The University of Southampton
University of Southampton Institutional Repository

An algorithm to coordinate measurements using stochastic human mobility patterns in large-scale participatory sensing settings

An algorithm to coordinate measurements using stochastic human mobility patterns in large-scale participatory sensing settings
An algorithm to coordinate measurements using stochastic human mobility patterns in large-scale participatory sensing settings
Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.
3936-3942
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas R
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas R
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Zenonos, Alexandros, Stein, Sebastian and Jennings, Nicholas R (2016) An algorithm to coordinate measurements using stochastic human mobility patterns in large-scale participatory sensing settings. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). pp. 3936-3942 .

Record type: Conference or Workshop Item (Paper)

Abstract

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.

Text
zenonos.pdf - Accepted Manuscript
Download (309kB)
Text
12100-56375-1-PB - Version of Record
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 12 November 2015
e-pub ahead of print date: 17 February 2016
Venue - Dates: Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), 2015-11-12
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 384821
URI: http://eprints.soton.ac.uk/id/eprint/384821
PURE UUID: 7d2af871-9cf0-4461-b76c-374f7eff81a2
ORCID for Alexandros Zenonos: ORCID iD orcid.org/0000-0003-4694-1642

Catalogue record

Date deposited: 14 Dec 2015 15:15
Last modified: 06 Oct 2020 23:35

Export record

Contributors

Author: Alexandros Zenonos ORCID iD
Author: Sebastian Stein
Author: Nicholas R Jennings

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×