Article

Entropy-based sensor selection heuristic for target localization

Authors:

Hanbiao Wang,

Kung Yao,

Greg Pottie,

Deborah EstrinAuthors Info & Claims

IPSN '04: Proceedings of the 3rd international symposium on Information processing in sensor networks

Pages 36 - 45

https://doi.org/10.1145/984622.984628

Published: 26 April 2004 Publication History

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Abstract

We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.

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Index Terms

Entropy-based sensor selection heuristic for target localization
1. Mathematics of computing
  1. Information theory
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Reviews

Reviewer: Harry Skianis

Although in our personal lives we try to create order, it is in science that we have the chance to look for maximum disorder. This disorder is better expressed in science, given the information theoretic term of a system's entropy. The term is widely used, in a multitude of diverse disciplines, providing valuable insight on the problems studied. In this particular work, the authors present a novel entropy-based sensor selection heuristic for localization. Considering a given target location, and candidate sensors, the proposed heuristic is able to choose the sensor that reduces the entropy of the target location distribution the most. When it comes to other approaches, it appears that the proposed solution is simple and accurate enough, and worth further consideration. The paper is a joy for the mind to read; it presents ideas in a progressive, well-paced manner, and supports them, when necessary, with a clear understanding of the field. The "Future Work" section is food for thought. Online Computing Reviews Service

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Information & Contributors

Information

Published In

IPSN '04: Proceedings of the 3rd international symposium on Information processing in sensor networks

April 2004

464 pages

ISBN:1581138466

DOI:10.1145/984622

Conference Chairs:
Kannan Ramchandran
University of California, Berkeley
,
Janos Sztipanovits
Vanderbilt University
,
Program Chairs:
Jennifer Hou
University of Illinois at Urbana Champaign
,
Thrasyvoulos Pappas
Northwestern University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2004

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IPSN04

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SIGBED

IPSN04: Third International Symposium on Information Processing in Sensor Networks 2004

April 26 - 27, 2004

California, Berkeley, USA

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Overall Acceptance Rate 143 of 593 submissions, 24%

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https://doi.org/10.3390/buildings14123930
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https://doi.org/10.23919/ACC60939.2024.10644304
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