MONSOON: A coevolutionary multiobjective adaptation framework for dynamic wireless sensor networks

Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an observation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologically-inspired adaptation mechanism, called MONSOON. MONSOON is designed to support data collection applications, event detection applications and hybrid applications. Each application is implemented as a decentralized group of software agents, analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data and/or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding environment conditions and adaptively invoking biologically-inspired behaviors such as pheromone emission, reproduction and migration. Each agent has its own behavior policy, as a gene, which defines how to invoke its behaviors. MONSOON allows agents to evolve their behavior policies (genes) and adapt their operations to given objectives. Simulation results show that MONSOON allows agents (WSN applications) to simultaneously satisfy conflicting objectives by adapting to dynamics of physical operational environments and network environments (e.g., sensor readings and node/link failures) through evolution.