Distributed Composition Services for Self-Organizing Sensor Network Applications

自组织传感器网络应用的开发可以通过分布式组合服务与分布式适配和查找服务相配合来简化，从而使传感器节点能够自我感知、自我配置并对实时变化做出响应。本研究致力于开发分布式组合服务的新概念、体系结构和机制，以支持自组织应用的持续运行，以满足在自组织部署、频繁节点故障、动态重构和移动性的情况下的按需需求。自组织服务的用途将在协作传感器融合应用中展示，用于检测、分类和跟踪移动对象，以及分布式传感器-执行器控制。试验台是一个由传感器节点组成的自组织网络，这些节点配备了嵌入式处理器、射频通信、多模式传感器和执行器。传感器节点被扩展为可重新配置的智能组件，具有即兴联网、自组装、动态适应设备故障和降级、节点移动性以及任务和网络需求变化的能力。它们使用的创新技术包括：(1)用于自组织传感器应用的服务和机制，(2)可扩展的分层组合服务，(3)轻量级可重构的通信机制，以及(4)在基于事件的数据中心网络上实现分布式服务。这项研究的更广泛影响包括开发高级入门课程课程和创新的传感器网络试验台和实验室，用于研究生和本科生，包括少数族裔学生的教育和研究培训。

Development of self-organizing sensor network applications can be simplified with distributed composition services that cooperate with distributed adaptation and lookup services to enable sensor nodes to be self-aware, self-configurable, and responsive to real-time changes. This research focuses on developing novel concepts, architecture and mechanisms for distributed composition services that support continuous operation of self-organizing applications to meet on-demand needs in spite of ad-hoc deployment, frequent node failures, dynamic reconfiguration, and mobility. The utility of the self-organizing services will be demonstrated in collaborative sensor fusion applications for detecting, classifying and tracking moving objects and distributed sensor-actuator control. The testbed is an ad-hoc network of sensor nodes that are equipped with embedded processors, RF communication, multi-mode sensors and actuators. Sensors nodes are augmented as reconfigurable smart components with capabilities for impromptu networking, self-assembly, dynamically adaptation to device failure and degradation, node mobility, and changes in task and network requirements. They use innovative techniques including: (1) Services and mechanisms for self-organizing sensor applications, (2) scalable hierarchical composition services, (3) lightweight reconfigurable communication mechanisms, and (4) implementation of distributed services over event-based data-centric networks. Broader impacts of this research include the development of advanced and introductory course curriculum and innovative sensor network testbeds and laboratories for education and research training of graduate and undergraduate students including minority students.