NeTS-NOSS: SNI: A General and Robust Networking Architecture for Distributed Data Processing in Sensor Networks

传感器网络推理（SNI）架构是第一个通用和强大的网络架构，专门为传感器网络中的推理而开发，能够在低成本无线传感器网络上快速部署各种复杂的大规模查询、数据处理和驱动任务。与之前大多数专注于此类推理任务（例如，跟踪或轮廓查找）的单个示例的方法不同，我们的基础设施被这些任务的强大抽象所利用，连接树，它能够有效地解决许多推理问题，包括概率推理（例如，传感器校准和目标跟踪），回归（例如，数据建模和轮廓查找）和优化(例如，执行器控制，决策，和模式分类)。SNI是通用且易于部署的：对各种复杂任务的有效抽象使新型传感器应用程序能够快速部署。此外，我们的方法是资源感知、高效和自适应的：节点具有有限的计算、通信和功率资源，因此SNI自动优化其通信模式以减少资源使用；这种优化是数据驱动的，因为高级任务的复杂性在很大程度上取决于被监视现象的当前状态。对节点和通信故障的鲁棒性是SNI的基本要素：低成本传感器容易出现有损通信、传感器和节点故障；我们的架构寻求提供理论和经验的稳健性保证。我们的评估过程包括在两个不同的试验台上进行彻底的测试，在不同的位置使用不同的硬件。该评估与我们的教育计划相结合，在本科和研究生课程中使用SNI。

Sensor Network Inference (SNI) architecture is the first general and robust networking architecture developed specifically for inference in sensor networks that enables the rapid deployment of a wide range of complex large-scale querying, data processing and actuation tasks on a low-cost wireless sensornet. Unlike most previous approaches that focus on individual examples of such inference tasks (e.g., tracking or contour finding), our infrastructure is leveraged by a powerful abstraction of such tasks, Junction Trees, which enables the efficient solution of many inference problems, including probabilistic inference (e.g., sensor calibration and target tracking), regression (e.g., data modeling and contour finding), and optimization (e.g., actuator control, decision-making, and pattern classification). SNI is general and easy to deploy: the effective abstraction for a wide range of complex tasks enables the rapid deployment of novel sensornets applications. Furthermore, our approach is resource aware, efficient and adaptive: nodes have limited computational, communication and power resources, thus SNI automatically optimizes its communication pattern to reduce resource usage; this optimization is data driven, since the complexity of the high-level task is greatly dependent on the current state of the monitored phenomena. Robustness to node and communication failures is a fundamental element of SNI: Low-cost sensornets are prone to lossy communication, sensor and node failures; our architecture seeks to provide both theoretical and empirical robustness guarantees. Our evaluation process includes thorough testing on two different testbeds, with different hardware, in different locations. This evaluation is coupled with our education plan by using SNI in undergraduate and graduate classes.