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

TL;DR

This paper introduces DOWSN, a distributed optimization framework for wireless sensor networks that enables online, collaborative problem-solving while respecting hardware constraints, demonstrated through extensive benchmarking and resource profiling.

Contribution

It presents a novel island-model infrastructure allowing simple, low-resource optimization algorithms to operate collaboratively in WSNs, enhancing their capabilities.

Findings

DOWSN effectively solves continuous optimization problems in WSNs.

Network parameters significantly influence optimization performance.

DOWSN uses minimal energy and memory resources, suitable for sensor hardware.

Abstract

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.