The Automatic Evolution of Distributed Controllers to Configure Sensor Network Operation

Tuning the parameters that control the operation of a wireless sensor network, such as sampling rate, is not a simple task. This is partly due to the distributed nature of the problem, but is also a result of the time-varying dynamics that a network experiences. Inspired by the way in which cells alter their behaviour in response to diffused protein concentrations, an abstract representation, termed a discrete gene regulatory network (dGRN), is introduced. Each node runs an identical dGRN controller which controls node activity and interaction. The controllers are authored automatically using an evolutionary algorithm. The communication that occurs between nodes is neither specified nor designed, but emerges naturally. As a particular example, we illustrate that our approach can generate effective strategies for nodes to cooperatively track a moving target. The obtained strategies vary according to the user's accuracy requirements and the speed of the target, and are similar to those which would be expected from a network engineer. We also present results from our proof-of-concept dGRN implementation on T-Mote Sky nodes. Our approach takes high-level user application requirements and from these, automatically generates distributed parameter tuning algorithms. The dGRN framework thus greatly reduces the amount of effort involved in adjusting a sensor network's operation.