Participant Recruitment and Data Collection Framework for Opportunistic Sensing: A Comparative Analysis

Mobile crowdsensing is a novel approach that exploits the sensing capabilities offered by smartphones and users’ mobility to sense large scale areas without requiring the deployment of sensors insitu. Opportunistic sensing utilizes users’ normal behavior to crowd-source sensing missions. In this work, we propose a novel framework for fully distributed, opportunistic sensing that coherently integrates two main components that operate in DTN mode: i. participant recruitment and ii. data collection. We adopt a new approach to match mobility profiles of users to the coverage of the sensing mission. We analyze several distributed approaches for both components through extensive trace-based simulations, including epidemic routing, PROPHET, spray and wait, profile-cast, and opportunistic geocast. The performances of these protocols are compared using realistic mobility traces from wireless LANs, various mission coverage patterns and sink mobility profiles. Our results show how the performances of the considered protocols vary, depending on the particular scenario, and suggest guidelines for future development of distributed opportunistic sensing systems.