Bio-Inspired Routing Algorithms Survey for Vehicular Ad Hoc Networks

• Published in: IEEE Communications surveys and tutorials Vol. 17; no. 2; pp. 843 - 867
• Main Authors: Bitam, Salim, Mellouk, Abdelhamid, Zeadally, Sherali
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2015
• Subjects: Bio-inspired Algorithm; Reinforcement Learning; Routing; Routing Optimization; Routing protocols; Safety; Vehicles; Vehicular ad hoc networks; Vehicular Ad-hoc Network; Wireless communication; Vehicular ad hoc network; routing optimization; bio-inspired algorithm; reinforcement learning
• ISSN: 1553-877X
• DOI: 10.1109/COMST.2014.2371828

Vehicular Ad hoc NETworks (VANETs) play a key role in the design and development of Intelligent Transportation Systems (ITS) that aim to improve road safety and transportation productivity. VANETs cover vehicle-to-vehicle and vehicle-to-roadside communications. One of the most important challenges of this type of network is the timely and reliable dissemination of messages among vehicular nodes that enable drivers to take appropriate decisions to improve road safety. In the past decade, many routing protocols for VANETs that can support reliability and safety requirements have been proposed. These protocols suffer from several limitations, including complexity, lack of scalability to large scale networks, routing overheads, etc. To address these limitations, various bio-inspired approaches have been proposed to route packets among vehicular nodes in an optimized manner. We survey recent proposed bio-inspired routing algorithms for the VANET environment. In particular, we identify the key features, strengths, and weaknesses of these algorithms and compare them by using various criteria. Moreover, we propose a unified formal model of the bio-inspired multimodular approaches applied to VANET routing. We highlight main future research directions in this area.