Sensor Networks: from Dependence Analysis Via Matroid Bases to Online Synthesis

TL;DR

This paper introduces a theoretical framework for sensor dependence using polymatroid structures, proposing algorithms for sensor selection and fusion that improve reliability without prior performance data.

Contribution

It formalizes sensor dependence with polymatroid measures and develops new algorithms for sensor selection and fusion that are efficient and data-agnostic.

Findings

Polymatroid structure models sensor dependence effectively.

Proposed algorithms outperform traditional methods in sensor selection.

Fusion algorithm converges without prior sensor performance data.

Abstract

Consider the two related problems of sensor selection and sensor fusion. In the first, given a set of sensors, one wishes to identify a subset of the sensors, which while small in size, captures the essence of the data gathered by the sensors. In the second, one wishes to construct a fused sensor, which utilizes the data from the sensors (possibly after discarding dependent ones) in order to create a single sensor which is more reliable than each of the individual ones. In this work, we rigorously define the dependence among sensors in terms of joint empirical measures and incremental parsing. We show that these measures adhere to a polymatroid structure, which in turn facilitates the application of efficient algorithms for sensor selection. We suggest both a random and a greedy algorithm for sensor selection. Given an independent set, we then turn to the fusion problem, and suggest a novel variant of the exponential weighting algorithm. In the suggested algorithm, one competes against an augmented set of sensors, which allows it to converge to the best fused sensor in a family of sensors, without having any prior data on the sensors' performance.