scholarly journals Participatory sensing: applications and architecture [Internet Predictions]

2010 ◽  
Vol 14 (1) ◽  
pp. 12-42 ◽  
Author(s):  
D. Estrin
Keyword(s):  
Participatory Sensing ◽  
Sensing Applications

scholarly journals A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoguang Niu ◽  
Jiawei Wang ◽  
Qiongzan Ye ◽  
Yihao Zhang
Keyword(s):  
Dynamic Balance ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Circulation System ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Ideal Situation ◽  
Conflicting Objectives ◽  
Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

scholarly journals Self-organising management of user-generated data and knowledge

2014 ◽  
Vol 30 (3) ◽  
pp. 237-264 ◽  
Author(s):  
Sam Macbeth ◽  
Jeremy V. Pitt
Keyword(s):  
Pervasive Computing ◽  
User Generated Content ◽  
Participatory Sensing ◽  
Open Approach ◽  
Computational Logic ◽  
New Class ◽  
Sensing Applications ◽  
Development Framework ◽  
Governance Model ◽  
Institutional Analysis And Development

AbstractThe proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.

scholarly journals A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

2017 ◽  
Author(s):  
Qikun Xiang ◽  
Jie Zhang ◽  
Ido Nevat ◽  
Pengfei Zhang
Keyword(s):  
Real World ◽  
Gaussian Processes ◽  
Spatial Regression ◽  
Participatory Sensing ◽  
Sensing Applications ◽  
Different Types ◽  
Inaccurate Estimation ◽  
Real World Datasets ◽  
Gp Model ◽  
Mixture Of Gaussian

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

scholarly journals A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data

2018 ◽  
Vol 13 (3) ◽  
pp. 203-222 ◽  
Author(s):  
Ourania Kounadi ◽  
Bernd Resch
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Participatory Sensing ◽  
Practical Relevance ◽  
Design Guideline ◽  
Technical Aspects ◽  
Sensing Data ◽  
Research Survey ◽  
Sensing Applications ◽  
Clear Guideline

Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants’ privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables.

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