Genetic Programming in Wireless Sensor Networks
Lecture Notes in Computer Science
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 series of variations to evolutionary computing paradigms such as Genetic Programming to enable their operation within the wireless sensor network. The ability to
... ute evolutionary algorithms within the WSN has innumerable advantages including, intelligent-sensing, resource optimized communication strategies, intelligent-routing protocol design, novelty detection, etc to name a few. In this paper we first discuss an evolutionary computing algorithm that operates within a distributed wireless sensor network. Such algorithms include continuous evolutionary computing. Continuous evolutionary computing extends the concept of an asynchronous evolutionary cycle where each individual resides and communicates with its immediate neighbors in an asynchronous time-step and exchanges genetic material. We then describe the adaptations required to develop practicable implementations of evolutionary computing algorithms to effectively work 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 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 a variety of problem sets and describe the results.