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Coordinating measurements for environmental monitoring in uncertain participatory sensing settings

Coordinating measurements for environmental monitoring in uncertain participatory sensing settings
Coordinating measurements for environmental monitoring in uncertain participatory sensing settings
Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.
1076-9757
433-474
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136

Zenonos, Alexandros, Stein, Sebastian and Jennings, Nicholas R. (2018) Coordinating measurements for environmental monitoring in uncertain participatory sensing settings. Journal of Artificial Intelligence Research, 61, 433-474. (doi:10.1613/jair.5484).

Record type: Article

Abstract

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

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Accepted/In Press date: 2 December 2017
e-pub ahead of print date: 10 March 2018
Published date: 10 March 2018

Identifiers

Local EPrints ID: 416702
URI: http://eprints.soton.ac.uk/id/eprint/416702
ISSN: 1076-9757
PURE UUID: 5fb79bdd-18a8-43dc-ae16-5dc241bc815e
ORCID for Alexandros Zenonos: ORCID iD orcid.org/0000-0003-4694-1642
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

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Date deposited: 05 Jan 2018 17:30
Last modified: 16 Mar 2024 03:57

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Contributors

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

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