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

CRII：NeTS：绿色移动人群感知建模与分析

• 批准号: 1924463
• 负责人: Ming Li
• 金额: $ 8.72万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924463&HistoricalAwards=false
• 关键词: CRII; NeTS; Modeling; Analysis; Green; CRII; NeTS; Modeling; Analysis; Green

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.

移动人群传感（MCS）是一种新的感测范式，基于人群的力量以及各种移动设备的不断增强的感应功能。由于执行传感任务可以迅速耗尽电池供电的移动设备的能量，因此这种担忧在很大程度上影响了MC的广泛部署。因此，如何提高MC中的能源效率是一项必须且具有挑战性的任务。受到无线网络和能源收集技术的最新进展的启发，拟议的研究旨在开发联合感应任务计算和通信框架，以实现各种感应任务的绿色MC。该研究项目和活动具有更好的潜力，可以更好地支持新兴新兴的MCS应用程序，例如医疗保健，环境监测，交通监测，社交行为监测等。期望该研究结果启发其他理论和系统的研究，从而为开发能源有效的MCS的网络设计和能源管理方面做出贡献。该项目计划让女性和代表性不足的少数族裔学生参与研究活动。该项目的结果将通过出版物和谈判进行传播。该项目制定了一项激动人心的研究计划，重点介绍与建模和分析绿色MC相关的基本挑战。观察到任务处理中消耗的能量及其分布相互关联，提出了一个统一的框架来建模计算和通信中的能源消耗以在两者之间取得平衡以达到能源效率。由于可再生能源已成为传统能源的可行替代方案，因此它被纳入传感人群中，以减少感应设备的网格能量需求。然后，研究动态能量优化问题，以最大程度地限制能量支出，以在支持性能保证的传感任务中，通过全面考虑随时间变化的计算资源分配，可再生能源供应和无线通道条件。此外，某些传感设备设想能够将额外收获的可再生能源转移到附近的其他人，以便充分探索MC中的空置能量和计算资源。最后，为了刺激移动设备加入MCS，开发了激励机制和异质拍卖市场。