Evolutionary Constraint-based Multiobjective Adaptation for Self-Organizing Wireless Sensor Networks

Wireless sensor applications (WSNs) are often required to simultaneously satisfy con icting operational objectives (e.g., latency and power consumption). Based on an observation that various biological systems have developed the mecha- nisms 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 sen- sor data on individual nodes, and carry the data to base stations. They perform this data collection functionality by autonomously sensing their local and surrounding environ- ment conditions and adaptively invoking biological behav- iors such as pheromone emission, replication, reproduction and migration. Each agent has its own behavior policy, as a gene, which defnes how to invoke its behaviors. MON- SOON allows agents to evolve their behavior policies (i.e., genes) and simultaneously adapt to con icting objectives. In addition to consider multiple objectives equally, MON- SOON also allows agents to evolve in a constraint-based (or intentionally-biased) manner. A constraint is defned 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 con icting ob- jectives in a self-organizing manner.