QnQ: Quality and Quantity based Unified Approach for Secure and Trustworthy Mobile Crowdsensing


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

Research Center/Lab(s)

Center for Research in Energy and Environment (CREE)

Second Research Center/Lab

Center for High Performance Computing Research

Third Research Center/Lab

Intelligent Systems Center


The work has been supported by the following NSF grants: CNS-1818942, CNS-1545037, CNS1545050, and DGE-1433659.

Keywords and Phrases

Computer networks; Mobile computing; Crowd sensing; Dependable systems; Participatory Sensing; Quality of information; Reputation; Trust; Behavioral research; Crowdsensing

International Standard Serial Number (ISSN)

1536-1233; 1558-0660

Document Type

Article - Journal

Document Version


File Type





© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2020