Book Description

Ad hoc wireless multi-hop networks (AHWMNs) are communication networks that consist entirely of wireless nodes, placed together in an ad hoc manner, i.e. with minimal prior planning. All nodes have routing capabilities, and forward data packets for other nodes in multi-hop fashion. Nodes can enter or leave the network at any time, and may be mobile, so that the network topology continuously experiences alterations during deployment. AHWMNs pose substantially different challenges to networking protocols than more traditional wired networks. These challenges arise from the dynamic and unplanned nature of these networks, from the inherent unreliability of wireless communication, from the limited resources available in terms of bandwidth, processing capacity, etc., and from the possibly large scale of these networks. Due to these different challenges, new algorithms are needed at all layers of the network protocol stack. We investigate the issue of adaptive routing in AHWMNs, using ideas from artificial intelligence (AI). Our main source of inspiration is the field of Ant Colony Optimization (ACO). This is a branch of AI that takes its inspiration from the behavior of ants in nature. ACO has been applied to a wide range of different problems, often giving state-of-the-art results. The application of ACO to the problem of routing in AHWMNs is interesting because ACO algorithms tend to provide properties such as adaptivity and robustness, which are needed to deal with the challenges present in AHWMNs. On the other hand, the field of AHWMNs forms an interesting new application domain in which the ideas of ACO can be tested and improved. In particular, we investigate the combination of ACO mechanisms with other techniques from AI to get a powerful algorithm for the problem at hand. We present the AntHocNet routing algorithm, which combines ideas from ACO routing with techniques from dynamic programming and other mechanisms taken from more traditional routing algorithms. The.