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Data management in environmental monitoring sensor networks
Gupchup, Jayant The Johns Hopkins University 2012 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
Data gathered from multi-month to multi-year battery-powered environmental monitoring sensor networks present numerous challenges. This thesis explores three problems. First, design issues related to loading, storing and data integrity are studied in detail. An end-to-end system addressing these tasks by decoupling deployment-specific and deployment-independent phases is presented. This solution places a strong emphasis on the ability to trace the origins of every collected measurement for provenance and scientific reproducibility. Second, we explore the problem of assigning accurate global timestamps to the measurements collected using the motes' local clocks. In deployments lacking a persistent gateway, a data-driven approach is employed to assign timestamps to within 10 parts per million. Building on these experiences, we developed a novel low-power approach to accurately timestamp measurements in the presence of random, frequent mote reboots. The system is tested in simulation and on a real deployment in a Brazilian rain forest. It is able to achieve an accuracy in the order of seconds for more than 99% of measurements even when a global clock source is missing for days to months. Lastly, this thesis explores a generic data-driven approach to reduce communication costs in order to increase network lifetime. In particular, spatial correlation among sampling stations is leveraged to adaptively retrieve data from a small subset of informative sensors rather than all instrumented locations. Soil temperature data collected every half hour for four months from 50 locations is used to evaluate this system. The method achieves a factor of two reduction in collected data with a median error of 0.06°C and 95th percentile error of 0.325°C. This work is part of the Life Under Your Feet project developed at the Hopkins Inter-Networking Research (HiNRG) and eScience Labs at the Johns Hopkins University. At the time of writing, the data collected for this project is available at http://www.lifeunderyourfeet.org.