Analytical modeling of swarm intelligence in wireless sensor networks through Markovian agents

Wireless Sensor Networks (WSN) consist of a large number of tiny sensor nodes that are usually randomly distributed over a geographical region. In order to reduce power consumption, battery operated sensors undergo cycles of sleeping - active periods; furthermore, sensors may be located in hostile environments increasing their attitude to failure. As a result, the topology of the WSN may be varying in time in an unpredictable manner. For this reason multi-hop routing algorithms to carry messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows to emerge a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper is aimed to show that the recently proposed modeling technique, known as Markovian Agents, is suited to implement swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support the previous claim.