Adaptive stochastic sensor scheduling for multi-channel radio environment mapping

This paper addresses the problem of scheduling a distributed set of RF sensors for the creation of a multichannel Radio Environment Map (REM). We propose a method for tracking the spatial distribution of the activity patterns on each channel via Kaiman filtering. The activity patterns on each channel are then used to inform schedule creation by balancing the achievable expected uncertainty across all channels by using the concepts of Permissible Consecutive Observation Loss (PCOL) and Least Consecutive Observation (LCO). For a stationary wireless sensor network, where each sensor can scan one channel at a time, we create an adaptive stochastic channel sensing order that balances the spatial uncertainty of the estimate of the REM for each of the channels of interest. Herein, the algorithm's effectiveness is demonstrated through simulation. It will also be demonstrated on actual spectrum measurements under the DARPA RadioMap, for which this algorithm was developed.