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Target counting with Presburger constraints and its application in sensor networks

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Target counting with Presburger constraints and its application in sensor networks. / Linker, Sven; Sevegnani, Michele.
In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 475, No. 2231, 20190278, 29.11.2019.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Linker, S & Sevegnani, M 2019, 'Target counting with Presburger constraints and its application in sensor networks', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 475, no. 2231, 20190278. https://doi.org/10.1098/rspa.2019.0278

APA

Linker, S., & Sevegnani, M. (2019). Target counting with Presburger constraints and its application in sensor networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2231), Article 20190278. https://doi.org/10.1098/rspa.2019.0278

Vancouver

Linker S, Sevegnani M. Target counting with Presburger constraints and its application in sensor networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2019 Nov 29;475(2231):20190278. Epub 2019 Nov 6. doi: 10.1098/rspa.2019.0278

Author

Linker, Sven ; Sevegnani, Michele. / Target counting with Presburger constraints and its application in sensor networks. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2019 ; Vol. 475, No. 2231.

Bibtex

@article{35cc00b1a14a4537897be2c09d08b9f5,
title = "Target counting with Presburger constraints and its application in sensor networks",
abstract = "One of the applications popularized by the emergence of wireless sensor networks is target counting: the computational task of determining the total number of targets located in an area by aggregating the individual counts of each sensor. The complexity of this task lies in the fact that sensing ranges may overlap, and therefore, targets may be overcounted as, in this setting, they are assumed to be indistinguishable from each other. In the literature, this problem has been proven to be unsolvable, hence the existence of several estimation algorithms. However, the main limitation currently affecting these algorithms is that no assurance regarding the precision of a solution can be given. We present a novel algorithm for target counting based on exhaustive enumeration of target distributions using linear Presburger constraints. We improve on current approaches since the estimated counts obtained by our algorithm are by construction guaranteed to be consistent with the counts of each sensor. We further extend our algorithm to allow for weighted topologies and sensing errors for applicability in real-world deployments. We evaluate our approach through an extensive collection of synthetic and real-life configurations.",
author = "Sven Linker and Michele Sevegnani",
year = "2019",
month = nov,
day = "29",
doi = "10.1098/rspa.2019.0278",
language = "English",
volume = "475",
journal = "Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences",
issn = "1364-5021",
publisher = "Royal Society of Chemistry Publishing",
number = "2231",

}

RIS

TY - JOUR

T1 - Target counting with Presburger constraints and its application in sensor networks

AU - Linker, Sven

AU - Sevegnani, Michele

PY - 2019/11/29

Y1 - 2019/11/29

N2 - One of the applications popularized by the emergence of wireless sensor networks is target counting: the computational task of determining the total number of targets located in an area by aggregating the individual counts of each sensor. The complexity of this task lies in the fact that sensing ranges may overlap, and therefore, targets may be overcounted as, in this setting, they are assumed to be indistinguishable from each other. In the literature, this problem has been proven to be unsolvable, hence the existence of several estimation algorithms. However, the main limitation currently affecting these algorithms is that no assurance regarding the precision of a solution can be given. We present a novel algorithm for target counting based on exhaustive enumeration of target distributions using linear Presburger constraints. We improve on current approaches since the estimated counts obtained by our algorithm are by construction guaranteed to be consistent with the counts of each sensor. We further extend our algorithm to allow for weighted topologies and sensing errors for applicability in real-world deployments. We evaluate our approach through an extensive collection of synthetic and real-life configurations.

AB - One of the applications popularized by the emergence of wireless sensor networks is target counting: the computational task of determining the total number of targets located in an area by aggregating the individual counts of each sensor. The complexity of this task lies in the fact that sensing ranges may overlap, and therefore, targets may be overcounted as, in this setting, they are assumed to be indistinguishable from each other. In the literature, this problem has been proven to be unsolvable, hence the existence of several estimation algorithms. However, the main limitation currently affecting these algorithms is that no assurance regarding the precision of a solution can be given. We present a novel algorithm for target counting based on exhaustive enumeration of target distributions using linear Presburger constraints. We improve on current approaches since the estimated counts obtained by our algorithm are by construction guaranteed to be consistent with the counts of each sensor. We further extend our algorithm to allow for weighted topologies and sensing errors for applicability in real-world deployments. We evaluate our approach through an extensive collection of synthetic and real-life configurations.

U2 - 10.1098/rspa.2019.0278

DO - 10.1098/rspa.2019.0278

M3 - Journal article

VL - 475

JO - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

JF - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

SN - 1364-5021

IS - 2231

M1 - 20190278

ER -