MOdel-based approaches for enhancing wireless sensor network usability, programmability, reliability and efficiency

虽然无线传感器网络（WSNs）提供了巨大的潜力，作为一种监测工具，他们目前是令人生畏的复杂程序和使用。 许多类型的传感器数据需要纳入一个模型，然后才能成为有用的观察的基础上，我建议将它们与明确的物理模型和决策上下文，以降低复杂性，提高能源经济性和可靠性的无线传感器网络。 基于模型的物理环境和无线传感器网络的响应规范将大大降低应用程序实现的复杂性。 将显式模型扩展到WSN应用程序开发中还允许模型编译器具有自动将模型相关计算分发到WSN中的节点所需的信息。 这种基于模型的无线传感器网络不是将所有传感器数据中继出网络进行外部处理，而是负责以分布式方式内部跟踪和报告物理环境状态的“最佳猜测”。 网络内模型执行将通过减少通信来提高电力经济性，并通过减少对与外部中央系统的通信的依赖来提高监控应用程序的鲁棒性。 我们计划提供面向用户的数据库接口，编程语言和相关的编译器的流行类的应用程序，并支持库需要支持以模型为中心的应用程序开发。 TinyDB无线传感器网络数据库系统将被适配为允许关于模型状态和输出的临时或定期用户查询，使用从模型域和自然概念（例如时间和可能性）提取的维度。 在本项目的后期阶段，我们将扩展所涉及的模型并引入决策上下文模型。 通过将对数据对决策需求的影响的理解纳入其中，这种整合将改善能源和传感器数据之间的权衡。 我们希望这项工作能够大大降低在设施、工业过程、农场和民用结构中部署基于无线传感器的监控的成本效益障碍，从而造福社会。通过提高无线传感器网络的重要子类的可用性，可编程性和功率效率，这些项目应该促进有效的监控应用程序的开发。 在涉及设施、工业过程、农场和土木结构监测的应用中，做出贡献的机会特别大。

While wireless sensor networks (WSNs) offer great potential for use as a monitoring tool, they are currently dauntingly complex to program and use.  Building on the observation that many types of sensor data require incorporation into a model before they can become useful, I propose to reduce complexity and improve power economy and reliability in WSNs by integrating them with explicit models of physical and decision context.  Model-based specification of the physical environment and a WSN's response to it will greatly reduce application implementation complexity.  Incorporating explicit models into WSN application development also permits a model compiler with the information required to automatically distribute model-related computation to nodes across the WSN.  Rather than relaying all sensor data out of the network for external processing, such model-based WSNs will instead be responsible for internally tracking and reporting on a "best guess" of the state of the physical environment in a distributed fashion.  In-network model execution will improve power economy by reducing communication, and increase monitoring applications' robustness by lessening reliance on communication to an external central system.       We plan to provide user-oriented database interfaces, programming languages and associated compilers for popular classes of applications, and supporting libraries needed to support model-centric applications development.  The TinyDB WSN database system will be adapted to allow ad hoc or periodic user queries regarding model state and output using dimensions drawn from the model domain and natural concepts such as time and likelihoods.  In the later stages of this project, we will expand the models involved and introduce models of decision context.  By incorporating an understanding of the implications of data for decision-making needs, this integration will permit improved tradeoffs between energy and sensor data.      We expect this work to benefit society by substantially lowering the barriers to cost-effective deployment of wireless sensor-based monitoring in facilities, industrial processes, farms, and civil structures. By enhancing the usability, programmability, and power efficiency of important subclasses of WSNs, these projects should facilitate the development of effective monitoring applications.  Opportunities for contribution are particularly strong in applications involving the monitoring of facilities, industrial processes, farms, and civil structures.