Applications of Evolutionary Computation

Wireless Sensor Networks (WSNs) are widely adopted for applications ranging from surveillance to environmental monitoring. While powerful and relatively inexpensive, they are subject to behavioural faults whichmake them unreliable. Due to the complex interactions between network nodes, it is difficult to uncover faults in aWSN by resorting to formal techniques for verification and analysis, or to testing. This paper proposes an evolutionary framework to detect anomalous behaviour related to energy consumption in WSN routing protocols. Given a collection protocol, the framework creates candidate topologies and evaluates them through simulation on the basis of metrics measuring the radio activity on nodes. Experimental results using the standard Collection Tree Protocol show that the proposed approach is able to unveil topologies plagued by excessive energy depletion over one or more nodes, and thus could be used as an offline debugging tool to understand and correct the issues before network deployment and during the development of new protocols.