Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things
Burak Kantarcı, Hussein Talaat Mouftah · 2014
In the Internet of Things (IoT) era, smart devices that are equipped with various types of sensors can enable access to the IoT architecture through a cloud-inspired service model, namely Sensing-as-a-Service (S2aaS). S2aaS can provide crowdsourced data to an application running on a cloud platform. The crowdsourced data can be used for several purposes such as public safety. One of the biggest challenges here is the incentive mechanisms for the users who are requested to provide S2aaS. In this paper, we propose mobility-aware trustworthy crowdsourcing (MATCS) framework in a cloud-centric IoT architecture which adopts and extends a previous scheme, Trustworthy Sensing for Crowd Management (TSCM) [1] by incorporating user mobility-awareness in the presence of maliciously altered sensing data. MATCS employs a user-centric incentive mechanism which collects sensing data based on an auction procedure. In the auction procedure, MATCS uses users' reputations, bids, current location and their estimated dislocation during crowdsourcing process. Furthermore, in order to investigate the benefits of reputation-awareness, we also propose reputation-unaware Mobility-Aware Crowdsourcing (MACS). Performance of MATCS is evaluated via simulations, and it is compared to MACS and a benchmark scheme, which aims at making a compromise between the utilities of the users and the platform by considering neither mobility nor trustworthiness. Simulation results confirm that mobility-awareness improves the utility of the platform significantly whereas combining reputation-awareness and mobility-awareness by MATCS can triple the improvement. Besides, user incomes are not significantly impacted by MACS or MATCS when users are mobile. Furthermore, maliciously altered data ratio can be degraded by 20%~55% by reputation-awareness in MATCS.