Difference between revisions of "Megerian2002"
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|abstract=Wireless ad hoc sensor networks have the potential to provide the missing interface between the physical world and the Internet, thus impacting a large number of users. This connection will enable computational treatments of the physical world in ways never before possible. In this far reaching scenario, Quality of Service can be expressed in terms of accuracy and/or latency of observing events and the overall state of the physical world. Consequently, one of the fundamental problems in sensor networks is the calculation of coverage, which can be defined as a measure of the ability to detect objects within a sensor filed. Exposure is directly related to coverage in that it is an integral measure of how well the sensor network can observe an object, moving on an arbitrary path, over a period of time. After elucidating the importance of exposure, we formally define exposure and study its properties. We have developed an efficient and effective algorithm for exposure calculations in sensor networks, specifically for finding minimal exposure paths. The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks. The algorithm can be applied to any given distribution of sensors, sensor and sensitivity models, and characteristics of the network. Furthermore, it provides an unbounded level of accuracy as a function of run time and storage. Finally, we provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation. | |abstract=Wireless ad hoc sensor networks have the potential to provide the missing interface between the physical world and the Internet, thus impacting a large number of users. This connection will enable computational treatments of the physical world in ways never before possible. In this far reaching scenario, Quality of Service can be expressed in terms of accuracy and/or latency of observing events and the overall state of the physical world. Consequently, one of the fundamental problems in sensor networks is the calculation of coverage, which can be defined as a measure of the ability to detect objects within a sensor filed. Exposure is directly related to coverage in that it is an integral measure of how well the sensor network can observe an object, moving on an arbitrary path, over a period of time. After elucidating the importance of exposure, we formally define exposure and study its properties. We have developed an efficient and effective algorithm for exposure calculations in sensor networks, specifically for finding minimal exposure paths. The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks. The algorithm can be applied to any given distribution of sensors, sensor and sensitivity models, and characteristics of the network. Furthermore, it provides an unbounded level of accuracy as a function of run time and storage. Finally, we provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation. | ||
|pages=443 - 454 | |pages=443 - 454 | ||
|month= | |||
|year=2002 | |||
|volume=8 | |volume=8 | ||
|journal=ACM Journal of Wireless Networks | |journal=ACM Journal of Wireless Networks | ||
|title=Exposure In Wireless Sensor Networks: Theory And Practical Solutions | |title=Exposure In Wireless Sensor Networks: Theory And Practical Solutions | ||
|entry=article | |entry=article | ||
}} | }} | ||
Revision as of 03:28, 4 September 2021
| Megerian2002 | |
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| author | S. Megerian and F. Koushanfar and G. Qu and G. Veltri and M. Potkonjak |
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| journal | ACM Journal of Wireless Networks |
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| pages | 443 - 454 |
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| title | Exposure In Wireless Sensor Networks: Theory And Practical Solutions |
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| volume | 8 |
| year | 2002 |
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Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093