Confidence-interval based sensing quality evaluation for mobile sensor networks

Sensing quality evaluation is fundamentally important for mobile sensor network. However, due to the inherent sensing uncertainty in mobile sensor networks and the unavailability of the ground truth, achieving effective and accurate evaluation on sensing quality is extremely challenging. In this paper, we propose a confidence-interval based sensing quality evaluation method, leveraging the Fisher information and the asymptotic normality property of maximum likelihood estimation. The simulation results demonstrate our method can evaluate the sensing quality more reasonably and accurately than the status quo method. Further, our evaluation asymptotically approaches to the ground truth with the stepwise movements of sensors.