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

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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.