RR: Wireless Sensor Network Laboratory Infrastructure

本项目在传感器网络和分布式和集群计算领域进行研究，在传感器网络和无线网络上进行基于精度的查询处理实验。由于传感器设备由普通电池供电，因此在传感器网络中，电力是一种有限的资源。为了解决这一限制，提出了用于聚合查询的功率感知查询技术。而不是向用户提供精确的答案，引入了精度，让用户完全控制精度和能源使用之间的权衡。另一个项目研究基于商品组件构建的自组织存储集群的设计和实现，调查大型存储集群的性能和可用性，以及负载平衡和数据复制支持，适合于数据密集型应用程序。能够有效地跨设备分配计算的卸载技术也在研究之中，因为这些技术可以显著延长手持设备的使用寿命。还有一个项目研究如何有效地跨设备分配计算，从而节省系统的“虚拟电池寿命”。提高未来分区决策的效率。考虑到来自系统的反馈，技术应该能够“适应”系统中不断变化的条件。异构系统的卸载技术包括三类设备：传感器、移动设备和电力工作站。该研究寻求能够确定异构网络中节点提供的资源的轻量级服务发现解决方案。该基础设施支持以下领域的研究：传感器信息处理、传感器数据收集、基于集群的数据存储、基于传感器的系统组件的电源管理，以及传感器网络中的无线、服务和资源发现。提出的基础设施将用于在传感器网络上进行基于精度的查询处理实验。更广泛的影响：实验室基础设施提供了集成各种具有不同特性的传感设备的手段。学术研究和商业研究都将受益。软件工件和新课程方向的经验影响着学生的训练。

This project, conducting research in the areas of sensor networks and distributed and cluster computing, experiments with precision-based query processing over sensor networks and wireless networking. Since sensor devices are powered by ordinary batteries, power constitutes a limiting resource in sensor networks. To address this limitation, power-aware query techniques are proposed for aggregation queries. Instead of providing users with exact answers, precision is introduced, giving full control to the users of the tradeoff between precision and energy usage. Another project studies the design and implementation of a self-organizing storage cluster built upon commodity components, investigating performance and availability of large-scale storage clusters as well as load balancing and data replication support, suitable for data intensive applications. Off loading techniques that effectively partition computation across devices are also under study, since these techniques can significantly extend the life of hand-held devices. Still another project investigates techniques to effectively partition computation across devices conserving the "virtual battery life" of the system. improving the efficacy of future partitioning decisions. Considering feedback from the system, the techniques should be able to "adapt" to changing conditions in the system. The off-loading techniques for heterogeneous systems include three categories of devices: sensors, mobile devices, and power workstations. The research seeks lightweight service discovery solutions that can determine the resources provided by nodes in the heterogeneous network. The infrastructure enables research in the following areas: Sensor Information Processing, Sensor Data Collection, Cluster-Based Data Storage, Power Management of Sensor-Based System Components, and Wireless and Service and Resource Discovery in Sensor Networks.The proposed infrastructure will be used to experiment with precision-based query processing over sensor networks.Broader Impact: The lab infrastrucure provides means to integrate a variety of sensing devices with different characteristics. Academic, as well as commercial research will benefit. Software artifacts and experience into novel curricular directions impact the training of students.