Multi‐objective auto‐regressive whale optimisation for traffic‐aware routing in urban VANET

Rewadkar, D. N.; Doye, Dharmpal D.

doi:10.1049/iet-ifs.2018.0002

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…Moreover, in [17], Whale Optimization Algorithm (WOA) originated from the observation of humpback whales hunting behavior. In [18], the authors adopt WOA to solve multi-objective optimization to present a traffic-aware routing protocol. In light of the aforementioned work, swarm intelligence can effectively solve complex optimization problems in networks, which is a means to realize intelligent network management.…”

Section: A Swarm Intelligencementioning

confidence: 99%

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Zhao

Hawbani

et al. 2021

IEEE Trans. Wireless Commun.

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To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

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Section: A Swarm Intelligencementioning

confidence: 99%

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Zhao

Hawbani

et al. 2021

IEEE Trans. Wireless Commun.

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“…In [12], the authors presented a traffic-aware routing protocol in VANET by introducing multi-objective auto-regressive whale optimization (ARWO) algorithm. ARWO selects the best path from multiple paths by considering multiple objectives such as end-to-end delay, link lifetime, and node distance in the fitness function.…”

Section: Topological Approachmentioning

confidence: 99%

Intelligent Transport System Using Time Delay-Based Multipath Routing Protocol for Vehicular Ad Hoc Networks

Ghaemi

El-Ocla

Yadav

et al. 2021

Sensors

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During the last decade, the research on Intelligent Transportation System (ITS) has improved exponentially in real-life scenarios to provide optimized transport network performance. It is a matter of importance that alert messages are delivered promptly to prevent vehicular traffic problems. The fact is an ITS system per se could be a part of a vehicular ad hoc network (VANET) which is an extension of a wireless network. In all sorts of wireless ad hoc networks, the network topology is subjected to change due to the mobility of network nodes; therefore, an existing explored route between two nodes could be demolished in a minor fraction of time. When it comes to the VANETs, the topology likely changes due to the high velocity of nodes. On the other hand, time is a crucial factor playing an important role in message handling between the network’s nodes. In this paper, we propose Time delay-based Multipath Routing (TMR) protocol that effectively identifies an optimized path for packet delivery to the destination vehicle with a minimal time delay. Our algorithm gives a higher priority to alert messages compared to normal messages. It also selects the routes with the short round-trip time (RTT) within the RTT threshold. As a result, our algorithm would realize two goals. Firstly, it would speed up the data transmission rate and deliver data packets, particularly warning messages, to the destination vehicle promptly and therefore avoid vehicular problems such as car accidents. Secondly, the TMR algorithm reduces the data traffic load, particularly of the normal messages, to alleviate the pressure on the network and therefore avoids network congestion and data collisions. This, in turn, lessens the packets’ retransmissions. To demonstrate the effectiveness of the proposed protocol, the TMR has been compared with the other protocols such as AOMDV, FF-AOMDV, EGSR, QMR, and ISR. Simulation results demonstrate that our proposed protocol proves its excellent performance compared to other protocols.

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“…Data congestion control is utilized to improve network capacities, expanding the dependability of the VANET by diminishing packet losses and correspondence delays in Jamal Toutouh and Alba 23 . Rewadkar and Doye 24 discussed VANET analysis by the execution of Auto‐Regressive Whale Optimization (ARWO) protocol is contrasted with four existing procedures, that is, stable routing protocol, fractional glowworm swarm optimization, glowworm swarm optimization, and WOA, using the metrics, delay, distance, traffic density, and throughput. Though different algorithms have been developed for VANET, there is still a room to further enhance the clustering efficiency and energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

An enhanced whale optimization algorithm for vehicular communication networks

Kittusamy¹,

Elhoseny

Shankar

2019

Int J Communication

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In the field of vehicle communication networks, vehicular ad hoc network (VANET) is established with the help of road side unit (RSU). Each vehicle in the traffic system is managed by RSU with certain mobility factor, but still, there are some issues in the mobility management. In order to avoid such expensive RSUs and to enhance the mobility management of VANET system, the paper proposes a novel algorithm for the process of organizing a cluster structure and cluster head (CH) election suitable for VANETs. The proposed adaptive weighted clustering protocol (AWCP) groups the random nodes, and then the optimal CH is attained by the optimization of network parameters. For the purpose of optimization, an innovative algorithm called the enhanced whale optimization algorithm (EWOA) is introduced. For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The distance between the trusted vehicle node and RSU is analyzed by the proposed AWCP-EWOA model. The results demonstrate the proposed AWCP-EWOA model outperforms compared with AWCP and AWCP-Whale Algorithm (WA) protocols in terms of clustering efficiency and its mobility enhancement.

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Multi‐objective auto‐regressive whale optimisation for traffic‐aware routing in urban VANET

Cited by 12 publications

References 38 publications

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Intelligent Transport System Using Time Delay-Based Multipath Routing Protocol for Vehicular Ad Hoc Networks

An enhanced whale optimization algorithm for vehicular communication networks

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