Sociability-Driven Framework for Data Acquisition in Mobile Crowdsensing over Fog Computing Platforms for Smart Cities

Abstract

Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) augments the capabilities of the platform without enquiring additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates a number of key operations including user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users as well as the energy they spend to sense and deliver data. We propose a new user recruitment policy called Distance, Sociability, Energy (DSE). This policy exploits three criteria: (i) spatial distance between users and tasks, (ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and (iii) remaining battery charge of the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics assessing the efficiency of recruitment and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20 percent if compared to policies using only spatial distance as selection criterion.