Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

Satsangi, Yash, Whiteson, Shimon and Oliehoek, Frans A ORCID: 0000-0003-4372-5055 (2015) Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection. In: 2015.

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Abstract

<jats:p> 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 real-world dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing methods but incurs only a fraction of the computational cost, leading to much better scalability in the number of cameras. </jats:p>

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Planning and scheduling, Sensor selection, POMDPs
Subjects:	?? QA75 ??
Depositing User:	Symplectic Admin
Date Deposited:	20 Oct 2015 07:49
Last Modified:	24 Mar 2024 19:37
DOI:	10.1609/aaai.v29i1.9666
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2032381