Mica motes power model is most excellent as contrast to MicaZ and generic power models. Kumar Lubna Naaz Fatima at. el.(2019) 29 , centered on MANET's energy efficiency. Wenjuan Li et.al ( 2019 ) 26 because nodes in MANETs switch between several modes, such as transmitting, receiving, idle, and sleep, this increases consumption. Soheila Ghaneet. al (2020) 30 network lifetime is shortened by battery power. Therefore, it is given higher emphasis to decrease system power consumption, which will prolong the battery's life. The researcher GuojiXu et.al ( 2018 ) 31 and Zhenyu Zhouet. et al (2019) 28 have covered number of past studies to have a firsthand understanding of the subject matter of the study. Many aspects related to result of mobility and power consumption in Ad hoc routing protocols in different scenarios are touched in present review of literature. Theme of the previous studies covered the range from recital assessment of routing protocols to analysis of mobility outcome resting on power utilization in MANETs. According to Ako Abdullah et al(2022) 32 , the proposed E-AODV routing scheme performs better than Stable and bandwidth aware and traditional AODV routing protocols in terms of PDR, average network throughput, average end-to-end delay, and normalized routing overhead. Kumar, S. and R. Sidharth(2021) 34 evaluated the performance of AODV,DSR and ZRP under various mobility models including file base mobility. In order to accurately evaluate VANETs, computer simulation scenarios are created to effectively replicate human driving behaviour. Boucetta, S. I.et.al. ( 2021 ) 35 discuss these existing solutions and the need to improve them into intelligent tools. This paper critically analyze energy consumption in file and random waypoint mobility models concerning generic, mica-motes and micaZ energy models which are not taken up in any research work. 3. METHODOLOGY AND PERFORMANCE METRICS 3.1 Selection of AODV routing protocol AODV perform better while the quantity of nodes augments because nodes turn out to be supplementary stationary and show the way to more stable trail from source to destination. Other routing protocols recital goes down while number of nodes increases with respect to energy consumptions. In this study pause time is increased up to 30 seconds, change the interval, start and end time, which increases the performance of AODV routing protocols less packets are dropped due to link breaks. AODV routing protocols finds a loop free if a link breaks and sends the remaining packets on the new route. So it requires extended end-to-end delay time contrast to others. AODV exhibits straight end-to-end delay of data packet compare to others. Researchers have selected AODV routing protocol for simulation purpose and compare the file and random way point mobility models. Researchers choose only 50 randomly place node in 1000m X 1000m terrain size for better performance in both the file and random way point models. Simulation is the most apposite technique for determining the custom of routing protocols as the results of a simulation study are more practical and reasonably alike with a real circumstance. So that researcher preferred the QualNet 5.0.2 simulator for conducting the experiments on the different simulation network scenarios. 3.2Simulation Setup This simulation study aims to calculate the AODV routing protocol's energy usage in the Generic, Mica-Mote, and MicaZ wireless energy models under file and random way point mobility models mobility with 50 randomly deployed mobile nodes. AODV protocol with 802.11 MAC layer protocol and CBR traffic type is used for simulation purpose. A 1200 mAH battery with Linear Model and battery charge monitoring interval of 60s is taken into consideration. A total of 10,000 bytes of data is to be transmitted in packet of 512 bytes. A chunk of 50 nodes were placed randomly in terrain dimension of 1000m * 1000m.Nodes were allowed to move with a minimum speed of 0mps and maximum speed of 10mps with a pause time of 30s.Generic, Mica-Motes and MicaZ energy models are used for the simulation study. 3.3 Performance Metrics To analyze and contrast the routine of routing protocols, the following metrics have been used: Powers utilized in transmit form A node is theoretical to be in broadcast manner while it corresponds facts packet to supplementary nodes in set of connections. Power utilized Tx = (330*Packet Size)/2*106 Power utilized in received mode Equation for finding Power utilized in received mode Power utilized Rx = (230* Packet Size)/2*106 Power utilized in idle mode: Idle power is a wear out power that be supposed to be decreased or get rid of. The power utilized in Idle Mode is: PI = PR (PI: Power utilized in Idle, PR: Power utilized in Received Mode) Formulae used to measure energy consumption in a specific mode Fatima L N et al ( 2019 ) 33 Eng_Trans (Transmit mode Energy consumption) = (TC *volt) * time (1) Eng_Recv (Receive mode Energy consumption) = (RC *volt) * time (2) Eng_Idle (Idle mode Energy consumption) = (IC *volt) * time (3) Eng_Sleep (Sleep mode Energy consumption) = (SC *volt) * time (4) Where TC = Transmitting current,RC = Receiving current, IC = Idle current, SC = Sleep current A specific node's total energy consumption would be the sum of Eng_Trans, Eng_Recv, Eng_Idle and Eng_Sleep. Algorithm for calculating power usage Step 1: Initialize the variables: Interval, Actual Interval = 0, Pxi (Energy consumed by current mode) = 0, Current = getNodeTime (); Step 2: Calculate values of Pxi and Current at a given instant t Actual Interval = (Current - Start time) seconds; If (Interval = Actual Interval) Then go to step 4 Else Go to step 3 Step 3: Check the State of the battery. If (Remaining battery charge ! = 0) then go to step 4 and determine the power usage in current mode and decrease the battery charge. Else Current = End time of battery; Go to step 6 Step 4: Calculate the energy consumed by node Determine energy consumption by node using Eq. 1, 2, 3 and 4. Remaining battery charge = Total battery charge–Energy consumed by current activity (Ex) Increment the value of Energy consumed in X mode. Total energy in X (Transmit/ Receive/ Idle/ Sleep) mode = Ex + Pxi Pxi = Total energy in X mode; Step 5: Go to step 2 Step 6: Print the output 4. PERFORMANCE EVALUATION Outcomes of power utilized (in mJoule) in transmit mode, power utilized (in mJoule) in receive mode, power utilized (in mJoule) in idle mode are given below: Power utilized(in mJoule) in Transmit mode The simulation consequences are exposed in Figs. 1, 2 and 3. It has been found that AODV routing protocols used less power in the transmit mode for the generic, mica-motes and micaZ power models then did far therandom way point mobility models., Power utilized (in mJoule) in Received mode : The simulation consequences are exposed in Figs. 4, 5 and 6. With regard to general, mica-motes, and micaZpower used models, it was found that AODV routing protocol consumed less energy in received mode in file mobility model compared to random waypoint mobility model. Power utilized(in mJoule) in Idle mode: The simulation consequences are exposed in Figs. 7, 8 and 9. It is observed that AODV routing protocol consumed less energy in idle mode in file mobility model as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower models. Power utilized(in mJoule) in transmit, received and idle modes by AODV routing protocols with 50 random nodes is less in file mobility models as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower utilized models. It is happened due to signals received and forwarded to MAC and Signals received but with errors timing is better in file mobility models then random waypoint mobility model shown Fig. 13, 14. 5. CONCLUSIONS Improvement in battery power utilization of routing protocols is critical tasks as the outcomes of these routing protocols diverge with different parameters in MANETs. Quality of routing protocols has been tested in some of the previous studies using various parameters such as network size, node density, pause time, and speed of nodes. There are various other radio power utilized models and a different mobility model that may affect the power expenditure of routing protocols in MANET’s. In this study AODV routing protocol is used to test with file and random waypoint mobility models respectively in generic, mica-motes and micaZpower utilized models. According to simulation results, Power utilized (in mJoule) in transmit, received and idle modes by AODV routing protocols with 50 random nodes is less in file mobility models as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower utilized models. Declarations Funding Statements: We have no Funding related to the manuscript. Data Availability Statement: We are not getting any data from other sources. 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Ako Abdullah, Emre Ozen and Husnu Bayramoglu,” Enhanced-AODV Routing Protocol to ImproveRoute Stability of MANETs”, The International Arab Journal of Information Technology, Vol. 19(5), 2022. Fatima LN, Mahin SH, Taranum F. 2019. Efficient strategies to reduce power consumption in MANETs. PeerJ Comput. Sci. 5:e228,2019. Kumar, S., and R. Sidharth. "Performance of AODV, DSR and ZRP for Different Mobility Model in MANET." vol 1049 (2021): 0-2. Boucetta, S. I., Guichi, Y., & Johanyák, Z. C. (2021, May). Review of Mobility Scenarios Generators for Vehicular Ad-Hoc Networks Simulators. In Journal of Physics: Conference Series (Vol. 1935, No. 1, p. 012006). IOP Publishing. 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. 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legend\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3129059/v1/056d898c6b9fe23f9f35057e.png"},{"id":39806469,"identity":"a1cfafee-1320-4656-941d-9942615b2ed7","added_by":"auto","created_at":"2023-07-10 15:36:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1553783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3129059/v1/049578c2-ecec-4655-86a9-431f4a1c2048.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Critical Analysis of Mobility Models on Power Consumption of MANET’S AODV Protocol","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eMANET is self-organizing network which form an uninformed arrangement of transportable nodes. Herein set-up, all nodes are mobile and know how to be associated vigorously at what time essential devoid of any federal control. The life span of every itinerant node be reliant on power and therefore the utilization of every mobile node is a significant, Larsson T. and Hedman N. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003csup\u003e1\u003c/sup\u003e, Djenouri, D. et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003csup\u003e2\u003c/sup\u003e. In diverse circumstances, for instance, armed actions, disaster rescue, scientific field missions; energy protection is significant job since every node would be able to stay alive in set of connections for extensive instant if the adequate power is offered in favor of the node. Different characteristics of MANET resembling dynamic topologies, bandwidth restraint, energy restraint and link unsteadiness posed critical challenges during communications. Along with them, power utilization is the main key performance metrics for MANET and straightforwardly relates to the outfitted duration of the network. Therefore, power utilization needs to be representation and examined in MANET.\u003c/p\u003e \u003cp\u003eEnergy Models\u003c/p\u003e \u003cp\u003eThe energy consumed by the source and receiver circuitry is calculated using a radio model and energy amplifier of the transmitter within the range of energy position roles of the radio (transmits, receive, idle and sleep modes). Significant radio power models are Generic, Mica-Motes, MicaZ and user defined energy model Varshney, P.K. et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003csup\u003e3\u003c/sup\u003e. In MANETs, WeizhiMenget. al (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003csup\u003e14\u003c/sup\u003e and Samundra Deep et. al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003csup\u003e27\u003c/sup\u003emade a point that in the emerging technologies like Blockchain and IoT, energy consumption by the mobile nodes would be a very important issue that needs to be addressed.\u003c/p\u003e \u003cp\u003eMobility models\u003c/p\u003e \u003cp\u003eThe exchange of data (packets) between two nodes is referred to as routing. Discovering and maintaining routes between sources and destinations in a dynamic topology with known links and little resource consumption is the basic objective of routing. A system that minimises routing loops and guarantees dependable communication exchanges is described by a routing protocol.\u003c/p\u003e \u003cp\u003eMobility is a critical component of MANETs and is crucial in assessing the effectiveness of routing systems. An elementary subject engage in MANETs is the recognition of best trail linking any starting place node to target node. MANET topologies are dynamic and ever-changing making routing is a challenging procedure. In transportable modelling, the behaviours and excitement brought on by user mobility can be described using both logical and replicating models. Current levels of mobility are viewed under the imitation model as being more inclusive and significant. These models support the development of cogent explanations for various issues. The RWP mobility model, the RPG mobility model, the Manhattan mobility model, the random Gaussian Markov mobility model, the motorway mobility model, the RWM model, and the random directional mobility model are some examples of mobility models, according to Camp, Boleng, and Davis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003csup\u003e4\u003c/sup\u003e. Other models include the CSM model, column mobility model, and Markovian mobility model. Many coordinated assessment studies of routing protocols have used the RWP paradigm. The two primary models are:\u003c/p\u003e \u003cp\u003eRandom Waypoint Mobility (RWM) Model\u003c/p\u003e \u003cp\u003eThe RWP mobility model chooses indiscriminate targets and momentums for every node. Subsequent to the nodes attain their picked goals, they break in proceedings for a known amount of instant and after that the method is recurring.\u003c/p\u003e \u003cp\u003eFile-based Mobility (FBM) Model\u003c/p\u003e \u003cp\u003eThe FBM model uses waypoints for every node precisely via the client in a node location file. Every waypoint is a measurement of a node\u0026rsquo;s position and direction and the instant as a result of which the node disembarks to next position. The node shifts from one waypoint to the other that in a directly streak at a stable rate.\u003c/p\u003e \u003cp\u003eAd-hoc On-demand Distance Vector (AODV) Routing Protocol\u003c/p\u003e \u003cp\u003eAODV presents a rapid reworked copy to dynamic connection state, few connection errors, and little dispensation and reminiscence usage overhead. This makes it possible for contributing mobile nodes that want to create and sustain ad-hoc networks to automatically begin multi-hop routing.\u003c/p\u003e \u003cp\u003eAODV does not cache the whole route in any network node, allowing transportable nodes to quickly find routes for new targets. AODV allows mobile nodes to react gracefully to changes in the network's architecture and damaged links. It uses sequence numbers to prevent routing loops.\u003c/p\u003e"},{"header":"2. LITERATURE SURVEY","content":"\u003cp\u003eBusari S.A. et.al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e mentioned that performance of cellular systems does not scale proportionally with increase in the employed mmWave bandwidth.\u003c/p\u003e \u003cp\u003eFeeney (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003csup\u003e6\u003c/sup\u003e performed imitation learning for DSR and AODV in mobile ad-hoc network and observed that DSR achieve superior than AODV protocol in packet delivery \u0026amp; high mobility. Jayakumar and Gopinath (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003csup\u003e7\u003c/sup\u003e measured the performance of DSR and AODV. Jain and Shrivastava (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003csup\u003e8\u003c/sup\u003e found that AODV achieves improved average end-to-end latency in the two ray ground model, while DSR achieves higher overall performance in the path loss propagation model.\u003c/p\u003e \u003cp\u003eSreerama and Das (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003csup\u003e9\u003c/sup\u003escrutinized with the intention of while boost the pause time DSR go one better than AODV in phrases of packet delivery ratio. Shah et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003csup\u003e10\u003c/sup\u003e analyzed that in stumpy mobility DSR carry out fine but in case of lofty mobility AODV perform superior than DSR. Parekh and Wandra (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003csup\u003e11\u003c/sup\u003e conducted simulation study and results indicated that DSDV is more preferable for a network with low mobility and less number of nodes. Additionally, research revealed that AODV works better in high mobility scenarios.\u003c/p\u003e \u003cp\u003eNand P., Astya R and Ali A. W. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003csup\u003e12\u003c/sup\u003e have utilized several mobility models \u0026amp; assess the AODV protocol's performance in a star topology. On a variety of parameters, including jitter, throughput, dropped packets, end-to-end delay, energy consumption in various processing modes, and percentage of time spent in various processing modes, performance analysis and evaluation has been conducted. On the QualNET 5.0, simulation has been performed; the study reveals variation in the performance on several criteria\u003c/p\u003e \u003cp\u003ePandey K and Swaroop A (2011)\u003csup\u003e13\u003c/sup\u003e analyzed the performance of AODV, DSR, ZRP and DSDV. AODV and DSR are more reliable than ZRP and DSDV. Pahuja et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003csup\u003e15\u003c/sup\u003e have done the comparative study for DSDV, AODV, DSR, TORA\u0026rsquo;s and ZRP routing protocols. Their results indicated that the DSDV is not satisfactory for any mobile network. A TORA show is comparatively deprived when throughput and packet delivery ratio are well thought-out.\u003c/p\u003e \u003cp\u003eNand and Sharma (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003csup\u003e16\u003c/sup\u003e conducted a simulation study using Qualnet 5.0.2 simulator on AODV, DSDV and DYMO routing protocols. According to simulation data, AODV performs better in terms of packet delivery than both DSR and DYMO routing protocols.\u003c/p\u003e \u003cp\u003eKaur and Rai (2012)\u003csup\u003e17\u003c/sup\u003e provided the a summary of the current routing protocols and stated that it is tricky to decide routing protocol in favor of dissimilar state of affairs as there is exchange between a variety of protocols. Kunz, T. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003csup\u003e18\u003c/sup\u003e studied different variants of OLSR protocol and compared the protocol performance for realistic and ideal versions. Wang, Y. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003csup\u003e19\u003c/sup\u003e presented an energy management model in his present study. With this energy model, he used an extended version of DSR routing protocol for further energy control. Simulation results indicated so as to the suggested routing protocol with power control mode could attain superior concert and extended the lifetime of network.\u003c/p\u003e \u003cp\u003eMaan, F. and Mazhar, N. (2011)\u003csup\u003e20\u003c/sup\u003e have evaluated the feat of MANET routing protocols respecting mobility models. The simulation consequences point out that Node mobility significantly affects how well routing methods perform. Wang, T. and Huang, C. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003csup\u003e21\u003c/sup\u003e have evaluated that network life span rely deeply on the energy utilization of nodes. Result shows that the routing protocols have modest effect on node energy utilization based on slack traffic situation.\u003c/p\u003e \u003cp\u003eKafhali, S. E. and Haqiq, A. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003csup\u003e23\u003c/sup\u003e have evaluated AODV, DSR and DSDV routings protocols with Random Waypoint, Mnhattan Grid and Reference Point Group mobility models were used in this study. Er-Rouidi et. al. (2016)\u003csup\u003e24\u003c/sup\u003e studied energy consumption of AODV,DSR,DSDV and OLSR.P. K. et al. (2016)\u003csup\u003e25\u003c/sup\u003e compare the AODV and DYMO code of conduct with approbation to Generic, Mica-motes and MicaZ energy models for a exacting simulation situation using QualNet simulator. Communication has been evaluated regarding generic, Mica-motes and MicaZ energy models for a exacting simulation situation. It was originate that the Mica motes power model is most excellent as contrast to MicaZ and generic power models.\u003c/p\u003e \u003cp\u003eKumar Lubna Naaz Fatima at. el.(2019)\u003csup\u003e29\u003c/sup\u003e, centered on MANET's energy efficiency. Wenjuan Li et.al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003csup\u003e26\u003c/sup\u003e because nodes in MANETs switch between several modes, such as transmitting, receiving, idle, and sleep, this increases consumption. Soheila Ghaneet. al (2020)\u003csup\u003e30\u003c/sup\u003e network lifetime is shortened by battery power. Therefore, it is given higher emphasis to decrease system power consumption, which will prolong the battery's life. The researcher GuojiXu et.al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003csup\u003e31\u003c/sup\u003e and Zhenyu Zhouet. et al (2019)\u003csup\u003e28\u003c/sup\u003e have covered number of past studies to have a firsthand understanding of the subject matter of the study. Many aspects related to result of mobility and power consumption in Ad hoc routing protocols in different scenarios are touched in present review of literature. Theme of the previous studies covered the range from recital assessment of routing protocols to analysis of mobility outcome resting on power utilization in MANETs.\u003c/p\u003e \u003cp\u003eAccording to Ako Abdullah et al(2022)\u003csup\u003e32\u003c/sup\u003e, the proposed E-AODV routing scheme performs better than Stable and bandwidth aware and traditional AODV routing protocols in terms of PDR, average network throughput, average end-to-end delay, and normalized routing overhead. Kumar, S. and R. Sidharth(2021)\u003csup\u003e34\u003c/sup\u003e evaluated the performance of AODV,DSR and ZRP under various mobility models including file base mobility. In order to accurately evaluate VANETs, computer simulation scenarios are created to effectively replicate human driving behaviour. Boucetta, S. I.et.al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003csup\u003e35\u003c/sup\u003e discuss these existing solutions and the need to improve them into intelligent tools.\u003c/p\u003e \u003cp\u003eThis paper critically analyze energy consumption in file and random waypoint mobility models concerning generic, mica-motes and micaZ energy models which are not taken up in any research work.\u003c/p\u003e"},{"header":"3. METHODOLOGY AND PERFORMANCE METRICS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Selection of AODV routing protocol\u003c/h2\u003e \u003cp\u003eAODV perform better while the quantity of nodes augments because nodes turn out to be supplementary stationary and show the way to more stable trail from source to destination. Other routing protocols recital goes down while number of nodes increases with respect to energy consumptions. In this study pause time is increased up to 30 seconds, change the interval, start and end time, which increases the performance of AODV routing protocols less packets are dropped due to link breaks. AODV routing protocols finds a loop free if a link breaks and sends the remaining packets on the new route. So it requires extended end-to-end delay time contrast to others. AODV exhibits straight end-to-end delay of data packet compare to others. Researchers have selected AODV routing protocol for simulation purpose and compare the file and random way point mobility models. Researchers choose only 50 randomly place node in 1000m X 1000m terrain size for better performance in both the file and random way point models.\u003c/p\u003e \u003cp\u003eSimulation is the most apposite technique for determining the custom of routing protocols as the results of a simulation study are more practical and reasonably alike with a real circumstance. So that researcher preferred the QualNet 5.0.2 simulator for conducting the experiments on the different simulation network scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2Simulation Setup\u003c/h2\u003e \u003cp\u003eThis simulation study aims to calculate the AODV routing protocol's energy usage in the Generic, Mica-Mote, and MicaZ wireless energy models under file and random way point mobility models mobility with 50 randomly deployed mobile nodes. AODV protocol with 802.11 MAC layer protocol and CBR traffic type is used for simulation purpose. A 1200 mAH battery with Linear Model and battery charge monitoring interval of 60s is taken into consideration. A total of 10,000 bytes of data is to be transmitted in packet of 512 bytes. A chunk of 50 nodes were placed randomly in terrain dimension of 1000m * 1000m.Nodes were allowed to move with a minimum speed of 0mps and maximum speed of 10mps with a pause time of 30s.Generic, Mica-Motes and MicaZ energy models are used for the simulation study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Performance Metrics\u003c/h2\u003e \u003cp\u003eTo analyze and contrast the routine of routing protocols, the following metrics have been used:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePowers utilized in transmit form\u003c/strong\u003e \u003cp\u003eA node is theoretical to be in broadcast manner while it corresponds facts packet to supplementary nodes in set of connections.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePower utilized Tx = (330*Packet Size)/2*106\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePower utilized in received mode\u003c/strong\u003e \u003cp\u003eEquation for finding Power utilized in received mode\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePower utilized Rx = (230* Packet Size)/2*106\u003c/p\u003e \u003cp\u003ePower utilized in idle mode: Idle power is a wear out power that be supposed to be decreased or get rid of. The power utilized in Idle Mode is:\u003c/p\u003e \u003cp\u003ePI\u0026thinsp;=\u0026thinsp;PR (PI: Power utilized in Idle, PR: Power utilized in Received Mode)\u003c/p\u003e \u003cp\u003eFormulae used to measure energy consumption in a specific mode Fatima L N et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEng_Trans (Transmit mode Energy consumption) = (TC *volt) * time (1)\u003c/p\u003e \u003cp\u003eEng_Recv (Receive mode Energy consumption) = (RC *volt) * time (2)\u003c/p\u003e \u003cp\u003eEng_Idle (Idle mode Energy consumption) = (IC *volt) * time (3)\u003c/p\u003e \u003cp\u003eEng_Sleep (Sleep mode Energy consumption) = (SC *volt) * time (4)\u003c/p\u003e \u003cp\u003eWhere TC\u0026thinsp;=\u0026thinsp;Transmitting current,RC\u0026thinsp;=\u0026thinsp;Receiving current, IC\u0026thinsp;=\u0026thinsp;Idle current, SC\u0026thinsp;=\u0026thinsp;Sleep current\u003c/p\u003e \u003cp\u003eA specific node's total energy consumption would be the sum of Eng_Trans, Eng_Recv, Eng_Idle and Eng_Sleep.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlgorithm for calculating power usage\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStep 1: Initialize the variables: Interval, Actual Interval\u0026thinsp;=\u0026thinsp;0,\u003c/p\u003e \u003cp\u003ePxi (Energy consumed by current mode)\u0026thinsp;=\u0026thinsp;0,\u003c/p\u003e \u003cp\u003eCurrent\u0026thinsp;=\u0026thinsp;getNodeTime ();\u003c/p\u003e \u003cp\u003eStep 2: Calculate values of Pxi and Current at a given instant t\u003c/p\u003e \u003cp\u003eActual Interval = (Current - Start time) seconds;\u003c/p\u003e \u003cp\u003eIf (Interval\u0026thinsp;=\u0026thinsp;Actual Interval)\u003c/p\u003e \u003cp\u003eThen go to step 4\u003c/p\u003e \u003cp\u003eElse\u003c/p\u003e \u003cp\u003eGo to step 3\u003c/p\u003e \u003cp\u003eStep 3: Check the State of the battery.\u003c/p\u003e \u003cp\u003eIf (Remaining battery charge ! = 0) then go to step 4 and determine the power usage in current mode and decrease the battery charge.\u003c/p\u003e \u003cp\u003eElse\u003c/p\u003e \u003cp\u003eCurrent\u0026thinsp;=\u0026thinsp;End time of battery; Go to step 6\u003c/p\u003e \u003cp\u003eStep 4: Calculate the energy consumed by node\u003c/p\u003e \u003cp\u003eDetermine energy consumption by node using Eq.\u0026nbsp;1, 2, 3 and 4.\u003c/p\u003e \u003cp\u003eRemaining battery charge\u0026thinsp;=\u0026thinsp;Total battery charge\u0026ndash;Energy consumed by current activity (Ex)\u003c/p\u003e \u003cp\u003eIncrement the value of Energy consumed in X mode.\u003c/p\u003e \u003cp\u003eTotal energy in X (Transmit/ Receive/ Idle/ Sleep) mode\u0026thinsp;=\u0026thinsp;Ex\u0026thinsp;+\u0026thinsp;Pxi\u003c/p\u003e \u003cp\u003ePxi\u0026thinsp;=\u0026thinsp;Total energy in X mode;\u003c/p\u003e \u003cp\u003eStep 5: Go to step 2\u003c/p\u003e \u003cp\u003eStep 6: Print the output\u003c/p\u003e \u003c/div\u003e"},{"header":"4. PERFORMANCE EVALUATION","content":"\u003cp\u003eOutcomes of power utilized (in mJoule) in transmit mode, power utilized (in mJoule) in receive mode, power utilized (in mJoule) in idle mode are given below:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower utilized(in mJoule) in Transmit mode\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation consequences are exposed in Figs. 1, 2 and 3. It has been found that AODV routing protocols used less power in the transmit mode for the generic, mica-motes and micaZ power models then did far therandom way point mobility models.,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePower utilized (in mJoule) in Received mode\u003c/em\u003e: The simulation consequences are exposed in Figs. 4, 5 and 6. With regard to general, mica-motes, and micaZpower used models, it was found that AODV routing protocol consumed less energy in received mode in file mobility model compared to random waypoint mobility model.\u003c/p\u003e\n\u003cp\u003ePower utilized(in mJoule) in Idle mode: The simulation consequences are exposed in Figs. 7, 8 and 9. It is observed that AODV routing protocol consumed less energy in idle mode in file mobility model as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower models.\u003c/p\u003e\n\u003cp\u003ePower utilized(in mJoule) in transmit, received and idle modes by AODV routing protocols with 50 random nodes is less in file mobility models as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower utilized models. It is happened due to signals received and forwarded to MAC and Signals received but with errors timing is better in file mobility models then random waypoint mobility model shown Fig.\u0026nbsp;13, 14.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eImprovement in battery power utilization of routing protocols is critical tasks as the outcomes of these routing protocols diverge with different parameters in MANETs. Quality of routing protocols has been tested in some of the previous studies using various parameters such as network size, node density, pause time, and speed of nodes. There are various other radio power utilized models and a different mobility model that may affect the power expenditure of routing protocols in MANET\u0026rsquo;s. In this study AODV routing protocol is used to test with file and random waypoint mobility models respectively in generic, mica-motes and micaZpower utilized models. According to simulation results, Power utilized (in mJoule) in transmit, received and idle modes by AODV routing protocols with 50 random nodes is less in file mobility models as compared to random waypoint mobility model with respect to generic, mica-motes and micaZpower utilized models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statements:\u003c/h2\u003e \u003cp\u003eWe have no Funding related to the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eWe are not getting any data from other sources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLarsson T. and Hedman N. ,\u0026quot;Routing protocols in wireless ad-hoc networks: a simulation study\u0026quot;,DigitalaVetenskapliga Arkivet,1998.\u003c/li\u003e\n\u003cli\u003eDjenouri, D., Derhab, A. and Badache N., \u0026quot;Ad hoc Networks Routing Protocols and Mobility\u0026quot;, The international Arab Journal of Information Technology 3(2), pp. 126-133, 2006.\u003c/li\u003e\n\u003cli\u003eVarshney, P. 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Efficient strategies to reduce power consumption in MANETs. PeerJ Comput. Sci. 5:e228,2019.\u003c/li\u003e\n\u003cli\u003eKumar, S., and R. Sidharth. \u0026quot;Performance of AODV, DSR and ZRP for Different Mobility Model in MANET.\u0026quot; vol 1049 (2021): 0-2.\u003c/li\u003e\n\u003cli\u003eBoucetta, S. I., Guichi, Y., \u0026amp; Johany\u0026aacute;k, Z. C. (2021, May). Review of Mobility Scenarios Generators for Vehicular Ad-Hoc Networks Simulators. In Journal of Physics: Conference Series (Vol. 1935, No. 1, p. 012006). IOP Publishing.\u003c/li\u003e\n\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":"MANETs, Routing Protocols, Power Consumption, Network Topology, Mobility Models","lastPublishedDoi":"10.21203/rs.3.rs-3129059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3129059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMobile Adhoc Networks (MANETs) permit mobile nodes to communicate devoid of any sustain of predetermined and federal network as it does not follow any preset network communications and all nodes are gratis to travel from one position to a further. Power consumption or energy constraints play an important role in the flow of information between nodes. In the dynamic topology of MANETs, energy or battery is a huge difficulty to keep the mobile node running for a long time.\u003c/p\u003e \u003cp\u003eAny node can move from one position to another in a consistent manner, making it difficult to predict the movement of any node in a particular order. Due to this dynamic change in topology the route establishment and its maintenance becomes complicated. Hence energy utilization of the node is one of the parameters that may change the performance of MANETs. In this paper, simulation result based on AODV routing protocol with 50 random mobile nodes under file and random waypoint mobility models concerning generic, mica-motes and micaZ energy models have been analyzed. Simulation outcomes imply that the file mobility model exhibits superior preference over the random waypoint mobility models in generic, mica-motes and micaZ energy models\u003c/p\u003e","manuscriptTitle":"Critical Analysis of Mobility Models on Power Consumption of MANET’S AODV Protocol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-10 15:20:01","doi":"10.21203/rs.3.rs-3129059/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":"32f20ca5-c7d9-4721-b94e-6953ab0addc6","owner":[],"postedDate":"July 10th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-10T15:20:04+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-10 15:20:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3129059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3129059","identity":"rs-3129059","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}