NeTS: Large:Collaborative Research: Context-Driven Management of Heterogeneous Sensor Networks

无线传感器网络（WSNs）由智能传感器通过无线链路互连而成，正迅速成为真实的实时监测地理上分布的物理、化学或生物现象的首选技术。在环境监测、监视、污染控制和侦察应用中遇到的动态WSN环境，需要根据用户请求和环境变化对WSN资源进行响应性管理，并将其自适应分配给传感、网络支持、定位和规划任务。然而，对于诸如原始数据的分辨率、延迟、网络重新配置延迟和稳态中的资源利用率之类的标准，应当确保指定的服务质量。该项目开发了一种集成的跨层方法，用于无线传感器网络中的网络、数据库、控制、移动性管理和信息处理。特别是，上下文感知和节能的解决方案，追求机会主义的传感和处理技术的基础上，动态索引结构，新的查询语言结构，反应式移动性控制算法，并基于分布式压缩路由algorithm.The技术进步，从这项研究将显着简化部署的无线传感器网络，并导致新的上下文感知的应用。这些进展将直接受益于应急响应管理、环境威胁补救和生物栖息地监测等领域。除了开发所需的算法外，该项目还将使用物理设备实现仿真平台和监控环境。这些平台将为学生提供新的教育机会，积极探索资源有限环境中的信息获取和资源管理。所有项目资源将通过项目网页与公众共享。

Wireless sensor networks (WSNs) composed of smart sensors interconnected over wireless links are quickly becoming the technology of choice for monitoring and measuring geographically distributed physical, chemical, or biological phenomena in real time. Dynamic WSN environments encountered in environmental monitoring, surveillance, pollution control, and reconnaissance applications, require responsive management of WSN resources and their adaptive allocation to sensing, networking support, localization, and planning tasks, based on user requests and changes in the environment. A specified quality of service should however be ensured for criteria such as resolution of the raw-data, latency, network reconfiguration delay, and resource utilization in the steady-state. This project develops an integrated cross-layered approach to networking, databases, control, mobility management, and information processing in WSNs. In particular, context-aware and energy-efficient solutions are pursued that are based on opportunistic sensing and processing techniques, dynamic indexing structures, novel query language constructs, reactive mobility control algorithms, and distributed compression based routing algorithms.The technological advances from this research will significantly simplify the deployment of WSNs and lead to novel context-aware applications. The advances will directly benefit domains such as emergency-response management, environmental threat remediation, and biological habitat monitoring. Apart from developing the required algorithms, the project will implement simulation platforms and a monitoring environment using physical devices. The platforms will provide students with new educational opportunities to actively explore information acquisition and resource management in resource-constrained environments. All project resources will be shared with the public through a project webpage.