Distributed Data Collection Control in Opportunistic Mobile Crowdsensing

Abstract

Many researchers have nowadays shown a paramount interest in the rising field of Mobile Crowdsensing (MCS). Such paradigm is considered an easy and cost-effective choice for observing phenomena of common interest within the scope of Smart Cities and environmental monitoring. Nevertheless, it brings along many issues, such as fostering participation, reducing the power consumption of end devices and granting coverage. In this paper we focus on the problem of data collection control, which aims to avoid data redundancy and useless power consuming data transfers while assuring a sufficient number of observations for the purpose of coverage. In particular, we design a probabilistic distributed algorithm that aims to achieve a total per-zone number of observations close to a defined amount, while maximizing the fairness among users. We provide both the analytical definition of our algorithm and the performance evaluation through extensive simulations, establishing our algorithm as a good baseline for a poorly investigated problem.