NetSE: Small: Activity-Aware Sensor Network for Smart Environments

智能环境包含许多高度交互和嵌入式设备，以及自动控制这些设备以满足环境需求的能力。虽然智能环境提供了许多社会效益，但它们也为无线网络设计带来了新的复杂挑战。一个典型的家庭可能配备数百或数千个无线传感器，以帮助确保其居民的健康、安全和生产力。如果这些传感器持续在全警戒模式下工作，它们将消耗大量的能量和带宽。其结果是昂贵的基础设施需要不断维护以更换电池并确保服务质量。该项目的目标是为这种无线传感器网络注入认知能力和环境感知能力，使它们能够以更智能的方式行动。首席研究员（pi）将使用机器学习技术来识别智能环境中正在执行的活动。然后，这些上下文信息将被传送到网络，使传感器节点能够智能地决定何时休眠、何时唤醒以及如何路由信息。通过将传感器网络转换为活动感知传感器网络，研究人员假设它们将大大降低能量和带宽消耗。该项目的贡献包括：1)增强现有算法，以识别不完整、交错或由多个居民并行执行的活动；2)设计并实现一种算法，使每个传感器能够智能地决定采样率和睡眠/唤醒时间；3)在模拟和两个物理智能环境测试平台中测试算法。所有合成的和真实的数据集将连同源代码一起在互联网上传播，以促进社区范围内的比较和协作。更广泛的影响：该研究增强了具有认知能力和上下文感知的普适系统。通过将传感器网络与智能推理和学习能力相结合，pi提出了一种范例，可用于创建创新的智能传感器网络，维持智能环境，并提高智能环境居民的生活质量。

A smart environment contains many highly interactive and embedded devices as well as the ability to control these devices automatically in order to meet the demands of the environment. While smart environments offer many societal benefits, they also introduce new and complex challenges for wireless network design. A typical home may be equipped with hundreds or thousands of wireless sensors that aid in ensuring the health, safety, and productivity of its residents. If these sensors are continuously operating in full-alert mode, they will expend a great deal of energy and bandwidth. The result is an expensive infrastructure that requires constant maintenance to replace batteries and ensure quality-of-service. The goal of this project is to imbue such wireless sensor networks with cognitive capabilities and context awareness that will allow them to act in a more intelligent manner. The principal investigators (PIs) will use machine learning techniques to recognize activities that are being performed in the smart environment. This context information will then be conveyed to the network to allow sensor nodes to intelligently decide when to sleep, when to wake up, and how to route information. By transforming sensor networks into activity-aware sensor networks, the researchers hypothesize that they will greatly reduce energy and bandwidth consumption. The contributions of this project include: 1) enhance existing algorithms to recognize activities that are incomplete, interleaved, or performed in parallel by multiple residents, 2) design and implement an algorithm that will allow each sensor to intelligently decide sampling rates and sleep/wake times, and 3) test the algorithms in simulation and in two physical smart environment testbeds. All of the synthetic and real-world datasets will be disseminated, together with the source code, over the Internet to facilitate community-wide comparison and collaboration.Broader Impact: The research enhances pervasive systems with cognitive capabilities and context awareness. By partnering sensor networks with intelligent reasoning and learning capabilities, The PIs are proposing a paradigm that can be used to create innovative intelligent sensor networks, sustain smart environments, and improve the quality of life for residents of smart environments.