Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols

Zemrane, Hamza; Baddi, Youssef; Hasbi, Abderrahim

doi:10.1016/j.procs.2019.11.014

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…The next node, sequence number, and hop count are the three key entries that can be used in the AODV protocol routing table [17]. The AODV protocol distinguishes routes from source to destination by broadcasting [18], requesting a response message to all of its neighboring nodes [19], and seeks a correct route from source to destination. After the broadcasting of RREQ messages using the AODV protocol, there is the possibility of discovery of more than one routes [20], in such cases the routes with the maximum sequence number are selected [21] for data transmission.…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of AODV and OLSR protocol in Mobile Ad Hoc Network using NS3

S¹,

Bappalige

2020

IJMTS

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Mobile Ad Hoc networks is a network in which energy is a main constraint and selection of a protocol that minimizes the energy usage is a key issue. Mobile Ad hoc network communicates with other nodes, without the help of base station and Communication is possible by forwarding a data unit consisting of control information and user data known as packets from one node to other. Furthermore, another key issue in mobile ad hoc networks is routing since the nodes are in mobility and tend to change the paths and move out of the network. The evaluation of energy efficient routing protocols can be effectively performed using NS3. Three types of routing protocols can be seen, Reactive, Proactive and Hybrid and in this paper, AODV a reactive protocol and OLSR a proactive protocol is compared and Delivery ratio of packets, Packet Loss and count of packets received are evaluated to analyze the energy efficiency of protocols based on these metrics.

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Section: Methodsmentioning

confidence: 99%

Performance Analysis of AODV and OLSR protocol in Mobile Ad Hoc Network using NS3

S¹,

Bappalige

2020

IJMTS

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“…Reactive protocol. Also called "On-demand" [9], establish (from a node given) a route to a destination (s) only when a node initiates a process called: "route discovery process". Once this route has been established, it will therefore be kept in the routing table until the destination is no longer accessible or the time for establishing the routes (configured by the protocol) is considered to have expired.…”

Section: Routing In Uaanet Network Of Dronesmentioning

confidence: 99%

Routing Communication Inside Ad Hoc Drones Network

Zemrane¹,

Baddi

Hasbi³

2021

Int. J. Interact. Mob. Technol.

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The world knows a constant development of technology applied in different sectors of activities: health, factories, homes, transportation, and others, one of the big axes that take a lot of attention today is the drone’s field. To communicate information a fleet of drones can use different communication architectures: centralized communication architecture, satellite communication architecture, cellular network communication architecture and a specific AdHoc communication architecture called the UAANET drones architecture. In our work we focused specifically on the routing of information inside the UAANET where we analyze and compare the performances of the reactive protocol AODV and the proactive protocol OLSR, when the UAANET use an applications based on the HTTP protocol, the FTP protocol, the database queries, voice application, and video conferencing application.

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“…A sensor is a device that can sense the change in its surrounding environment [67], [68]. The Internet of Things can fabricate and advance numerous areas of action we can discover the IoT eHealth Ecosystem [69], [70], [71], the loT Intelligent Transportation Ecosystem [72], the IoT Smart Home Ecosystem [73], [74], [75], and mosquito release Ecosystem etc as shown graphically in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir

Nisar²,

Raja

et al. 2021

IEEE Access

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The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology's use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito's density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10 -06 to 10 -09 . The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil's inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.INDEX TERMS Mosquito release ecosystem, IoT, SDN, artificial neural networks, heuristic algorithm, Adams's method.

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Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols

Cited by 20 publications

References 10 publications

Performance Analysis of AODV and OLSR protocol in Mobile Ad Hoc Network using NS3

Performance Analysis of AODV and OLSR protocol in Mobile Ad Hoc Network using NS3

Routing Communication Inside Ad Hoc Drones Network

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

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