Skip to content

Research at St Andrews

Self-stabilising target counting in wireless sensor networks using Euler integration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

DOI

Open Access permissions

Open

Author(s)

Danilo Pianini, Simon Andrew Dobson, Mirko Viroli

School/Research organisations

Abstract

Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.
Close

Details

Original languageEnglish
Title of host publication2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
PublisherIEEE Computer Society
Pages11-20
ISBN (Electronic)9781509065554
DOIs
Publication statusPublished - 12 Oct 2017
Event11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2017) - University of Arizona, Tucson, United States
Duration: 18 Sep 201722 Sep 2017
Conference number: 11
https://saso2017.telecom-paristech.fr/

Conference

Conference11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2017)
Abbreviated titleSASO
CountryUnited States
CityTucson
Period18/09/1722/09/17
Internet address

    Research areas

  • Wireless Sensor Networks, Algebraic Topology, Self-stabilising Algorithms

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Lifelong learning in sensor-based human activity recognition

    Ye, J., Dobson, S. A. & Zambonelli, F., 18 Nov 2019, In : IEEE Pervasive Computing. 18, 3, p. 49-58 10 p.

    Research output: Contribution to journalArticle

  2. Representation learning for minority and subtle activities in a smart home environment

    Rosales Sanabria, A., Kelsey, T., Dobson, S. A. & Ye, J., 28 Oct 2019, In : Journal of Ambient Intelligence and Smart Environments. Pre-press, p. 1-19

    Research output: Contribution to journalArticle

  3. Sensor-based human activity mining using Dirichlet process mixtures of directional statistical models

    Fang, L., Ye, J. & Dobson, S. A., 5 Oct 2019, Proceedings of the 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA'19). IEEE Computer Society

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  4. simplicial: Simplicial topology in Python

    Dobson, S. A., 19 Sep 2019

    Research output: Non-textual formSoftware

  5. Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models

    Fang, L., Ye, J. & Dobson, S. A., 16 Jun 2019, Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019). IEEE Computer Society, p. 33-42 8780572

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

ID: 250560264

Top