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QnQ: Quality and Quantity based Unified Approach for Secure and Trustworthy Mobile Crowdsensing
IEEE Transactions on Mobile Computing
  • Shameek Bhattacharjee
  • Nirnay Ghosh
  • Vijay K. Shah
  • Sajal K. Das, Missouri University of Science and Technology

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which is then used to judiciously disburse incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware decision scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses and significantly improves operational accuracy in presence of rogue contributions.

Computer Science
The work has been supported by the following NSF grants: CNS-1818942, CNS-1545037, CNS1545050, and DGE-1433659.
Article in Press
Keywords and Phrases
  • Computer networks,
  • Mobile computing,
  • Crowd sensing,
  • Dependable systems,
  • Participatory Sensing,
  • Quality of information,
  • Reputation,
  • Trust,
  • Behavioral research,
  • Crowdsensing
Document Type
Article - Journal
Document Version
File Type
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
Citation Information
Shameek Bhattacharjee, Nirnay Ghosh, Vijay K. Shah and Sajal K. Das. "QnQ: Quality and Quantity based Unified Approach for Secure and Trustworthy Mobile Crowdsensing" IEEE Transactions on Mobile Computing (2018) ISSN: 1536-1233; 1558-0660
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