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Sensors
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Privacy, Trust and Incentives in Crowdsensing
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Privacy, Trust and Incentives in Crowdsensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 9281

Special Issue Editors

Prof. Dr. Alessandro Bogliolo

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy
Interests: digital social innovation; wireless sensor networks; crowdsensing
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenz Cuno Klopfenstein

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy
Interests: mobile platforms; multimedia; crowdsensing; computational thinking

Special Issue Information

Dear Colleagues,

The ubiquitous nature of sensor-equipped smartphone devices, smart home assistants, vehicles with communication and sensing capabilities, ultrapersonal devices, such as wearables, and a growing number of IoT devices has given rise to previously unthinkable possibilities in terms of data collection. Virtually every device in our lives, including the most inconspicuous and most limited ones, has the ability to sense data from their environment, transform it using on-board computing capabilities, and transmit it through Internet connectivity. The large availability of big data cloud computing environments has also given us the ability to collect and analyze huge-scale quantities of data. These capabilities carry with them the tremendous potential of crowdsensing and collective intelligence through user participation, but also several implications and open challenges, such as privacy protection, data obfuscation and anonymization, data quality and ground-truth discovery with unreliable sensors, incentive and reward mechanisms to drive user participation, and trust and reputation of users and devices. Recently, forms of mobile crowdsensing have been proposed as effective instruments for contagion tracing and containment for the COVID-19 pandemic. These applications, however, raise urgent questions of technical and ethical nature, such as how to reconcile user privacy and public interest.

This Special Issue has the goal of publishing the results of recent research studies related to crowdsensing and its varied applications, with a focus on privacy, trust, and incentive mechanisms.

Dr. Alessandro Bogliolo
Dr. Lorenz Cuno Klopfenstein
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mobile crowdsensing
  • crowdsourcing
  • data quality
  • big data
  • data privacy and anonymity
  • trust
  • reward and incentive systems
  • citizen science
  • contact tracing

Published Papers (3 papers)

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Research

23 pages, 1153 KiB  
Article
PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems
by Zhong Zhang, Dae Hyun Yum and Minho Shin
Sensors 2021, 21(21), 7045; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217045 - 24 Oct 2021
Viewed by 1850
Abstract
Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the [...] Read more.
Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the users’ privacy and discourage their participation. In particular, the rewarding process can expose the participants’ sensor data and possibly link sensitive data to their identities. In this work, we propose a privacy-preserving reward system in crowdsensing using the blind signature. The proposed scheme protects the participants’ privacy by decoupling contributions and rewarding claims. Our experiment results show that the proposed mechanism is feasible and efficient. Full article
(This article belongs to the Special Issue Privacy, Trust and Incentives in Crowdsensing)
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17 pages, 1432 KiB  
Article
Mobility-Aware Privacy-Preserving Mobile Crowdsourcing
by Guoying Qiu, Yulong Shen, Ke Cheng, Lingtong Liu and Shuiguang Zeng
Sensors 2021, 21(7), 2474; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072474 - 02 Apr 2021
Cited by 4 | Viewed by 1736
Abstract
The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as [...] Read more.
The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability. Full article
(This article belongs to the Special Issue Privacy, Trust and Incentives in Crowdsensing)
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21 pages, 2113 KiB  
Article
Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform
by Robin Kraft, Ferdinand Birk, Manfred Reichert, Aniruddha Deshpande, Winfried Schlee, Berthold Langguth, Harald Baumeister, Thomas Probst, Myra Spiliopoulou and Rüdiger Pryss
Sensors 2020, 20(12), 3456; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123456 - 18 Jun 2020
Cited by 10 | Viewed by 4873
Abstract
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to [...] Read more.
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case. Full article
(This article belongs to the Special Issue Privacy, Trust and Incentives in Crowdsensing)
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