Mobile crowd sensing : task assignment, privacy protection, incentive, and application

2021 ◽  
Author(s):  
Zhigang Jia ◽  
Weiwei Zhao ◽  
Jie Luo ◽  
Yang Chen ◽  
ChangJing Xu
Keyword(s):  
Privacy Protection ◽  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

2018 ◽  
Vol 66 (11) ◽  
pp. 5526-5538 ◽  
Author(s):  
Zhenyu Zhou ◽  
Junhao Feng ◽  
Bo Gu ◽  
Bo Ai ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Route Planning ◽  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals Privacy protection in mobile crowd sensing: a survey

2019 ◽  
Vol 23 (1) ◽  
pp. 421-452 ◽  
Author(s):  
Yongfeng Wang ◽  
Zheng Yan ◽  
Wei Feng ◽  
Shushu Liu
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
System Structure ◽  
Smart Devices ◽  
Future Research ◽  
Sensitive Information ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

AbstractThe unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

QoS-constrained sensing task assignment for mobile crowd sensing

2014 ◽  
Author(s):  
Zhijie Wang ◽  
Dijiang Huang ◽  
Huijun Wu ◽  
Yuli Deng ◽  
Ailixier Aikebaier ◽  
...  
Keyword(s):  
Task Assignment ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals Verifiable Location-Encrypted Spatial Aggregation Computing for Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kun Niu ◽  
Changgen Peng ◽  
Weijie Tan ◽  
Zhou Zhou ◽  
Yi Xu
Keyword(s):  
Privacy Protection ◽  
Spatial Information ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Spatial Aggregation ◽  
Mobile Crowd Sensing ◽  
Sensitive Data ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Benefiting from the development of smart urban computing, the mobile crowd sensing (MCS) network has emerged as momentous communication technology to sense and collect data. The users upload data for specific sensing tasks, and the server completes the aggregation analysis and submits to the sensing platform. However, users’ privacy may be disclosed, and aggregate results may be unreliable. Those are challenges in the trust computation and privacy protection, especially for sensitive data aggregation with spatial information. To address these problems, a verifiable location-encrypted spatial aggregation computing (LeSAC) scheme is proposed for MCS privacy protection. In order to solve the spatial domain distributed user ciphertext computing, firstly, we propose an enhanced-distance-based interpolation calculation scheme, which participates in delegate evaluator based on Paillier homomorphic encryption. Then, we use aggregation signature of the sensing data to ensure the integrity and security of the data. In addition, security analysis indicates that the LeSAC can achieve the IND-CPA indistinguishability semantic security. The efficiency analysis and simulation results demonstrate the communication and computation overhead of the LeSAC. Meanwhile, we use the real environment sensing data sets to verify availability of proposed scheme, and the loss of accuracy (global RMSE) is only less than 5%, which can meet the application requirements.

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