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Analysis of Deterministic Tracking of Multiple Objects Using a Binary Sensor Network

Published: 01 August 2011 Publication History

Abstract

Let consider a set of anonymous moving objects to be tracked in a binary sensor network. This article studies the problem of associating deterministically a track revealed by the sensor network with the trajectory of an unique anonymous object, namely the multiple object tracking and identification (MOTI) problem. In our model, the network is represented by a sparse connected graph where each vertex represents a binary sensor and there is an edge between two sensors if an object can pass from one sensed region to another one without activating any other sensor. The difficulty of MOTI lies in the fact that the trajectories of two or more objects can be so close that the corresponding tracks on the sensor network can no longer be distinguished (track merging), thus confusing the deterministic association between an object trajectory and a track.
The article presents several results. We first show that MOTI cannot be solved on a general graph of ideal binary sensors even by an omniscient external observer if all the objects can freely move on the graph. Then we describe restrictions that can be imposed a priori either on the graph, on the object movements, or on both, to make the MOTI problem always solvable. In the absence of an omniscient observer, we show how our results can lead to the definition of distributed algorithms that are able to detect when the system is in a state where MOTI becomes unsolvable.

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    Published In

    cover image ACM Transactions on Sensor Networks
    ACM Transactions on Sensor Networks  Volume 8, Issue 1
    August 2011
    247 pages
    ISSN:1550-4859
    EISSN:1550-4867
    DOI:10.1145/1993042
    Issue’s Table of Contents
    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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    Publication History

    Published: 01 August 2011
    Accepted: 01 November 2010
    Revised: 01 August 2010
    Received: 01 December 2008
    Published in TOSN Volume 8, Issue 1

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    Author Tags

    1. Binary sensor network
    2. distributed algorithm
    3. impossibility and characterization
    4. passive trajectory tracking

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    • (2018)Implementing a semi-causal domain-specific language for context detection over binary sensorsProceedings of the 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3278122.3278134(66-78)Online publication date: 5-Nov-2018
    • (2016)Learning Mixtures of Markov Chains from Aggregate Data with Structural ConstraintsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.252242628:6(1518-1531)Online publication date: 1-Jun-2016
    • (2016)Multiple Transmitter Localization and Whitespace Identification Using Randomly Deployed Binary SensorsIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2016.26340002:4(358-369)Online publication date: Dec-2016
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    • (2015)On Target Counting by Sequential Snapshots of Binary Proximity SensorsWireless Sensor Networks10.1007/978-3-319-15582-1_2(19-34)Online publication date: 2015
    • (2014)Multiple target counting and tracking using binary proximity sensorsProceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing10.1145/2632951.2632959(397-406)Online publication date: 11-Aug-2014
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    • (2013)Integer programming based approach for multiple-targets trajectory identification in WSNs2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)10.1109/ICNSC.2013.6548720(106-111)Online publication date: Apr-2013
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