Qn: Quality and Quantity Based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

Abstract

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 problems, 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 in turn is used to judiciously disburse user 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 trustworthy decision making 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.

Credibility on Crowdsensing Data Acquisition

“…A credibility estimator can process the participant's data and compute an updated credibility indicator which saves the new reputation score for use in future iterations. The recent study of Bhattacharjee et al [206], which is an extension of the Bhattacharjee et al [179], reinforces the idea of an ecosystem with components that are currently acting cooperatively to enhance credibility.…”

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

“…A distributed probabilistic algorithm is presented in [176], where a probabilistic design regulates the amount of data contributed from users in a certain region of interest to minimize data redundancy and energy waste. The algorithm is [172], [173] Energy efficiency Strategies to lower the battery drain of mobile devices during data sensing and reporting [174]- [178] Resource allocation Strategies for efficient resource allocation during data contribution, such as channel condition, power spectrum, computational capabilities [179]- [181] Scalability Solutions to develop DCFs with good scalability properties during run-time data acquisition and processing [182], [183] Sensing task coverage Definition of requirements for task accomplishment, such as spatial and temporal coverage [184]- [187] Trustworthiness and privacy Strategies to address issues related to preserve privacy of the contributing users and integrity of reported data [188]- [193] based on limited feedback from the central collector and does not require users to complete a specific task. EEMC (Enabling energy-efficient mobile crowdsensing) aims to reduce energy consumption due to data contribution both for individuals and the crowd while making secure the gathered information from a minimum number of users within a specific timeframe [177].…”

“…It is based on a dynamic management of nodes and estimation of the trust degree of the public key, which is provided by encountering nodes. QnQ is a Quality and Quantity based unified approach that proposes an event-trust and user-reputation model to classify users according to different classes, such as honest, selfish, or malicious [193]. Specifically, QnQ is based on a rating feedback scheme that evaluates the expected truthfulness of specific events by exploiting a QoI metric to lower effects of selfish and malicious behaviors.…”

A Prospect Theoretic Approach for Trust Management in IoT Networks under Manipulation Attacks

“…Previously, prospect theory has been applied to analyze wireless communications and traffic routing [11,17,36] and for detecting false reporting attacks and users in vehicular crowdsensing systems [6]. In [17], a random access game is formulated using prospect theory to study channel access between two subjective end-users in wireless networks.…”