Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution.