Path planning based QoS routing in VANET

Shaikh, Farheen Iqbal; Hingoliwala, H. A.

doi:10.1109/bid.2017.8336570

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…In order to maximize the capacity of transmission and keep the QoS at an application level in terms of safety, there was a scheme of optimization that was introduced by Shaikh and Hingoliwala [10] to adjust the parameters of transmission, and these are employed in the real test beds.…”

Section: Related Workmentioning

confidence: 99%

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Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Pagadala

Kumari

Thakur

et al. 2023

Computational Intelligence and Neuroscience

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Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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Section: Related Workmentioning

confidence: 99%

“…., K. Tis fast method of simulated reannealing had been presented by Ingber in the year 1989 by Ingber. It has a cooling schedule which is as follows (10):…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Pagadala

Kumari

Thakur

et al. 2023

Computational Intelligence and Neuroscience

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“…The approach works only for low mobility of nodes. Authors in [18] proposed a novel path planning algorithm for reliable and efficient routing in VANET. Congestion is manager by distributing load over alternate paths and splitting the larger packets to small packets.…”

Section: Related Workmentioning

confidence: 99%

“…From the predicted RTT, the value of N is calculated as: (18) Where, k is the total number of messages that can be forwarded when RTT is at a best lower bound value. This value is configured by network administrator.…”

Section: Service Differentiation and Flow Controlmentioning

confidence: 99%

Improving QoS in Internet of Vehicles Integrating Swarm Intelligence Guided Topology Adaptive Routing and Service Differentiated Flow Control

Kayarga¹,

Kumar²

2023

IJACSA

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Internet of Vehicles (IoV) is an evolution of vehicular adhoc network with concepts of internet of things (IOT). Each vehicle in IOV is an intelligent object with various capabilities like sensors, computation, storage, control etc. Vehicles can connect to any other entity in the network using various services like DSRC, C2C-CC etc. Ensuring QoS for vehicle to everything (V2X) communication is a major challenge in IoV. This work applies an integration of hybrid metaheuristics guided routing and service differentiated flow control to ensure QoS in Internet of Vehicles. Clustering based network topology is adopted with clustering based on hybrid metaheuristics integrating particle swarm optimization with firefly algorithm. Over the established clusters routing decision is done using swarm intelligence. Packet flows in the network are service differentiated and flow control is done at cluster heads to reduce the congestion in the network. High congestion in routes is mitigated with back up path satisfying the QoS constraints. Due to optimization in clustering, routing and data forwarding process, the proposed solution is able to achieve higher QoS. Through simulation analysis, the proposed solution is able to achieve 2% higher packet delivery ratio and 9.67% lower end to end packet latency compared to existing works.

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“…(ii) Route Maintain: Route maintain involves the best path to find and avoid to interruption during transmission and it indentifies the whole connection is being able to transmitting or not [13].…”

Section: An Overview Of Proposed Approach Of Dsr and Psoamentioning

confidence: 99%

Enhanced Vehicular AD HOC Network Protocol to Improve Quality of Service in Vehicle to Vehicle Communication

2019

IJITEE

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The vehicular ad-hoc networks (VANETs) are specific type or a sub form of Mobile ad hoc networks (MANETs). However the main problem which is related to this network is the Quality of Service (QoS) which mainly occurs due to rapid change topology nature in the network and lack of stability of communication. Consequently, some of the challenges that researcher focus on routing protocols for VANET. The problem which is faced by this network with these protocols is the dynamic environment in their route instability. This paper approaches the combination of Dynamic Source Routing protocol (DSR) and Particle Swarm Optimization Algorithm (PSO) to solve the problems of Routing protocols which help to improve the Quality of service (QoS) in the network. The approach which is introduced in this paper is to make use for making the better Quality of Service (QoS) in the VANET. The simulation results in MATLAB exactly predict the overall performances regarding the proposed work in terms of the packet drop ratio, transmission delay, channel utilization, Throughput and Energy consumption under varying conditions

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Path planning based QoS routing in VANET

Cited by 5 publications

References 9 publications

Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Improving QoS in Internet of Vehicles Integrating Swarm Intelligence Guided Topology Adaptive Routing and Service Differentiated Flow Control

Enhanced Vehicular AD HOC Network Protocol to Improve Quality of Service in Vehicle to Vehicle Communication

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