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Journal articles on the topic 'Coverage problem'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Murray, Alan T. "Maximal Coverage Location Problem." International Regional Science Review 39, no. 1 (December 9, 2015): 5–27. http://dx.doi.org/10.1177/0160017615600222.

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2

Zhang, Guo-Qiang, and Licong Cui. "A set coverage problem." Information Processing Letters 110, no. 4 (January 2010): 158–59. http://dx.doi.org/10.1016/j.ipl.2009.11.012.

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3

Khuller, Samir, Anna Moss, and Joseph (Seffi) Naor. "The budgeted maximum coverage problem." Information Processing Letters 70, no. 1 (April 1999): 39–45. http://dx.doi.org/10.1016/s0020-0190(99)00031-9.

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4

Chi, D. T., and Y. T. Su. "On a satellite coverage problem." IEEE Transactions on Aerospace and Electronic Systems 31, no. 3 (July 1995): 891–96. http://dx.doi.org/10.1109/7.395249.

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5

Davoodi, Mansoor, and Ali Mohades. "Solving the constrained coverage problem." Applied Soft Computing 11, no. 1 (January 2011): 963–69. http://dx.doi.org/10.1016/j.asoc.2010.01.016.

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6

Cohen, Reuven, and Liran Katzir. "The Generalized Maximum Coverage Problem." Information Processing Letters 108, no. 1 (September 2008): 15–22. http://dx.doi.org/10.1016/j.ipl.2008.03.017.

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7

Shakhatreh, Hazim, Abdallah Khreishah, and Issa Khalil. "Indoor Mobile Coverage Problem Using UAVs." IEEE Systems Journal 12, no. 4 (December 2018): 3837–48. http://dx.doi.org/10.1109/jsyst.2018.2824802.

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8

SHAHABUDIN, S. H. "Content coverage in problem-based learning." Medical Education 21, no. 4 (July 1987): 310–13. http://dx.doi.org/10.1111/j.1365-2923.1987.tb00369.x.

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9

Levinson, Gershon, and Sol Shnider. "Obstetric Anesthesia Coverage-A Continuing Problem." Anesthesiology 65, no. 3 (September 1, 1986): 245–46. http://dx.doi.org/10.1097/00000542-198609000-00001.

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10

Shetty, Bala, Rathindra Sarathy, and Arun Sen. "The K-coverage concentrator location problem." Applied Mathematical Modelling 16, no. 2 (February 1992): 94–100. http://dx.doi.org/10.1016/0307-904x(92)90086-i.

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11

Deng, Xiu, Jiguo Yu, Dongxiao Yu, and Congcong Chen. "Transforming Area Coverage to Target Coverage to Maintain Coverage and Connectivity for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 10 (October 1, 2012): 254318. http://dx.doi.org/10.1155/2012/254318.

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Area coverage is one of the key issues for wireless sensor networks. It aims at selecting a minimum number of sensor nodes to cover the whole sensing region and maximizing the lifetime of the network. In this paper, we discuss the energy-efficient area coverage problem considering boundary effects in a new perspective, that is, transforming the area coverage problem to the target coverage problem and then achieving full area coverage by covering all the targets in the converted target coverage problem. Thus, the coverage of every point in the sensing region is transformed to the coverage of a fraction of targets. Two schemes for the converted target coverage are proposed, which can generate cover sets covering all the targets. The network constructed by sensor nodes in the cover set is proved to be connected. Compared with the previous algorithms, simulation results show that the proposed algorithm can prolong the lifetime of the network.
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12

Song, Zhiming, Xiangyun Hu, Maocai Wang, and Guangming Dai. "Judgement Theorems and an Approach for Solving the Constellation-to-Ground Coverage Problem." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/5097324.

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The satellite constellation-to-ground coverage problem is a basic and important problem in satellite applications. A group of judgement theorems is given, and a novel approach based on these judgement theorems for judging whether a constellation can offer complete single or multiple coverage of a ground region is proposed. From the point view of mathematics, the constellation-to-ground coverage problem can be regarded as a problem entailing the intersection of spherical regions. Four judgement theorems that can translate the coverage problem into a judgement about the state of a group of ground points are proposed, thus allowing the problem to be efficiently solved. Single- and multiple-coverage problems are simulated, and the results show that this approach is correct and effective.
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13

Arivudainambi, D., S. Balaji, B. Sripathy, and P. Vijayaraju. "Enhancing Quality of Coverage for Target Coverage Problem Using Discrete Haar Wavelet." Wireless Personal Communications 101, no. 4 (May 8, 2018): 1817–37. http://dx.doi.org/10.1007/s11277-018-5792-4.

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14

Narasimhan, Sridhar. "The concentrator location problem with variable coverage." Computer Networks and ISDN Systems 19, no. 1 (September 1990): 1–10. http://dx.doi.org/10.1016/0169-7552(90)90114-8.

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15

Moulton, Vincent, and Andreas Spillner. "Phylogenetic diversity and the maximum coverage problem." Applied Mathematics Letters 22, no. 10 (October 2009): 1496–99. http://dx.doi.org/10.1016/j.aml.2009.03.017.

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16

Farbstein, Boaz, and Asaf Levin. "Maximum coverage problem with group budget constraints." Journal of Combinatorial Optimization 34, no. 3 (December 7, 2016): 725–35. http://dx.doi.org/10.1007/s10878-016-0102-0.

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17

Mini, S., Siba K. Udgata, and Samrat L. Sabat. "M-Connected Coverage Problem in Wireless Sensor Networks." ISRN Sensor Networks 2012 (March 29, 2012): 1–9. http://dx.doi.org/10.5402/2012/858021.

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Solving coverage problem alone is not adequate in a wireless sensor network, since data has to be transmitted to the base station. This leads to the lookout for an energy efficient method to solve connected coverage problem. This paper addresses M-connected (each sensor node will have at least M other sensor nodes within its communication range) target coverage problem in wireless sensor networks, where the required level of connectivity and coverage may be high or low as required. We propose a heuristic for M-connected target coverage problem, where initially a cover is decided and later on it is checked for M-connectivity. M-connectivity for simple coverage, k-coverage, and Q-coverage is focussed on in this paper. We use a Low-Energy Adaptive Clustering Hierarchy (LEACH) inspired model, where a cluster is considered as a set of sensor nodes satisfying M-connectivity and required level of coverage. It is enough if one among these nodes transmits the monitored information to the base station. When the required level of coverage are high, chances of nodes being connected in the cover is high. Simulation results show that our proposed method can achieve better results than Communication Weighted Greedy Cover (CWGC).
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18

CHATTERJEE, KRISHNENDU, LUCA DE ALFARO, and RUPAK MAJUMDAR. "THE COMPLEXITY OF COVERAGE." International Journal of Foundations of Computer Science 24, no. 02 (February 2013): 165–85. http://dx.doi.org/10.1142/s0129054113400066.

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We study the problem of generating a test sequence that achieves maximal coverage for a reactive system under test. We formulate the problem as a repeated game between the tester and the system, where the system state space is partitioned according to some coverage criterion and the objective of the tester is to maximize the set of partitions (or coverage goals) visited during the game. We show the complexity of the maximal coverage problem for non-deterministic systems is PSPACE-complete, but is NP-complete for deterministic systems. For the special case of non-deterministic systems with a re-initializing “reset” action, which represent running a new test input on a re-initialized system, we show that the complexity is coNP-complete. Our proof technique for reset games uses randomized testing strategies that circumvent the exponentially large memory requirement of deterministic testing strategies. We also discuss the memory requirement for deterministic strategies and extensions of our results to other models, such as pushdown systems and timed systems.
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19

Eydi, Alireza, and Javad Mohebi. "Modeling and solution of maximal covering problem considering gradual coverage with variable radius over multi-periods." RAIRO - Operations Research 52, no. 4-5 (October 2018): 1245–60. http://dx.doi.org/10.1051/ro/2018026.

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Facility location is a critical component of strategic planning for public and private firms. Due to high cost of facility location, making decisions for such a problem has become an important issue which have gained a large deal of attention from researchers. This study examined the gradual maximal covering location problem with variable radius over multiple time periods. In gradual covering location problem, it is assumed that full coverage is replaced by a coverage function, so that increasing the distance from the facility decreases the amount of demand coverage. In variable radius covering problems, however, each facility is considered to have a fixed cost along with a variable cost which has a direct impact on the coverage radius. In real-world problems, since demand may change over time, necessitating relocation of the facilities, the problem can be formulated over multiple time periods. In this study, a mixed integer programming model was presented in which not only facility capacity was considered, but also two objectives were followed: coverage maximization and relocation cost minimization. A metaheuristic algorithm was presented to solve the maximal covering location problem. A simulated annealing algorithm was proposed, with its results presented. Computational results and comparisons demonstrated good performance of the simulated annealing algorithm.
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20

Javan Bakht, Ahmad, Homayun Motameni, and Hosein Mohamadi. "A learning automata-based algorithm to solve imbalanced k-coverage in visual sensor networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2817–29. http://dx.doi.org/10.3233/jifs-191170.

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One of the most important problems in directional sensor networks is k-coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k-coverage problem). Due to the significance of solving the imbalanced k-coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k-coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.
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21

Lane, Julie B. "Women Are a Problem." Communication & Sport 6, no. 1 (December 28, 2016): 25–40. http://dx.doi.org/10.1177/2167479516685578.

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Media coverage of Title IX over the past several decades has both praised the law and the achievements of female athletes who have benefited from it and highlighted claims that men’s college sports have been the unanticipated victims of the effort to increase opportunities for women. This study sought to understand how coverage of the debate in 1974–1975 over the Title IX regulations helped shape discourse about the law with regard to intercollegiate athletics. Through a combination of archival research and qualitative media analysis, I identified arguments made by Title IX critics and advocates and analyzed coverage of the debate in the New York Times and the Washington Post, paying particular attention to the presence or absence of what Dunja Antunovic called conflict and celebratory narratives. I found that conflict narratives that reflected concerns of Title IX critics overwhelmed celebratory narratives as well as anticommercialism narratives that I also detected. I concluded that these newspapers allowed critics, led by the National Collegiate Athletic Association, to shape the discourse about the meaning of Title IX and its consequences, thereby reinforcing male dominance of the American sport culture and missing an opportunity to question the commercialization of intercollegiate athletics.
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22

Hüsler, Jürg. "Maximal, non-uniform spacings and the coverage problem." Journal of Applied Probability 25, no. 3 (September 1988): 519–28. http://dx.doi.org/10.2307/3213981.

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The spacings have been widely applied to many situations, such as for example the coverage problem of the circle or the line, under the hypothesis of uniformity. The paper examines the asymptotic behaviour of the maximal spacings under non-uniformity, assuming that the existing density f has a minimum point m with f(m) = 0. The results are applied to the coverage of the circle by randomly placed arcs.
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23

Fu, Norie, Naonori Kakimura, Kei Kimura, and Vorapong Suppakitpaisarn. "Maximum lifetime coverage problem with battery recovery effect." Sustainable Computing: Informatics and Systems 18 (June 2018): 1–13. http://dx.doi.org/10.1016/j.suscom.2018.02.007.

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24

McGregor, Andrew, and Hoa T. Vu. "Better Streaming Algorithms for the Maximum Coverage Problem." Theory of Computing Systems 63, no. 7 (July 23, 2018): 1595–619. http://dx.doi.org/10.1007/s00224-018-9878-x.

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25

Hüsler, Jürg. "Maximal, non-uniform spacings and the coverage problem." Journal of Applied Probability 25, no. 03 (September 1988): 519–28. http://dx.doi.org/10.1017/s0021900200041243.

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The spacings have been widely applied to many situations, such as for example the coverage problem of the circle or the line, under the hypothesis of uniformity. The paper examines the asymptotic behaviour of the maximal spacings under non-uniformity, assuming that the existing density f has a minimum point m with f(m) = 0. The results are applied to the coverage of the circle by randomly placed arcs.
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26

Pratap, Drona. "Big Step Greedy Heuristic for Maximum Coverage Problem." International Journal of Computer Applications 125, no. 7 (September 17, 2015): 19–24. http://dx.doi.org/10.5120/ijca2015905954.

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27

Manju, Pawan Bhambu, and Sandeep Kumar. "Target K-coverage problem in wireless sensor networks." Journal of Discrete Mathematical Sciences and Cryptography 23, no. 2 (February 17, 2020): 651–59. http://dx.doi.org/10.1080/09720529.2020.1729511.

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28

Ellison, John N., and John G. Nevius. "MTBE: Coverage for the Petroleum Industry's “Spreading” Problem." Environmental Claims Journal 16, no. 3-4 (January 2004): 213–18. http://dx.doi.org/10.1080/10406020490909899.

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29

Khezri, Shirin, and Mahdi Nazaari A. "Solving wireless sensor network coverage problem using LAEDA." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 452. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp452-458.

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<p>Coverage improvement is one of the main problems in wireless sensor networks. Given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. For achieving good coverage, the sensors should be placed in adequate places. In this article, estimation of distribution algorithm based on learning automata is presented for solving the sensor placement (LAEDA-SP) in distributed sensor networks by considering two factors: 1) the complete coverage and 2) the minimum costs. The proposed algorithm is a model based on search optimization method that uses a set of learning automata as a probabilistic model of high-quality solutions seen in the search process. It is applied in a various area with different size. The results not only confirmed the successes of using the new method in sensor replacement but also they showed that the proposed method performs more efficiently compared to the state-of-the-art methods such as simulated annealing (SA) and population-based incremental learning algorithms (PBIL).</p>
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30

Huang, Chi-Fu, and Yu-Chee Tseng. "The Coverage Problem in a Wireless Sensor Network." Mobile Networks and Applications 10, no. 4 (August 2005): 519–28. http://dx.doi.org/10.1007/s11036-005-1564-y.

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31

Orlowski, L. A., W. D. Grundy, and P. W. Mielke. "An empirical coverage test for theg-sample problem." Mathematical Geology 23, no. 4 (May 1991): 583–89. http://dx.doi.org/10.1007/bf02065808.

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32

Apollonio, Nicola, and Bruno Simeone. "The maximum vertex coverage problem on bipartite graphs." Discrete Applied Mathematics 165 (March 2014): 37–48. http://dx.doi.org/10.1016/j.dam.2013.05.015.

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33

Dhein, Guilherme, Alberto Francisco Kummer Neto, and Olinto César Bassi de Araújo. "The Multiple Traveling Salesman Problem with Backup Coverage." Electronic Notes in Discrete Mathematics 66 (April 2018): 135–42. http://dx.doi.org/10.1016/j.endm.2018.03.018.

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34

Aldous, D. "Stein's method in a two-dimensional coverage problem." Statistics & Probability Letters 8, no. 4 (September 1989): 307–14. http://dx.doi.org/10.1016/0167-7152(89)90037-0.

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35

Bansal, Manish, and Kiavash Kianfar. "Planar Maximum Coverage Location Problem with Partial Coverage and Rectangular Demand and Service Zones." INFORMS Journal on Computing 29, no. 1 (January 2017): 152–69. http://dx.doi.org/10.1287/ijoc.2016.0722.

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36

Cui, Ya Ping. "Key Problem of Component-Based Software Development." Applied Mechanics and Materials 687-691 (November 2014): 1896–99. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1896.

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In order to improve the component dynamic test efficiency, this paper proposes a keating component built-in test case generation method of genetic algorithm and designs the chromosome coding method. The test point and keating component facet description of dynamic test data generation method. Mass in order to improve the generation of test cases and add Yang the convergence speed of genetic algorithm. We improve the algorithm of the method for calculating the fitness function and fitness function not only consider the case of path coverage, but also considers the path coverage rate of increase, thus effectively improved the path coverage and reduce the Yang cases produce cost.
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37

Gorain, Barun, and Partha Sarathi Mandal. "Approximation Algorithms for Barrier Sweep Coverage." International Journal of Foundations of Computer Science 30, no. 03 (April 2019): 425–48. http://dx.doi.org/10.1142/s0129054119500138.

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Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.
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38

Zhang, Yanhua, Xingming Sun, and Zhanke Yu. "Solving k-Barrier Coverage Problem Using Modified Gravitational Search Algorithm." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1206129.

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Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA) is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm.
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39

Peng, Song, and Yonghua Xiong. "An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks." Sensors 19, no. 5 (March 8, 2019): 1192. http://dx.doi.org/10.3390/s19051192.

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Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.
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40

Sumalatha, G., and P. Anil. "Applications of Energy Aware Routing Protocols in WSN." Oriental journal of computer science and technology 9, no. 1 (April 28, 2016): 24–29. http://dx.doi.org/10.13005/ojcst/9.01.05.

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In this paper we have reviewed application of energy aware routing protocols like LEACH, LEACH-C, PEGASIS, BCDCP, ENPC-NPSO, ANPC etc In WSN sensors are randomly deployed in the sensor field which brings the coverage problem. Hence energy and coverage problem are very scarce resources for such sensor systems and has to be managed wisely in order to extend the life of the sensors and maximizing coverage for the duration of a particular mission. In past a lot of cluster based algorithm and techniques were used. In this paper we also find out all type of PSO based algorithm, their application and limitation over present techniques to overcome the problems of low energy and coverage of sensor range.
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41

Sumalatha, G., and P. Kumar. "Applications of Energy Aware Routing Protocols in WSN." Oriental journal of computer science and technology 9, no. 1 (April 28, 2016): 24–29. http://dx.doi.org/10.13005/ojcst/901.05.

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In this paper we have reviewed application of energy aware routing protocols like LEACH, LEACH-C, PEGASIS, BCDCP, ENPC-NPSO, ANPC etc In WSN sensors are randomly deployed in the sensor field which brings the coverage problem. Hence energy and coverage problem are very scarce resources for such sensor systems and has to be managed wisely in order to extend the life of the sensors and maximizing coverage for the duration of a particular mission. In past a lot of cluster based algorithm and techniques were used. In this paper we also find out all type of PSO based algorithm, their application and limitation over present techniques to overcome the problems of low energy and coverage of sensor range.
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42

Song, Zhiming, Haidong Liu, Xiaoyu Chen, and Maocai Wang. "An efficient algorithm for solving the constellation-to-ground region coverage problem based on longitude strip division." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 4 (August 2021): 919–29. http://dx.doi.org/10.1051/jnwpu/20213940919.

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Aiming at the constellation-to-ground region coverage problem, an efficient solution method called the longitude strip method is proposed. By dividing the ground region into several longitude strips, the latitude range of each strip is computed according to the region. Similarly, concerning the coverage area of the satellite, the latitude range of each strip is also calculated. On this basis, the coverage rate can be obtained by comprehensive statistics. Additionally, the upper and lower bounds for problems of continuous coverage and accumulative coverage are solved. Numerical simulation experiments show the proposed algorithm has higher accuracy and it is also efficient in resolving the ground area with arbitrary shapes.
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43

Lo, Shaw-Hwa. "From the Species Problem to a General Coverage Problem via a New Interpretation." Annals of Statistics 20, no. 2 (June 1992): 1094–109. http://dx.doi.org/10.1214/aos/1176348672.

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44

Huang, Peng, Feng Lin, Chang Liu, Jian Gao, and Ji-liu Zhou. "ACO-Based Sweep Coverage Scheme in Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/484902.

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Coverage problem is one of the major issues in wireless sensor networks (WSN). In order to optimize the network coverage, different coverage formulations have been proposed. Recently, a newly emerging coverage scheme in wireless sensor networks, sweep coverage, which uses mobile sensors to monitor certain points of interest (POIs), is proposed. However, the data delivery to sink, an important problem in WSN, is not considered in original sweep coverage and many of the existing works did not consider it yet. In this work, a novel algorithm named ACOSC (ACO-based sweep coverage) to solve the sweep coverage problem considering periodical coverage of POIs and delivery of data simultaneously is proposed. The evaluation results show that our algorithm has better performance than existing schemes.
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45

K.Hirani, Priti, and Manali Singh. "A Survey on Coverage Problem in Wireless Sensor Network." International Journal of Computer Applications 116, no. 2 (April 22, 2015): 1–3. http://dx.doi.org/10.5120/20305-2347.

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46

Lee, Keun-Young. "The Problem of Forced Insurance Coverage on Prenatal Ultrasound." Journal of the Korean Medical Association 50, no. 12 (2007): 1044. http://dx.doi.org/10.5124/jkma.2007.50.12.1044.

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47

Liang, Dieyan, Hong Shen, and Lin Chen. "Maximum Target Coverage Problem in Mobile Wireless Sensor Networks." Sensors 21, no. 1 (December 29, 2020): 184. http://dx.doi.org/10.3390/s21010184.

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We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1−1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1−1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵−1 approximation ratio by using a colouring technique, where ▵ denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.
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48

Burger, Mernout, Marco Huiskamp, and Tamás Keviczky. "Complete Field Coverage as a Multi-Vehicle Routing Problem." IFAC Proceedings Volumes 46, no. 18 (August 2013): 97–102. http://dx.doi.org/10.3182/20130828-2-sf-3019.00050.

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49

Zhou, Xing, Huaimin Wang, Bo Ding, Wei Peng, and Rui Wang. "Multi-objective evolutionary computation for topology coverage assessment problem." Knowledge-Based Systems 177 (August 2019): 1–10. http://dx.doi.org/10.1016/j.knosys.2019.03.033.

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50

Koltsov, Vladimir P., Le Tri Vinh, and Daria A. Starodubtseva. "TO THE PROBLEM OF SHOT PEENING COVERAGE DEGREE DETERMINATION." Proceedings of Irkutsk State Technical University 21, no. 11 (November 2017): 45–52. http://dx.doi.org/10.21285/1814-3520-2017-11-45-52.

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