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Wireless Sensor Networks (WSNs) is an emerging technology in severalapplication domains, ranging from urban surveillance to environmental andstructural monitoring. Computational Intelligence (CI) techniques areparticularly suitable for enhancing these systems. However, when embedding CIinto wireless sensors, severe hardware limitations must be taken into account.In this paper we investigate the possibility to perform an online, distributedoptimization process within a WSN. Such a system might be used, for example, toimplement advanced network features like distributed modelling, self-optimizingprotocols, and anomaly detection, to name a few. The proposed approach, calledDOWSN (Distributed Optimization for WSN) is an island-model infrastructure inwhich each node executes a simple, computationally cheap (both in terms of CPUand memory) optimization algorithm, and shares promising solutions with itsneighbors. We perform extensive tests of different DOWSN configurations on abenchmark made up of continuous optimization problems; we analyze the influenceof the network parameters (number of nodes, inter-node communication period andprobability of accepting incoming solutions) on the optimization performance.Finally, we profile energy and memory consumption of DOWSN to show theefficient usage of the limited hardware resources available on the sensornodes.

Distributed optimization in wireless sensor networks: an island-model framework