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mobicom-00 (1) - Research Challenges in Environmental...

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1 Introduction The availability of tremendous computation power coupled with widespread connectivity have fueled the development of real-time environmental observation and forecasting systems (EOFS). These systems couple real-time in-situ monitoring of physical processes with distribution networks that carry data to centralized processing sites. The processing sites run models of the physical pro- cesses, possibly in real-time, to predict trends or outcomes using on-line data for model tuning and verification. The forecasts can then be passed back into the physical monitoring network to adapt the monitoring with respect to expected conditions. For example, one could reposition sensors closer to the predicted source of a disturbance to improve sampling accuracy. EOFS have several unique characteristics that pose interesting challenges in the areas of wireless networking, systems, and mobile computing. In particular, these systems are large-scale, distributed embedded systems in which data primarily flows from remote sensors over wireless links to collec- tion points, and from these to centralized process- ing via wired links. The system supports a small number of concurrent applications, and like an embedded system can be tuned to meet the needs of the specific workload it is intended to support. The sensor stations can have cost, power, size, and weight constraints, the environment in which they This project was supported in part by the National Science Foundation grant CCR- 9876217, DARPA contracts/grants N66001-97- C-8522, and N66001-97-C-8523, and by Tek- tronix, Inc. and Intel Corporation. Early develop- ment of CORIE, a reference testbed for this paper, was partially funded by the Office of Naval Research (Grant N00014-96-1-0893) Research Challenges in Environmental Observation and Forecasting Systems David C. Steere * , Antonio Baptista ** , Dylan McNamee * , Calton Pu *** , and Jonathan Walpole * * Department of Computer Science and Engineering Oregon Graduate Institute ** Center for Coastal and Land Margin Research Oregon Graduate Institute *** College of Computing Georgia Institute of Technology Abstract We describe Environmental Observation and Forecasting Systems (EOFS), a new class of large-scale distributed system designed to monitor, model, and forecast wide-area physical processes such as river sys- tems. EOFS have strong social relevance in areas such as education, transportation, agriculture, natural resource planning and disaster response. In addition, they represent an opportunity for scientists to study large physical systems to an extent that was not previously possible. Building the next generation of EOFS pose a number of difficult challenges in all aspects of wireless networking, including media protocols for long distance vertical communication through water, flooding algorithms in ad-hoc network topologies, support for rate- and time-sensitive applications, and location-dependent mobile computing.
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run is variable, and the stations may be capable of changing their location. Typically the greater the
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