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.
