Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an obser- vation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologically-inspired adaptation mechanism, called MON- SOON. MONSOON is designed to support data collection applications, event detection applications and hybrid appli- cations. Each application is implemented as a decentralized group of software agents, analogous to a bee colony (appli- cation) consisting of bees (agents). Agents collect sensor data and /or detect an event (a significant change in sen- sor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding en- vironment conditions and adaptively invoking biologically- inspired behaviors such as pheromone emission, reproduc- tion and migration. Each agent has its own behavior pol- icy, 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. Sim- ulation results show that MONSOON allows agents (WSN applications) to simultaneously satisfy conflicting objec- tives by adapting to dynamics of physical operational envi- ronments and network environments (e.g., sensor readings and node /link failures) through evolution.