CRII: NeTS: Modeling and Analysis of Green Mobile Crowd Sensing

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

Mobile crowd sensing (MCS) arises as a new sensing paradigm based on the power of the crowd together with the ever-increasing sensing capabilities of various mobile devices. As carrying out sensing tasks can deplete the energy of battery-powered mobile devices quickly, this concern largely affects the wide deployment of MCS. Therefore, how to enhance the energy efficiency in MCS is an imperative and challenging task. Inspired by recent advances in wireless networking and energy harvesting techniques, the proposed research aims to develop joint sensing task computation and communication framework to achieve green MCS for various sensing tasks. The research project and activities have significant potential to better support newly emerging MCS applications such as healthcare, environment monitoring, traffic monitoring, social behavior monitoring, etc. The research results are expected to inspire other theoretical and systematic studies to contribute to the networking design and energy management aspects of developing energy-efficient MCS. The project plans to engage female and under-represented minority students in the research activities. The results of the project will be disseminated through publications and talks. This project has an exciting two-year research plan focusing on fundamental challenges associated with modeling and analyzing green MCS. Observing that the energy consumed in task processing and its distribution correlates to each other, a unified framework to jointly model the energy consumption in computation and communication is proposed to strike a balance between the two to achieve energy efficiency. As renewable energy has emerged as a feasible alternative to the traditional energy sources, it is incorporated in the sensing crowd so as to decrease the on-grid energy demand from sensing devices. Dynamic energy optimization problems are then investigated to minimize energy expenditure in supporting performance-guaranteed sensing tasks, by comprehensively considering time-varying computing resource allocation, renewable energy supply, and wireless channel conditions. Moreover, some sensing devices are envisioned to be capable of transferring extra harvested renewable energy to others nearby, so as to fully explore the vacant energy and computation resources in MCS. Finally, in order to stimulate mobile devices to join MCS, incentive mechanisms and heterogeneous auction markets are developed.