Dr. Sumeet Dua

Max P. & Robbie L. Watson Eminent Scholar Chair

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Ankur Rajopadhye (2006)

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Optimized Greedy Algorithm Based Sensor Placement for Distributed Sensor Network; MS-CS Thesis, Student: Ankur Rajopadhye (2006).

Efficient sensor deployment is a critical issue which directly influences the cost and quality of any sensor network. Challenges in efficient sensor placement include power efficiency, maximum network life expectancy, pervasive coverage, connectivity optimization, and, taking all of these factors into account, cost optimization. In this work, a sensor placement algorithm for optimizing the number and locations of sensors to completely cover a sensor field is proposed. The framework assumes a probabilistic sensor detection model, considering the inherent uncertainty involved in sensor detections. Preferential coverage of vulnerable regions in the sensor field is desired for certain mission critical applications like battlefield surveillance. Such preferential regions are modeled as multiple arbitrary shaped regions in the sensor field. Obstacles like buildings, trees, and hills which can be present anywhere in the sensor field, including preferential regions, are modeled as multiple irregular shaped regions. Preferential coverage of n regions in the sensor field with an n-tier probability criterion proves the effectiveness of the proposed algorithm.
An Optimized Greedy Algorithm for efficient deployment of sensors in a sensor field containing multiple obstacles and preferential regions is proposed. The approach is based on placing sensors at pre-calculated distances in horizontal and vertical directions if the sensor location does not fall within preferential regions. The greedy algorithm with an optimization phase (pruning phase) is then used to find the best sensor locations to cover the leftover uncovered region.
The results prove that the proposed algorithm uses fewer sensors than MAX_AVG_COV, MAX_MIN_COV [13], and random placement algorithms.

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