CAREER: Collaborative Signal and Information Processing in Sensor Networks

该提案提出了一个将研究与教育相结合的职业发展计划。该计划的研究部分侧重于研究协同信号和信息处理（CSIP）算法以及支持传感器网络中的计算模型和协议。教育部分的目的是通过创新的课程开发，积极的学生辅导，并通过将研究成果纳入课堂教学，以提高该部门新创建的计算机工程计划。该项目整合了研究和教育工作，响应了传感器网络所带来的独特挑战，并为PI的职业发展做出了重大贡献。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的协同处理是必不可少的，以补偿彼此在传感，处理，电源和容错方面的有限能力。传感器网络资源的极度受限对CSIP提出了独特的挑战，其中最大的挑战就是能量有效性和容错性之间的矛盾要求.虽然节能方法试图限制冗余，使得完成某项任务所需的能量最小，但由于传感器可能有故障，故障甚至恶意，因此需要冗余来提供容错。这两个objectives.Intellectual Merit之间的平衡：提出了一个综合的研究计划，解决了传感器网络提出的独特挑战。该计划不仅涉及有效处理算法的开发，还研究了支持协同处理的计算范式和协议的设计，这些计算范式和协议在促进协同处理中起着重要作用。特别是，研究计划集中在三个主题：第一，设计，评估和实施的一个新的范例，移动代理为基础的范例，在传感器网络中实现协同处理。在这个模型中，而不是每个传感器节点sendinglocal信息到一个处理中心的集成，是典型的基于客户端/服务器的计算，集成代码被移动到传感器节点通过移动的代理。我们讨论了基于移动代理的协作处理在提供渐进的准确性，同时保持一定程度的容错的潜力。第二，开发和评估了一个分散的反应式分簇协议，以帮助适应不断变化的环境。由于部署的节点数量庞大，协作通常在同一集群内的节点之间进行。我们提出了一个分散的反应聚类（DRC）协议，其中的聚类过程中，只有当事件被检测到。从能量消耗和网络生命周期两个方面分析了网络性能增益。第三，网络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，并开发了分散式算法，实现了能源效率和带宽效率的优势，以及与集中式算法相比的检测性能增益。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，作为融合多学科特色的领域之一，将作为一个实验集中区，启动创新的课程设计，这有助于带来独特的功能，以提高新建立的计算机工程专业的质量和知名度的部门。尽管传感器网络在CSIP中显示出巨大的潜力，但其资源的极度受限在很大程度上限制了其实际应用。对CSIP的计算模型、协议和处理算法进行性能评估，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。研究和教育成果都将通过互联网上的Twiki协作平台提供。

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