Self-Organized Routing for Wireless Microsensor Networks (original) (raw)

2005, IEEE transactions on systems, man, and cybernetics

In this paper, we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless microsensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus, effective network-routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability, and the dynamic nature of such networks (given that sensors may fail, move, or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach. Index Terms-Adaptive self-organized routing, distributed systems, mechanism design, sensor network. I. INTRODUCTION W IRELESS microsensor networks provide an efficient solution to the problem of performing autonomous environmental monitoring. Large numbers of small, low-cost battery-powered sensors can be scattered randomly over a large area, where they will automatically sense and record local environmental conditions. The sensors then use low-power wireless transceivers to transmit their recorded data to a receiver or sink, where it is logged, acted upon or transmitted onwards. When making such networks operational, a key question is how to effectively manage resources such as battery life and communication bandwidth, given the dynamic nature of the system and the limited knowledge of the network topology. In such cases, the use of a central control regime becomes undesirable and thus there is much interest in investigating the use of distributed control methodologies in these networks [9],