Practical sensor management for an energy-limited detection system

Real-time detection of intermittent events requires continual monitoring and processing of sensor data. A battery-powered device that supports multiple sensing modalities and processing algorithms has the potential to save energy by using expensive sensors and algorithms only when the event of interest is most likely to occur. To develop a policy for sensing and processing management, we adopt maximum sequential information gain as an objective criterion for such energy-limited systems, which can be solved via dynamic programming. For binary hypothesis testing with two sensing options, the optimal management policy is a simple two-threshold test on the posterior belief. Detection of bird presence/absence in a wildlife monitoring application shows up to a 37% reduction in error rate over standard constant-duty-cycle sensing.