A geographic routing algorithm based on Cat Swarm Optimization for vehicular ad-hoc networks

Kasana, Reena; Kumar, Sushil

doi:10.1109/spin.2017.8049921

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…Fitness function is derived to identify best fittest neighbor for packet forwarding. It is clearly visible that CORFA reaches to the stable value much earlier than PSOR 28 and CSO‐GR 29 . As we have used minimizing function so the smaller the fitness function value is, the more it is in favor of the routing requirements.…”

Section: Performance Evaluationmentioning

confidence: 97%

“…It is clearly visible that CORFA reaches to the stable value much earlier than PSOR 28 and CSO-GR. 29 As we have used minimizing function so the smaller the fitness function value is, the more it is in favor of the routing requirements. Time complexity of CORFA is calculated by counting the loops in the algorithm.…”

Section: Convergence Analysis With Bio-inspired Vanet Routing Protocolsmentioning

confidence: 99%

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Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

Ghosh

Misra

Chakraborty

2023

Trans Emerging Tel Tech

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QoS aware vehicular ad‐hoc network (VANET) routing protocols address the increasing demand for delay‐sensitive vehicular applications to establish intelligent transportation. Primary challenge with ad‐hoc nature of VANET is that the communication between vehicle‐to‐vehicle and vehicle‐to‐infrastructure is prone to link failure. Bio‐inspired algorithms offer robust solutions to secure VANET links. Accordingly in this article, novel canine olfactory route‐finding algorithm (CORFA) is proposed for VANET to achieve the best possible route with the minimum transmission of packets, which ensures enhanced QoS. The underlying architecture utilizes the exceptional ability of canines to evaluate and memorize the environment and pass the message to neighbors. It makes canines a great choice to model their behavior for VANET routing. RSUs are fundamental components of VANET that supply contents to the proceeding vehicles from their cache. As RSUs carry out information dissemination tasks efficiently, the already traversed and discovered routing paths in the recent past can be cached in RSU's storage and through back‐haul can be circulated to neighboring RSUs. Vehicles request the pre‐cached route and use them for data transmission. Thereby avoiding the route discovery and maintenance procedure reduces the routing overhead. In absence of a pre‐cached route, the fittest vehicle from each hop is selected as next forwarder for packet transmission. The proposed routing algorithm forms multiple routes during route discovery to handle link failure scenario. Traffic simulator, SUMO is used to generate the mobility model and integrated with MATLAB for analysing the performance of CORFA with related geographical and topological routing protocols that confirm the performance superiority of CORFA under several metrics such as packet delivery ratio, network latency, throughput, and overhead.

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Section: Performance Evaluationmentioning

confidence: 97%

Section: Convergence Analysis With Bio-inspired Vanet Routing Protocolsmentioning

confidence: 99%

Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

Ghosh

Misra

Chakraborty

2023

Trans Emerging Tel Tech

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show abstract

“…An initial component F 1 defines the Euclidean distance among moth and destination vehicle. It is applicable that a moth comprised of low distance to destination has been selected as next forwarding vehicle (Kasana & Kumar, 2017). This data is gained from the fact that a sender vehicle could accomplish geographic position of target vehicle ( x D , y D ) by querying position as well as neighbour is composed of corresponding location in Cooperative Awareness Messages (CAM).…”

Section: The Proposed Modelmentioning

confidence: 99%

Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities

Aljehane

Mansour

2021

Expert Systems

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Presently, smart city is designed to enhance the quality of life in city, fulfil the safety of the people, safe travelling, etc. Besides, big data has attracted significant attention among researchers in different fields as a large amount of data is being produced with diverse day-to-day applications. Besides, Vehicular adhoc network (VANET) is a kind of mobile adhoc network (MANET) that considers the vehicles as the nodes in a network. Since the VANET generates large amount of data, big data analytics can be used to gain meaningful understanding for improving the traffic management process such as planning, engineering, and operations. This paper designs a Big Data Analytics with Oppositional Moth Flame Optimization based Vehicular Routing Protocol for Future Smart Cities. The presented model maps the features of VANET with the attributes of the big data. In addition, oppositional moth flame optimization based vehicular routing (OMFOVR) technique is developed for VANET over the Hadoop Map Reduce standalone distributed framework. For validating the effectual performance of the proposed OMFOVR technique, a series of experiments were performed and the results are compared with the conventional NetBeans IDE platform. The experimental values showcased the betterment of the OMFOVR technique on the selection of routes over the compared methods.

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“…Kasana [19] proposed a new geographic routing protocol based on cat swarm optimization for the unique features of vehicle-mounted self-organizing networks (such as high mobility, low bandwidth and restricted mobility), with the purpose of finding the optimal effective strategy to select the next forwarding vehicle in a highly dynamic vehicle environment. A fitness function to optimize the impact of various parameters on the selection of the next forwarding vehicle was suggested.…”

Section: Related Workmentioning

confidence: 99%

QLGR: A Q-learning-based Geographic FANET Routing Algorithm Based on Multi-agent Reinforcement Learning

2021

KSII TIIS

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The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.

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A geographic routing algorithm based on Cat Swarm Optimization for vehicular ad-hoc networks

Cited by 15 publications

References 10 publications

Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities

QLGR: A Q-learning-based Geographic FANET Routing Algorithm Based on Multi-agent Reinforcement Learning

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