• Management Science and Financial Engineering
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• v.17 no.2
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• pp.39-62
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• 2011

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.691-701
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• 2011

In rotatable directional sensor networks, maximizing network lifetime while covering all the targets and forwarding the sensed data to the sink is a challenge problem. In this paper, we address the Maximum Directional Cover Tree (MDCT) problem of organizing the directional sensors into a group of non-disjoint subsets to extend the network lifetime. Each subset in which the directional sensors cover all the targets and forward the sensed data to the sink is activated at one time. For the MDCT problem, we first present an energy consumption model which mainly takes into account the energy consumption for rotation work. We also develop the Directional Coverage and Connectivity (DCC)-greedy algorithm to solve the MDCT problem. To evaluate the algorithm, we conduct simulations and show that it can extend the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2658-2679
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• 2018

This paper proposes a novel Random Scheduling Algorithm based on Subregion Coverage (RSASC), to solve the SET K-cover problem (an NP-complete problem). SET K-cover problem distributes the set of sensors into the maximum number of mutually exclusive subsets (MESSs) in such a way that each of them can be scheduled for lifetime extension of WSN. Sensor coverage divides the target region into different subregions. RSASC first sorts the subregions in the ascending order concerning their sensor coverage. Then, it forms the subregion groups according to their similar sensor coverage. Lastly, RSASC ensures the K-coverage of each subregion from every group by randomly scheduling the sensors. We consider the target-coverage and area-coverage applications of WSN to analyze the usefulness of our proposed RSASC algorithm. The distinct quality of RSASC is that it utilizes less number of deployed sensors (33% less) to form the optimum number of MESSs with the higher computational speed (saves more than 93% of the time) as compared to the existing three algorithms.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.728-736
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• 2009

We propose a method using the genetic algorithm to solve the maximum set cover problem. It is needed for scheduling the power of sensor nodes in extending the lifetime of the wireless sensor network with variable sensing range. The existing Greedy Heuristic method calculates the power scheduling of sensor nodes repeatedly in the process of operation, and so the communication traffic of sensor nodes is increased. The proposed method reduces the amount of communication traffic of sensor nodes, and so the energies of nodes are saved, and the lifetime of network can be extended. The effectiveness of this method was verified through computer simulation, and considering the energy losses of communication operations about 10% in the network lifetime is improved.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.29-41
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• 2017

The estimation problem of expected time to failure of units is studied in a discrete set up. A simple step-stress accelerated life testing is considered with a Type-I censored sample from geometric distribution that is a commonly used distribution to model the lifetime of a device in discrete case. Maximum likelihood estimators as well as the associated distributions are derived. Exact, approximate and bootstrap approaches construct confidence intervals that are compared via a simulation study. Optimal confidence intervals are suggested in view of the expected width and coverage probability criteria. An illustrative example is also presented to explain the results of the paper. Finally, some conclusions are stated.