Dynamic Sensor Scheduling for Spatio-Temporal Monitoring of Water Bodies

We present a formulation for dynamic sensor scheduling for environment monitoring applications using multi-agent systems. The domain of interest is represented by a parameterized state-space model which captures spatiotemporal correlations of the environment. The parameters of the model are adapted online as new measurements become available. We introduce an improved greedy approach that simultaneously determines the optimal sensing locations and assigns agents to these locations; and this approach minimizes travel costs incurred by the mobile agents while satisfying the dynamic reachability constraints. We provide performance guarantees for the algorithms under certain conditions and derive their polynomial-time computational complexities. We demonstrate the proposed approach for bio-mass monitoring applications in large water bodies.