Distributed Computing in Sensor Systems: 5th IEEE by Dominic Meier, Yvonne Anne Pignolet (auth.), Bhaskar

By Dominic Meier, Yvonne Anne Pignolet (auth.), Bhaskar Krishnamachari, Subhash Suri, Wendi Heinzelman, Urbashi Mitra (eds.)

The publication constitutes the refereed court cases of the 5th foreign convention on allotted Computing in Sensor structures, DCOSS 2009, held in Marina del Rey, CA, united states, in June 2009.

The 26 revised complete papers offered have been conscientiously reviewed and chosen from 116 submissions. The examine contributions during this lawsuits span many features of sensor structures, together with strength effective mechanisms, monitoring and surveillance, job popularity, simulation, question optimization, community coding, localization, program improvement, facts and code dissemination.

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Additional info for Distributed Computing in Sensor Systems: 5th IEEE International Conference, DCOSS 2009, Marina del Rey, CA, USA, June 8-10, 2009. Proceedings

Sample text

Our focus here, however, is on designing algorithms that provide near-optimal performance in an efficient, distributed manner. Some related problems involving cumulative probabilities are 30 H. Rowaihy et al. considered in [7], but those problems involve the product of task success probabilities instead of the sum. To our knowledge, we are the first to introduce the concept of fuzzy sensor location for sensor-task assignment problems. Related works in this area include [23], which addresses the issue of privacy when fusing data coming from sensors that are assigned to multiple event detection tasks, and [20], which describes a data dissemination technique to ensure that the locations of sensors in the network are not learned by an enemy.

The remaining part of this section describes the algorithms that the task leaders use to determine which sensors should be assigned to each task. As both problems as defined above are NP-hard, our algorithms are heuristic based. 2 Exact Location Algorithms In this subsection we propose algorithms to solve the sensor-task assignment problems, for detection and localization, when the exact locations of sensors are known. Event Detection Tasks In order to conserve energy we limit the number of sensors that can be assigned to a task to N , which is an application parameter.

Also, note that AG = 180◦ is not used in our experiments as sensors from the two sectors will be equivalent. As with fuzzy distance, finer location granularity leads to more sensor classes and hence higher computational overhead. Also, with finer granularity the task leader gains more information about a sensor’s location, which decreases privacy. 6 Performance Evaluation In this section we discuss experiments evaluating our algorithms. We implemented a simulator in Java and tested our algorithms on randomly generated problem instances.

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