Practical and Privacy-Aware Truth Discovery in Mobile Crowd Sensing Systems

2018 ◽  
Author(s):  
Guowen Xu ◽  
Hongwei Li ◽  
Rongxing Lu
Keyword(s):  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

2020 ◽  
Vol 16 (2) ◽  
pp. 1045-1057 ◽  
Author(s):  
Yang Du ◽  
Yu-E Sun ◽  
He Huang ◽  
Liusheng Huang ◽  
Hongli Xu ◽  
...  
Keyword(s):  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

scholarly journals A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Taochun Wang ◽  
Chengmei Lv ◽  
Chengtian Wang ◽  
Fulong Chen ◽  
Yonglong Luo
Keyword(s):  
Data Aggregation ◽  
Rapid Development ◽  
Mobile Crowd Sensing ◽  
Sense Data ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Head Node ◽  
True Values ◽  
Mobile Crowd

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

Efficient and Privacy-Preserving Truth Discovery in Mobile Crowd Sensing Systems

2019 ◽  
Vol 68 (4) ◽  
pp. 3854-3865 ◽  
Author(s):  
Guowen Xu ◽  
Hongwei Li ◽  
Sen Liu ◽  
Mi Wen ◽  
Rongxing Lu
Keyword(s):  
Privacy Preserving ◽  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Mobile Crowd

Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

2022 ◽  
Vol 22 (2) ◽  
pp. 1-15
Author(s):  
Tu N. Nguyen ◽  
Sherali Zeadally
Keyword(s):  
Mobile Devices ◽  
Cyber Physical System ◽  
Redundant Information ◽  
Mobile Crowd Sensing ◽  
Sensing System ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Collection Methods

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

scholarly journals Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ling-Yun Jiang ◽  
Fan He ◽  
Yu Wang ◽  
Li-Juan Sun ◽  
Hai-ping Huang
Keyword(s):  
Incentive Mechanism ◽  
Data Reliability ◽  
Mobile Crowd Sensing ◽  
Simulation Experiments ◽  
Universal System ◽  
Multiple User ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
The Impact ◽  
Mobile Crowd

Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.

scholarly journals Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems

2016 ◽  
Vol 3 (5) ◽  
pp. 839-853 ◽  
Author(s):  
Stylianos Gisdakis ◽  
Thanassis Giannetsos ◽  
Panagiotis Papadimitratos
Keyword(s):  
Mobile Crowd Sensing ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Mobile Crowd
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