期刊
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
卷 17, 期 2, 页码 843-867出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/COMST.2014.2371828
关键词
Vehicular ad hoc network; bio-inspired algorithm; routing optimization; reinforcement learning
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据