Towards Learning-based Sensor Management
The management of wireless sensor networks in the presence of multiple constraints is an open problem in systems research. Existing methods perform well when optimized for a single parameter (such as energy, delay, network bandwidth). However, we might want to establish trade-offs on the fly, and optimize the information flow/exchange. This position paper shall serve as a preliminary proof-of-concept that techniques and algorithms from the machine learning and data mining domains can be applied to network data to learn relevant information about the routing behavior of individual nodes and the overall state of the network. We describe two simple examples which demonstrate the application of existing algorithms and analyze the results to illustrate their usefulness.