Collaborative Research: CSR--EHS: Obtaining Realistic Communication and Sensing In-situ Models for Wireless Embedded Systems

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With the continuing advancement of MEMS technology, wireless embedded systems are deployed in various kinds of environments from well-controlled laboratories to turbulent ocean floors. System design has made simplifying and sometimes unrealistic assumptions about the resulting communication and sensing patterns. Although embedded devices are normally micro-calibrated individually before deployment, preliminary results indicate that the deployment environment is a dominating factor in communication and sensing characteristics of embedded devices. This research aims at developing a wide spectrum of modeling methodologies and related protocols for large-scale embedded systems under realistic environments. The main objective of this project lies in developing three novel modeling approaches, which complement each other and cover a large cost-benefit design space. The first is to develop a new radio irregularity model based on concepts of degree of irregularity and variance of signaling power. The second seeks a capability for abstraction of a completely repeatable physical environment, using an automated capture-and-replay process. The third features a novel way to use training events in a controlled manner to produce non-parametric realistic sensing and communication patterns. A key challenge for in-situ modeling lies in reconciling the conflict between the in-situ modeling accuracy and the related cost to build and use these models in resource-limited large-scale embedded systems. This project seeks to develop the models from the micro to macro levels where designers can choose the appropriate level of detail based on the accuracy needs, and also the models from parametric to non-parametric types where designers can choose the models with proper costs based on the available resources. The models will be available via common simulation systems, enabling embedded systems designers to develop solutions based on realism and avoid an all-to-common problem found today where solutions developed by simulation don't work in the real world. This, in turn, is expected to have a major impact on embedded systems by saving development time and money and resulting in more efficient, robust, predictable and controllable systems. Research results will be evaluated using traffic-monitoring and management testbeds at the University of Minnesota and the University of Virginia.