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

移动的人群感测（MCS）作为基于人群的力量以及各种移动的设备的不断增加的感测能力的新的感测范例而出现。由于执行感测任务可能会迅速耗尽电池供电的移动的设备的能量，因此这种担忧在很大程度上影响了MCS的广泛部署。因此，如何提高MCS的能量效率是一个迫切而具有挑战性的任务。受无线网络和能量收集技术的最新进展的启发，拟议的研究旨在开发联合传感任务计算和通信框架，以实现各种传感任务的绿色MCS。该研究项目和活动具有巨大的潜力，以更好地支持新兴的MCS应用，如医疗保健，环境监测，交通监测，社会行为监测等研究成果，预计将激发其他理论和系统的研究，以促进网络设计和能源管理方面的发展节能MCS。该项目计划让女生和人数不足的少数民族学生参与研究活动。该项目的成果将通过出版物和讲座进行传播。该项目有一个令人兴奋的两年研究计划，重点关注与建模和分析绿色MCS相关的基本挑战。观察到的能量消耗在任务处理和它的分布相互关联，提出了一个统一的框架，以联合建模的计算和通信的能量消耗之间取得平衡，以实现能源效率。由于可再生能源已成为传统能源的可行替代品，因此将其纳入传感人群，以减少传感设备的上网能量需求。动态能量优化问题进行了研究，以最大限度地减少能源支出，在支持性能保证的传感任务，综合考虑时变的计算资源分配，可再生能源供应，和无线信道条件。此外，一些传感设备被设想为能够将额外收获的可再生能源转移到附近的其他设备，以便充分利用MCS中的空闲能量和计算资源。最后，为了激励移动的设备加入MCS，激励机制和异构拍卖市场的发展。

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.