NeTS-NOSS: Exploiting Mobility for Information Exchange and Collaborative Decision-Making in Sensor Networks

该项目的主要目标是开发所需的架构和算法，利用节点的移动性，以提高传感器网络的性能。它是基于这样的前提下，移动性可以是一个重要的资产，在传感器网络中的信息交换和协作决策。该项目设想了一个具有四种不同类型的节点移动性的传感器网络：（i）无移动性，（ii）非协调移动性，（iii）预先计划的移动性和（iv）自适应移动性。每种类型的移动性都提供了一组不同的机会和挑战，以优化满足协作决策准确性要求所需的带宽和能耗。该项目将开发自适应传感和相关的信息交换策略，以实现这种优化。该项目的预期成果将包括：（i）用于有效支持传感器网络中的信息交换所需的通信模式的基本集合的算法，（ii）用于路径规划、数据采样和信息融合的自适应算法，用于选定的协作决策问题，例如源定位和场估计，（iii）延迟、能量、带宽的表征，和决策的准确性权衡的新算法，及（iv）演示的有效性，新算法通过模拟研究和实验的数据从一个试验台。预期的结果有可能影响社会的许多领域，包括智能交通系统，国土安全，医疗，公共安全和环境。结果的传播将通过会议和期刊上的出版物以及麦迪逊的威斯康星州大学的本科生和研究生课程进行。

The primary goal of the project is to develop the architectures and the algorithms needed to exploit mobility of nodes to enhance the performance of sensor networks. It is based on the premise that mobility can be a major asset for both information exchange and collaborative decision-making in sensor networks. The project envisions a sensor network with four different types of node mobility: (i) no mobility, (ii) uncoordinated mobility, (iii) pre-planned mobility, and (iv) adaptive mobility. Each type of mobility offers a different set of opportunities and challenges with respect to optimizing the bandwidth and energy consumption needed to meet the accuracy requirements of collaborative decision-making. The project will develop adaptive sensing and the associated information exchange strategies to carry out this optimization. The expected results from the project will include: (i) algorithms for a base set of communication patterns needed to efficiently support information exchange in sensor networks, (ii) adaptive algorithms for path planning, data sampling, and information fusion for selected collaborative decision-making problems such as source localization and field estimation, (iii) characterization of latency, energy, bandwidth, and decision accuracy trade-offs in the new algorithms, and (iv) demonstration of the effectiveness of the new algorithms through simulation studies and experimentation with data from a testbed. The expected results have the potential to impact many areas of society including intelligent transport systems, homeland security, medicine, public safety, and environment. Dissemination of the results will occur through publications in conferences and journals and through undergraduate and graduate courses at University of Wisconsin, Madison.