Bio-Inspired Multi-Objective Algorithms Applied on the Optimisation of the AODV's Routing Recovery Mechanism

Santana, Clodomir, Macedo, Mariana, Alves, Emilly, Guerreiro, Marcio T, Siqueira, Hugo Valadares, Gokhale, Anuradha and Bastos-Filho, Carmelo (2023) Bio-Inspired Multi-Objective Algorithms Applied on the Optimisation of the AODV's Routing Recovery Mechanism. IEEE Access, 11. pp. 116480-116496. ISSN 2169-3536

Abstract

Advances in electronic systems, wireless communication protocols, and intelligent devices allowed the development of networks of mobile devices such as cars, drones, and robots. The field of mobile ad hoc networks (MANETs) comprises networks where the mobility of the devices is one of the fundamental elements that characterise these networks. However, the node’s mobility leads to constant changes in the network’s topology, representing a challenge to routing protocols designed for MANETs. Although there is effort from researchers to tackle the intricacies of routing protocols in MANETs, there is still room for improvement as new applications with challenging specifications continue to arise. This research enriches the existing theoretical perspective by presenting an innovative method for optimising the routing performance of the ad hoc on-demand distance vector (AODV) protocol. Grounded on multi-objective metaheuristics, we aim to improve AODV’s routing recovery performance concerning routing delay, energy consumption, packet loss ratio, and route load metrics. To gauge the quality of our contribution, we compare its performance to the standard AODV, a mono-objective optimised AODV, and four other well-known routing protocols with different routing approaches. The results indicate that the proposed solution was superior to the original AODV with average improvements of 56.0%, 59.3%, 48.1% and 0.7% on route load, routing delay, packet loss ratio and energy consumption, respectively. It also presented competitive results compared to other routing protocols.

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Official URL:	https://ieeexplore.ieee.org/document/10274089
Item Type:	Article
Disciplines:	Computing, Maths, Engineering and Natural Sciences > Data Science & AI
Depositing User:	Mariana Macedo
Date Deposited:	10 Jul 2024 08:35
Last Modified:	25 Oct 2024 14:14
URI:	https://nul.repository.guildhe.ac.uk/id/eprint/2045

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