NeTS: Small: Large-Scale Opportunistic Data Crowdsourcing and Dissemination in Device-to-Device (D2D) Networks

NeTS：小型：设备到设备 (D2D) 网络中的大规模机会性数据众包和传播

• 批准号: 1649676
• 负责人: Hongyi Wu
• 金额: $ 38.5万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649676&HistoricalAwards=false
• 关键词: NeTS; Small; Large; Scale; Opportunistic

The vast majority of today's wireless communication systems operate in the microwave spectrum below 3 GHz, which is experiencing severe shortage and has become a crowded resource. To meet the 1000x growth challenge in mobile broadband traffic, the millimeter wave (mmWave) band, operating at frequencies between 20 and 300 GHz, has been identified for next-generation (5G) cellular systems. While the use of mmWave band addresses the pressing needs of more wireless spectrum, it brings a new set of unique technical challenges such as severe path loss and undesired coverage holes. To this end, Device-to-Device (D2D) networks are proposed to employ short-range wireless links to establish opportunistic connections between mobile users. In this project, the researchers will explore a diversity of application-oriented problems in D2D, culminating in the formulation of both new fundamental theories and advanced technologies that contribute to the development of next-generation mobile communication systems. This project will effectively stimulate multi-disciplinary collaboration across a broad spectrum of fields, including anthropology, communications, computer science, economics, public health, demography, and sociology. It will also effectively enrich courses by implementation and experimental activities, providing students with hands-on experience.The proposed research includes two research thrusts to design, implement, evaluate, and prototype new protocols and algorithms, in support of efficient data gathering and dissemination in D2D. First, a class of applications involve large-scale data gathering from mobile devices. Although crowdsourcing has been discussed in recent years, the marriage of crowdsourcing and D2D creates new, interesting research problems, due to the unique non-deterministic network paradigm. The researchers will investigate several dimensions in support of D2D-based crowdsourcing, including a competition-based participant recruitment scheme for delay-sensitive applications and an effective quest algorithm to deliver crowdsourcing requests. Second, efficient data dissemination is indispensable in many D2D applications. In contrast to the prior work that focuses on classical multicasting from a source to a given set of receivers, the researchers propose to investigate a unique and interesting problem where the receivers are not explicitly known. In such settings, a natural approach is to distribute data at some depositories, that further deliver the content to interested data consumers upon requests. Under this framework, the researchers will devise algorithms to choose optimal depositories for maximizing the total profit and develop new incentive schemes to enable efficient dissemination. Complementing these research thrusts is an experimental prototyping and validation track, with various design choices and alternatives experimentally studied, evaluated and refined.

当今绝大多数无线通信系统工作在3GHz以下的微波频谱中，这正经历严重短缺并已成为拥挤的资源。为了满足移动的宽带业务1000倍增长的挑战，工作在20和300 GHz之间频率的毫米波（mmWave）频带已被确定用于下一代（5G）蜂窝系统。虽然毫米波频段的使用解决了更多无线频谱的迫切需求，但它带来了一系列新的独特技术挑战，例如严重的路径损耗和不希望的覆盖漏洞。为此，提出了设备到设备（D2D）网络以采用短距离无线链路来建立移动的用户之间的机会连接。在这个项目中，研究人员将探索D2D中各种面向应用的问题，最终制定新的基础理论和先进技术，为下一代移动的通信系统的发展做出贡献。该项目将有效地促进跨广泛领域的多学科合作，包括人类学，通信，计算机科学，经济学，公共卫生，人口学和社会学。它还将通过实施和实验活动有效地丰富课程，为学生提供实践经验。拟议的研究包括两个研究重点，以设计、实施、评估和原型化新协议和算法，以支持D2D中的高效数据收集和传播。首先，一类应用涉及从移动的设备收集大规模数据。虽然近年来已经讨论了众包，但由于独特的非确定性网络范式，众包和D2D的结合产生了新的有趣的研究问题。研究人员将研究支持基于D2D的众包的几个方面，包括基于竞争的参与者招募计划，用于延迟敏感的应用程序和有效的搜索算法来提供众包请求。其次，高效的数据分发在许多D2D应用中是不可或缺的。与之前专注于从源到给定接收器集合的经典多播的工作相反，研究人员建议调查一个独特而有趣的问题，其中接收器并不明确。在这样的环境中，一种自然的方法是在一些存放处分发数据，这些存放处进一步根据请求将内容传递给感兴趣的数据消费者。在这个框架下，研究人员将设计算法来选择最佳的存储库，以最大限度地提高总利润，并开发新的激励计划，以实现有效的传播。补充这些研究推力是一个实验原型和验证轨道，与各种设计选择和替代实验研究，评估和完善。