A trust-based coordination system for participatory sensing applications

Title: A trust-based coordination system for participatory sensing applications
Author(s): Jennings, N
Zenonos, A
Stein, S
Item Type: Conference 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.
Publication Date: 30-Sep-2017
Date of Acceptance: 30-Sep-2017
URI: http://hdl.handle.net/10044/1/55662
Publisher: AAAI
Start Page: 226
End Page: 234
Journal / Book Title: Proc. 5th Int. Conf. on Human Computation and Crowdsourcing
Copyright Statement: Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Conference Name: 5th Int. Conf. on Human Computation and Crowdsourcing
Publication Status: Published
Start Date: 2017-10-17
Finish Date: 2017-10-26
Conference Place: Quebec City, Canada
Open Access location: https://aaai.org/ocs/index.php/HCOMP/HCOMP17/paper/view/15818
Appears in Collections:Faculty of Natural Sciences

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