Genetic Programming in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are medium scale manifestations of a paintable or amorphous computing paradigm. WSNs are becoming increasingly important as they attain greater deployment. New techniques for evolutionary computing (EC) are needed to address these new computing models. This paper describes a novel effort to develop a variation of traditional parallel evolutionary computing models to enable their use in the wireless sensor network. The ability to compute evolutionary algorithms within the WSN has innumerable advantages including intelligent-sensing, resource-optimized communication strategies, intelligent-routing protocol design, novelty detection, etc. In this paper we develop a parallel evolutionary algorithm suitable for use in a WSN. We then describe the adaptations required to develop practicable implementations to effectively operate in resource constrained environments such as WSNs. Several adaptations including a novel representation scheme, an approximate fitness computation method and a sufficient statistics based data reduction technique. These adaptations lead to the development of a GP implementation that is usable on the low-power, small footprint architectures typical to wireless sensor motes. We demonstrate the utility of our formulations and validate the proposed ideas using the algorithm to compute symbolic regression problems.