CAREER: Collaborative Signal and Information Processing in Sensor Networks

该提案提出了一项将研究与教育相结合的职业发展计划。该项目的研究部分集中在研究协作信号和信息处理(CSIP)算法以及传感器网络中的支持计算模型和协议。教育部分旨在通过创新的课程开发、积极的学生辅导以及将研究成果纳入课堂教学，加强系新设立的计算机工程课程。该项目整合了研究和教育工作，应对了传感器网络带来的独特挑战，并为PI的事业发展做出了重要贡献。传感器网络形成了一个松散耦合的分布式环境，其中多个传感器节点之间的协作处理是必不可少的，以弥补彼此在传感、处理、供电和容错方面的有限能力。传感器网络极其有限的资源对CSIP提出了独特的挑战，其中最大的挑战是能量效率和容错性之间的矛盾要求。虽然节能方法试图限制冗余，以使完成特定任务所需的能量最少，但由于传感器可能出现故障、故障甚至恶意，因此需要冗余来提供容错。必须在这两个目标之间建立平衡。智力优势：提出了一项综合研究计划，以应对传感器网络带来的独特挑战。该计划不仅涉及有效处理算法的开发，还研究了支持计算范例和协议的设计，这些范例和协议在促进协作处理方面发挥了重要作用。具体而言，该研究计划主要围绕三个主题展开：第一，设计、评估和实现了一种新的基于移动代理的传感器网络协同处理范式。在该模型中，每个传感器节点将本地信息发送到处理中心进行集成，而不是像基于客户端/服务器的计算中那样，集成代码通过移动代理移动到传感器节点。我们讨论了基于移动代理的协作处理在提供渐进精度的同时保持一定程度的默认容错的潜力。第二，开发和评估分散的反应式集群协议，以帮助适应不断变化的环境。由于部署的节点数量庞大，协作通常在同一群集中的节点之间进行。提出了一种分散式反应式分簇(DRC)协议，该协议仅在检测到事件时才启动分簇过程。分析了能量消耗和网络寿命对性能的影响。第三，网内数据处理的分布式优化研究。我们解决了具有挑战性的CSIP问题，包括多目标/事件检测和未知目标/事件识别，并开发了分散算法，实现了能量效率和带宽效率优势，以及与集中式算法相比的检测性能提升。广泛影响：PI致力于卓越的教学和热情的学生指导。传感器网络中的CSIP作为综合多学科特征的领域之一，将作为一个实验集中领域来启动创新的课程设计，这有助于引入独特的功能，以提高该系新设立的计算机工程专业的质量和可见度。虽然传感器网络在CSIP中显示出了巨大的潜力，但极其有限的资源在很大程度上限制了它们的实际部署。CSIP的计算模型、协议和处理算法的性能评估将有助于了解传感器网络的性能，并为CSIP算法的设计提供理论指导。研究和教育成果将通过互联网上的Twiki协作平台提供。1

This proposal presents a career development plan for integrating research and education. The researchcomponent of this program focuses on the study of collaborative signal and information processing (CSIP)algorithms as well as the supporting computing models and protocols in sensor networks. The educationalcomponent is aimed at enhancing the newly created computer engineering program of the departmentthrough innovative curriculum development, active student mentoring, and by incorporating research findingsinto classroom teaching. The project consolidates research and educational efforts, responds to theunique challenges presented by sensor networks, and contributes significantly to the PI's career development.A sensor network forms a loosely-coupled distributed environment where collaborative processing amongmultiple sensor nodes is essential in order to compensate for each other's limited capability in sensing, processing,power supply, and to tolerate faults. The extremely constraint resources of sensor networks havepresented unique challenges to CSIP, the biggest of which is the contradictory requirements between energyefficiency and fault tolerance. While energy-efficient approaches try to limit the redundancy such thatminimum amount of energy is required for fulfilling a certain task, redundancy is needed for providingfault tolerance since sensors might be faulty, malfunctioning, or even malicious. A balance has to be struckbetween these two objectives.Intellectual Merit: An integrated research plan is proposed that tackles the unique challenges presentedby sensor networks. This plan concerns not only the development of effective processing algorithms, italso studies the design of supporting computing paradigms and protocols which play an important role infacilitating the collaborative processing. In particular, the research plan focusses on three themes:First, the design, evaluation and implementation of a new paradigm, the mobile-agent-based paradigm,for realizing collaborative processing in sensor networks. In this model, instead of each sensor node sendinglocal information to a processing center for integration, as is typical in a client/server-based computing, theintegration code is moved to the sensor nodes through mobile agents. We discuss the potential of mobileagent-based collaborative processing in providing progressive accuracy while maintaining certain degree offault tolerance. Second, the development and evaluation of a decentralized reactive clustering protocol tohelp adapt to the changing environment. Due to the sheer amount of nodes deployed, collaboration is usuallycarried out among nodes within the same cluster. We propose a decentralized reactive clustering (DRC)protocol in which the clustering procedure is initiated only when events are detected. Performance gain interms of energy consumption and network lifetime is analyzed. Third, the study of distributed optimizationfor in-network data processing. We tackle the challenging CSIP problems including multiple target/eventdetection and unknown target/event identification and develop decentralized algorithms that achieve bothenergy-efficiency and bandwidth-efficiency advantages, as well as detection performance gain compared toits centralized counterpart.Broader Impact: The PI is dedicated to excellence in teaching and enthusiastic student mentoring.CSIP in sensor networks, as one of the areas that integrate multidisciplinary characteristics, will be usedas an experimental concentration area to initiate innovative curriculum design, which helps bring in uniquefeatures to increase the quality and visibility of the newly established computer engineering program ofthe department. Although sensor networks have revealed great potential in CSIP, the extremely constraintresources have largely limited their practical deployment. The performance evaluation of computing models,protocols, and processing algorithms for CSIP would help understand the capability of sensor networks aswell as provide theoretical guide in the design of CSIP algorithms. Both the research and educational resultswill be made available through the Twiki collaboration platform on the Internet.1