Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

A key challenge in the design of multi-sensor systems is the efficient allocation of scarce resources such as bandwidth, CPU cycles, and energy, leading to the dynamic sensor selection problem in which a subset of the available sensors must be selected at each timestep. While partially observable Markov decision processes (POMDPs) provide a natural decision-theoretic model for this problem, the computational cost of POMDP planning grows exponentially in the number of sensors, making it feasible only for small problems. We propose a new POMDP planning method that uses greedy maximization to greatly improve scalability in the number of sensors. We show that, under certain conditions, the value function of a dynamic sensor selection POMDP is submodular and use this result to bound the error introduced by performing greedy maximization. Experimental results on a realworld dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing metho...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek
• Keywords: AAAI 2015, Intelligent Agents