Evolutionary constraint-based multiobjective adaptation for self-organizing 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. With MONSOON, each application is designed as a decentralized group of software agents. This is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data on individual nodes, and carry the data to base stations. They perform this data collection functionality by autonomously sensing their local and surrounding environment conditions and adaptively invoking biological behaviors such as pheromone emission, replication, 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 (i.e., genes) and simultaneously adapt to conflicting objectives. In addition to consider multiple objectives equally, MONSOON also allows agents to evolve in a constraint-based (or intentionally-biased) manner. A constraint is defined as an upper or lower bound for each objective. Simulation results show that MONSOON allows agents (WSN applications) to adapt to dynamics of the network (e.g., node/link failures) through evolution and simultaneously satisfy conflicting objectives in a self-organizing manner.