The University of Southampton
University of Southampton Institutional Repository

A trust-based coordination system for participatory sensing applications

A trust-based coordination system for participatory sensing applications
A trust-based coordination system for participatory sensing applications
Participatory sensing (PS) has gained significant attention as a crowdsourcing methodology that allows ordinary citizens (non-expert contributors) to collect data using low-cost mobile devices. In particular, it has been useful in the collection of environmental data. However, current PS applications suffer from two problems. First, they do not coordinate the measurements taken by their users, which is required to maximise system efficiency. Second, they are vulnerable to malicious behaviour. In this context, we propose a novel algorithm
that simultaneously addresses both of these problems. Specifically, we use heteroskedastic Gaussian Processes to incorporate users’ trustworthiness into a Bayesian spatio-temporal regression model. The model is trained with measurements
taken by participants, thus it is able to estimate the value of
the phenomenon at any spatio-temporal location of interest
and also learn the level of trustworthiness of each user. Given
this model, the coordination system is able to make informed
decisions concerning when, where and who should take measurements
over a period of time. We empirically evaluate our
algorithm on a real-world human mobility and air quality
dataset, where malicious behaviour is synthetically produced,
and show that our algorithm outperforms the current state of
the art by up to 60.4% in terms of RMSE while having a reasonable
runtime.
malicious users, gaussian process, coordination
226-234
AAAI
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Zenonos, Alexandros
d192dd6b-c645-48d9-88a2-7e9ccef27d38
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Zenonos, Alexandros, Stein, Sebastian and Jennings, Nicholas (2017) A trust-based coordination system for participatory sensing applications. In Fifth AAAI Conference on Human Computation and Crowdsourcing. AAAI. pp. 226-234 .

Record type: Conference or Workshop Item (Paper)

Abstract

Participatory sensing (PS) has gained significant attention as a crowdsourcing methodology that allows ordinary citizens (non-expert contributors) to collect data using low-cost mobile devices. In particular, it has been useful in the collection of environmental data. However, current PS applications suffer from two problems. First, they do not coordinate the measurements taken by their users, which is required to maximise system efficiency. Second, they are vulnerable to malicious behaviour. In this context, we propose a novel algorithm
that simultaneously addresses both of these problems. Specifically, we use heteroskedastic Gaussian Processes to incorporate users’ trustworthiness into a Bayesian spatio-temporal regression model. The model is trained with measurements
taken by participants, thus it is able to estimate the value of
the phenomenon at any spatio-temporal location of interest
and also learn the level of trustworthiness of each user. Given
this model, the coordination system is able to make informed
decisions concerning when, where and who should take measurements
over a period of time. We empirically evaluate our
algorithm on a real-world human mobility and air quality
dataset, where malicious behaviour is synthetically produced,
and show that our algorithm outperforms the current state of
the art by up to 60.4% in terms of RMSE while having a reasonable
runtime.

Text
A Trust-Based Coordination System for Participatory Sensing Applications - Accepted Manuscript
Download (241kB)

More information

Accepted/In Press date: 25 June 2017
e-pub ahead of print date: 21 September 2017
Published date: 2017
Venue - Dates: 5th AAAI Conference on Human Computation and Crowdsourcing, , Quebec City, Canada, 2017-10-24 - 2017-10-26
Keywords: malicious users, gaussian process, coordination

Identifiers

Local EPrints ID: 412918
URI: http://eprints.soton.ac.uk/id/eprint/412918
PURE UUID: 7510e0f5-1e6d-4c8d-b5ed-0c5e1cb59999
ORCID for Alexandros Zenonos: ORCID iD orcid.org/0000-0003-4694-1642

Catalogue record

Date deposited: 08 Aug 2017 16:31
Last modified: 02 Nov 2020 17:33

Export record

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.

×