Self-organization of connectivity and geographical routing in large-scale sensor networks

A large-scale sensor network (LSSN) is formed when a very large number of sensor nodes with short-range communication capabilities are deployed randomly over an extended region. The random distribution of nodes in an LSSN leads to regions of varying density, which means that if all nodes have an identical transmission radius, the effective connectivity would vary over the system. This leads to inefficiency in energy usage (in regions of unnecessarily high connectivity) and the danger of partitioning (in regions of low node density). In this paper, we propose a technique for adapting a node's transmission radius based on a node's local information. Through localized coordination and self-organization, nodes try to attain fairly uniform connectivity in the system to aid in efficient data messaging in the system. We study the benefits of network adaptation by incorporating it into an adaptive geographical routing algorithm called corridor routing. We present simulation results showing significant improvement in performance over routing algorithms that do not use network adaptation. We also propose and study several scenarios for network adaptation in the presence of node failures, and explore the effect of parameter variation.