The coverageproblem is a fundamental problem for almost all applications in wireless sensor networks (WSNs). Many applications even impose the requirement of multilevel (k) coverage of the region of interest (ROI). I...
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The coverageproblem is a fundamental problem for almost all applications in wireless sensor networks (WSNs). Many applications even impose the requirement of multilevel (k) coverage of the region of interest (ROI). In this paper, we consider WSNs with uncertain properties. More precisely, we consider WSNs under the probabilistic sensing model, in which the detection probability of a sensor node decays as the distance between the target and the sensor node increases. The difficulty we encountered is that there is no unified definition of k-coverage under the probabilistic sensing model. We overcome this difficulty by proposing a "reasonable" definition of k-coverage under such a model. We propose a sensor deployment scheme that uses less number of deployed sensor nodes while ensuring good coverage qualities so that (i) the resultant WSN is connected and (ii) the detection probability satisfies a predefined threshold p(th), where 0 < p(th) < 1. Our scheme uses a novel "zone 1 and zone 1-2" strategy, where zone 1 and zone 2 are a sensor node's sensing regions that have the highest and the second highest detection probability, respectively, and zone 1-2 is the union of zones 1 and 2. The experimental results demonstrate the effectiveness of our scheme.
A critical issue for the k-coverageproblem in wireless sensor networks is how efficiently deploying sensors to cover an area of interest. In many critical scenarios such as in the military field, ensuring that each p...
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ISBN:
(纸本)9781450366458
A critical issue for the k-coverageproblem in wireless sensor networks is how efficiently deploying sensors to cover an area of interest. In many critical scenarios such as in the military field, ensuring that each point in the monitored area of interest is sufficiently covered can guaranty the effectiveness of intrusion detection for both monitoring and tracking applications. Prior research indicated that Mobile sensor Networks (MSNs) are capable of acting with great flexibility to enhance and cover holes appeared in certain regions when a sensor died due to limited energy and battery lifetime. In this paper, we consider the use of a strategy based on the collective motion mechanisms to relocate sensors nodes to achieve a higher k-coverage level. Each sensor node is able to compare its current k-coverage level with a predefined threshold so as to react dynamically by enabling a specific mobility behavior with a high priority. Based on this mobility behavior, a sensor node can move towards other sensors in its local neighborhood, and it would then be closer enough to them in order to enhance its k-coverage level and then it participates in achieving a higher k-coverage level for the whole group. Simulation results show the effectiveness of our considered approach in terms of the k-coverage level of 30 % as well as a significant improvement in energy consumption.
We study the sensor cover energy problem (SCEP) in wireless communicationa difficult nonconvex problem with nonconvex constraints. A local approach based on DC programming called DCA was proposed by Astorino and Migli...
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We study the sensor cover energy problem (SCEP) in wireless communicationa difficult nonconvex problem with nonconvex constraints. A local approach based on DC programming called DCA was proposed by Astorino and Miglionico (Optim Lett 10(2):355-368, 2016) for solving this problem. In the present paper, we propose a global approach to (SCEP) based on the theory of monotonic optimization. By using an appropriate reformulation of (SCEP) we propose an algorithm for finding quickly a local optimal solution along with an efficient algorithm for computing a global optimal solution. Computational experiments are reported which demonstrate the practicability of the approach.
One of the key research issues in wireless sensor networks (WSNs) is how sensors can efficiently be deployed to cover an area. In this paper, we solve the k-coveragesensor deployment problem to achieve multilevel (k)...
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One of the key research issues in wireless sensor networks (WSNs) is how sensors can efficiently be deployed to cover an area. In this paper, we solve the k-coveragesensor deployment problem to achieve multilevel (k) coverage of the area of interest I. We consider two subproblems: the k-coverage placement problem and the distributed dispatch problem. The placement problem asks how the minimum number of sensors required and their locations in I can be determined to guarantee that I is k-covered and the network is connected, while the dispatch problem asks how mobile sensors can be scheduled to move to the designated locations according to the result computed by the placement strategy if they are not in the current positions such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models and allow an arbitrary relationship between the communication distance and the sensing distance of sensors, thereby relaxing the limitations of existing results. For the dispatch problem, we propose a competition-based scheme and a pattern-based scheme. The competition-based scheme allows mobile sensors to bid for their closest locations, while the pattern-based scheme allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.
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