Exploring sensor networks using mobile agents

Abstract

Wireless sensor networks are often difficult to program and unable to adapt to a changing environment. Mobile agent middleware promises to address both concerns by providing higher-level programming abstractions and the ability to inject new agents into a preexisting network. The unique characteristics of wireless sensor networks like resource scarcity and emphasis on spatial locality require new algorithms for controlling agent behavior. This paper presents a procedure for one specific behavior: network exploration. Network exploration is needed by many tasks ranging from simple data collection to network health monitoring. Our proposed procedure uses a genetic algorithm to determine the number of agents and their itineraries, followed by techniques for in-network adaptation to unpredictable situations like node failure. This paper presents a genetic algorithm and its adaptation strategies. The procedure is evaluated using a wireless sensor network consisting of 25 Mica2 motes running Agilla, a mobile agent middleware for wireless sensor networks.