Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment

Vehicular sensor networks (VSN) enable brand new and promising sensing applications, such as traffic reporting, relief to environmental monitoring, and distributed surveillance. We have designed and implemented MobEyes, a middleware solution that supports VSN-based proactive urban monitoring applications, where the agents (e.g. police cars) harvest meta-data from regular VSN-enabled vehicles. In a typical urban sensing operation, multiple agents often collaborate in harvesting relevant data, processing them, and searching for the key information. Thus it is critical to design a mechanism to effectively coordinate the operations of multiple agents and guide them to seek most productive fields in a distributed manner. In this paper, we present a novel data harvesting algorithm in an urban sensing environment that has been designed based on biological inspirations such as foraging, stigmergy, and Lévy flight. The proposed algorithm enables the agents to move to “information patches ” where new information concentration is high. Also when the agent encounters some other agents in the same region it moves to another region so that their work is not duplicated. We validate the performance of our proposed data harvesting scheme via extensive simulations. From this study, we show datataxis effectively balances the movement of agents and distributes them, and performs better than other commonly used distributed harvesting strategies. We also report that our algorithm works well without requiring elaborate tuning of the operation parameters.