Biologically-Inspired Adaptive Data Aggregation for Multi-Modal Wireless Sensor Networks

This paper describes BiSNET (biologically-inspired architecture for sensor networks), which addresses several key issues in multi-modal wireless sensor networks such as autonomy, adaptability, self-healing and simplicity. Based on the observation that various biological systems have developed mechanisms to overcome these issues, BiSNET implements certain biological mechanisms such as energy exchange, pheromone emission, replication, and migration to design sensor network applications. This paper presents the biologically-inspired mechanisms in BiSNET, and evaluates their impacts on the issues described above. Simulation results show that BiSNET allows sensor nodes to autonomously adapt their duty cycle intervals for power efficiency and responsiveness of data transmission, to adaptively aggregate data from different types of sensor nodes, to collectively self-heal (i.e., detect and eliminate) false positive sensor data, and to be lightweight