  Wireless Sensor Networks (WSNs) is an emerging technology in several
application domains, ranging from urban surveillance to environmental and
structural monitoring. Computational Intelligence (CI) techniques are
particularly suitable for enhancing these systems. However, when embedding CI
into wireless sensors, severe hardware limitations must be taken into account.
In this paper we investigate the possibility to perform an online, distributed
optimization process within a WSN. Such a system might be used, for example, to
implement advanced network features like distributed modelling, self-optimizing
protocols, and anomaly detection, to name a few. The proposed approach, called
DOWSN (Distributed Optimization for WSN) is an island-model infrastructure in
which each node executes a simple, computationally cheap (both in terms of CPU
and memory) optimization algorithm, and shares promising solutions with its
neighbors. We perform extensive tests of different DOWSN configurations on a
benchmark made up of continuous optimization problems; we analyze the influence
of the network parameters (number of nodes, inter-node communication period and
probability of accepting incoming solutions) on the optimization performance.
Finally, we profile energy and memory consumption of DOWSN to show the
efficient usage of the limited hardware resources available on the sensor
nodes.
