Coverage-aware sensor engagement in dense sensor networks.

J. Embedded Computing 01/2009; 3:3-18.
Source: DBLP
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Available from: Tatsuya Suda, Aug 30, 2015
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    ABSTRACT: Wireless sensor networks (WSNs) are emerging as an effective means for environment monitoring. This paper investigates a strategy for energy efficient monitoring in WSNs that partitions the sensors into covers, and then activates the covers iteratively in a round-robin fashion. This approach takes advantage of the overlap created when many sensors monitor a single area. Our work builds upon previous work in "Power Efficient Organization of Wireless Sensor Networks" by Slijepcevic and Potkonjak, where the model is first formulated. We have designed three approximation algorithms for a variation of the SET K-COVER problem, where the objective is to partition the sensors into covers such that the number of covers that include an area, summed over all areas, is maximized. The first algorithm is randomized and partitions the sensors, in expectation, within a fraction 1 - 1/e (~.63) of the optimum. We present two other deterministic approximation algorithms. One is a distributed greedy algorithm with a 1/2 approximation ratio and the other is a centralized greedy algorithm with a 1 - 1/e approximation ratio. We show that it is NP-Complete to guarantee better than 15/16 of the optimal coverage, indicating that all three algorithms perform well with respect to the best approximation algorithm possible. Simulations indicate that in practice, the deterministic algorithms perform far above their worst case bounds, consistently covering more than 72% of what is covered by an optimum solution. Simulations also indicate that the increase in longevity is proportional to the amount of overlap amongst the sensors. The algorithms are fast, easy to use, and according to simulations, significantly increase the longevity of sensor networks. The randomized algorithm in particular seems quite practical.
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    ABSTRACT: Wireless ad-hoc sensor networks will provide one of the missing connections between the Internet and the physical world. One of the fundamental problems in sensor networks is the calculation of coverage. Exposure is directly related to coverage in that it is a measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of time. In addition to the informal definition, we formally define exposure and study its properties. We have developed an efficient and effective algorithm for exposure calculation 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 works for any given distribution of sensors, sensor and intensity models, and characteristics of the network. It provides an unbounded level of accuracy as a function of run time and storage. We provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation.
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    ABSTRACT: For many sensor network applications such as military surveillance, it is necessary to provide full sensing coverage to a security-sensitive area while at the same time minimize energy consumption and extend system life by leveraging the redundant deployment of sensor nodes. It is also preferable for the sensor network to provide differentiated surveillance service for various target areas with different degrees of security requirements. In this paper, we propose a differentiated surveillance service for sensor networks based on an adaptable energy-efficient sensing coverage protocol. In the protocol, each node is able to dynamically decide a schedule for itself to guarantee a certain degree of coverage (DOC) with average energy consumption inversely proportional to the node density. Several optimizations and extensions are proposed to provide even better performance. Simulation shows that our protocol accomplishes differentiated surveillance with low energy consumption. It outperforms other state-of-art schemes by as much as 50% reduction in energy consumption and as much as 130% increase in the half-life of the network. Keywords: Sensor Networks, Sensing Coverage, Energy Conservation, Differentiated Surveillance 1.
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