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Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm
1. | Modern Education Technology Center, Anhui Polytechnic University, Anhui Wuhu, 241000, China |
2. | School of Electrical Engineering, Anhui Polytechnic University, Anhui Wuhu, 241000, China |
3. | Modern Education Technology Center, School of Computer and Information Engineering, Anhui Wuhu, 241000, China |
As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.
References:
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A. A. Abbasi and M. Younis, A survey on clustering algorithms for wireless sensor networks, Computer Communications, 30 (2007), 2826-2841. Google Scholar |
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G. Ahmed and N. M. Khan, Adaptive power-control based energy-efficient routing in wireless sensor networks, Wireless Personal Communications, 94 (2017), 1297-1329. Google Scholar |
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I. F. Akyildiz, T. Melodia and K. R. Chowdhury, A survey on wireless multimedia sensor networks, Computer Networks, 51 (2007), 921-960. Google Scholar |
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I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless sensor networks: A survey, Computer Networks, 38 (2002), 393-422. Google Scholar |
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O. M. Alia and A. Al-Ajouri, Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, Ieee Sensors Journal, 17 (2017), 882-896. Google Scholar |
[6] |
M. Alipio, N. M. Tiglao, A. Grilo, F. Bokhari, U. Chaudhry and S. Qureshi, Cache-based transport protocols in wireless sensor networks: A survey and future directions, Journal of Network and Computer Applications, 88 (2017), 29-49. Google Scholar |
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G. Anastasi, C. Marco, M. Di Francesco and A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad Hoc Networks, 7 (2009), 537-568. Google Scholar |
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N. A. Aziz, A. W. Mohemmed, M. Y. Alias, K. Aziz and S. Syahali, Coverage maximization and energy conservation for mobile wireless sensor networks: A two phase particle swarm optimization algorithm, International Journal of Natural Computing Research, 3 (2012), 43-63. Google Scholar |
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M. Boudali, M. R. Senouci, M. Aissani and W. K. Hidouci, Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks, Annals of Telecommunications, 72 (2017), 221-232. Google Scholar |
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A. Boudries, M. Amad and P. Siarry, Novel approach for replacement of a failure node in wireless sensor network, Telecommunication Systems, 65 (2017), 341-350. Google Scholar |
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K. Bouyahia and M. Benchaiba, CRVR: Connectivity Repairing in Wireless Sensor Networks with Void Regions, Journal of Network and Systems Management, 25 (2017), 536-557. Google Scholar |
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H. Hakli and H. Uguz, A novel approach for automated land partitioning using genetic algorithm, Expert Systems with Applications, 82 (2017), 10-18. Google Scholar |
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G. J. Han, L. Liu, J. F. Jiang, L. Shu and G. Hancke, Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks, Ieee Transactions on Industrial Informatics, 13 (2017), 135-143. Google Scholar |
[14] |
S. Kebir, I. Borne and D. Meslati, A genetic algorithm-based approach for automated refactoring of component-based software, Information and Software Technology, 88 (2017), 17-36. Google Scholar |
[15] |
P. Martinez-Canada, C. Morillas, H. E. Plesser, S. Romero and F. Pelayo, Genetic algorithm for optimization of models of the early stages in the visual system, Neurocomputing, 250 (2017), 101-108. Google Scholar |
[16] |
A. Mehrabi and K. Kim, General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks, IEEE Transactions on Mobile Computing, 16 (2017), 1881-1896. Google Scholar |
[17] |
T. Nguyen, C. So-In, N. Nguyen and S. Phoemphon, A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks, Peer-to-Peer Networking and Applications, 10 (2017), 519-536. Google Scholar |
[18] |
A. Pananjady, V. K. Bagaria and R. Vaze, Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks, IEEE-ACM Transactions on Networking, 25 (2017), 98-111. Google Scholar |
[19] |
D. Raposo, A. Rodrigues, J. S. Silva and F. Boavida, A Taxonomy of Faults for Wireless Sensor Networks, Journal of Network and Systems Management, 25 (2017), 591-611. Google Scholar |
[20] |
J. So and H. Byun, Load-Balanced Opportunistic Routing for Duty-Cycled Wireless Sensor Networks, Ieee Transactions on Mobile Computing, 16 (2017), 1940-1955. Google Scholar |
[21] |
Z. Y. Sun, Y. X. Shu, X. F. Xing, W. Wei, H. B. Song and W. Li, LPOCS: A Novel Linear Programming Optimization Coverage Scheme in Wireless Sensor Networks, Ad Hoc & Sensor Wireless Networks, 33 (2016), 173-197. Google Scholar |
[22] |
Z. Y. Sun, Y. S. Zhang, Y. L. Nie, W. Wei, J. Lloret and H. B. Song, CASMOC: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks, Wireless Networks, 23 (2017), 1201-1222. Google Scholar |
[23] |
G. K. C. Thevar and G. Rohini, Energy efficient geographical key management scheme for authentication in mobile wireless sensor networks, Wireless Networks, 23 (2017), 1479-1489. Google Scholar |
[24] |
L. Wang, P. H. Kao and M. T. Wu, Using Partial Coverage Strategy to Prolong Service Time of a Cluster Based Wireless Sensor Network, Journal of Internet Technology, 18 (2017), 371-377. Google Scholar |
[25] |
D. S. Wang, M. Zhang, Z. Li, C. Song, M. X. Fu, J. Li and X. Chen, System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm, Optics Communications, 399 (2017), 1-12. Google Scholar |
[26] |
M. Wazid and A. K. Das, A secure group-based blackhole node detection scheme for hierarchical wireless sensor networks, Wireless Personal Communications, 94 (2017), 1165-1191. Google Scholar |
[27] |
C. L. Yang and K. W. Chin, On nodes placement in energy harvesting wireless sensor networks for coverage and connectivity, Ieee Transactions on Industrial Informatics, 13 (2017), 27-36. Google Scholar |
[28] |
J. Yick, B. Mukherjee and D. Ghosal, Wireless sensor network survey, Computer Networks, 52 (2008), 2292-2330. Google Scholar |
show all references
References:
[1] |
A. A. Abbasi and M. Younis, A survey on clustering algorithms for wireless sensor networks, Computer Communications, 30 (2007), 2826-2841. Google Scholar |
[2] |
G. Ahmed and N. M. Khan, Adaptive power-control based energy-efficient routing in wireless sensor networks, Wireless Personal Communications, 94 (2017), 1297-1329. Google Scholar |
[3] |
I. F. Akyildiz, T. Melodia and K. R. Chowdhury, A survey on wireless multimedia sensor networks, Computer Networks, 51 (2007), 921-960. Google Scholar |
[4] |
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless sensor networks: A survey, Computer Networks, 38 (2002), 393-422. Google Scholar |
[5] |
O. M. Alia and A. Al-Ajouri, Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, Ieee Sensors Journal, 17 (2017), 882-896. Google Scholar |
[6] |
M. Alipio, N. M. Tiglao, A. Grilo, F. Bokhari, U. Chaudhry and S. Qureshi, Cache-based transport protocols in wireless sensor networks: A survey and future directions, Journal of Network and Computer Applications, 88 (2017), 29-49. Google Scholar |
[7] |
G. Anastasi, C. Marco, M. Di Francesco and A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad Hoc Networks, 7 (2009), 537-568. Google Scholar |
[8] |
N. A. Aziz, A. W. Mohemmed, M. Y. Alias, K. Aziz and S. Syahali, Coverage maximization and energy conservation for mobile wireless sensor networks: A two phase particle swarm optimization algorithm, International Journal of Natural Computing Research, 3 (2012), 43-63. Google Scholar |
[9] |
M. Boudali, M. R. Senouci, M. Aissani and W. K. Hidouci, Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks, Annals of Telecommunications, 72 (2017), 221-232. Google Scholar |
[10] |
A. Boudries, M. Amad and P. Siarry, Novel approach for replacement of a failure node in wireless sensor network, Telecommunication Systems, 65 (2017), 341-350. Google Scholar |
[11] |
K. Bouyahia and M. Benchaiba, CRVR: Connectivity Repairing in Wireless Sensor Networks with Void Regions, Journal of Network and Systems Management, 25 (2017), 536-557. Google Scholar |
[12] |
H. Hakli and H. Uguz, A novel approach for automated land partitioning using genetic algorithm, Expert Systems with Applications, 82 (2017), 10-18. Google Scholar |
[13] |
G. J. Han, L. Liu, J. F. Jiang, L. Shu and G. Hancke, Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks, Ieee Transactions on Industrial Informatics, 13 (2017), 135-143. Google Scholar |
[14] |
S. Kebir, I. Borne and D. Meslati, A genetic algorithm-based approach for automated refactoring of component-based software, Information and Software Technology, 88 (2017), 17-36. Google Scholar |
[15] |
P. Martinez-Canada, C. Morillas, H. E. Plesser, S. Romero and F. Pelayo, Genetic algorithm for optimization of models of the early stages in the visual system, Neurocomputing, 250 (2017), 101-108. Google Scholar |
[16] |
A. Mehrabi and K. Kim, General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks, IEEE Transactions on Mobile Computing, 16 (2017), 1881-1896. Google Scholar |
[17] |
T. Nguyen, C. So-In, N. Nguyen and S. Phoemphon, A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks, Peer-to-Peer Networking and Applications, 10 (2017), 519-536. Google Scholar |
[18] |
A. Pananjady, V. K. Bagaria and R. Vaze, Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks, IEEE-ACM Transactions on Networking, 25 (2017), 98-111. Google Scholar |
[19] |
D. Raposo, A. Rodrigues, J. S. Silva and F. Boavida, A Taxonomy of Faults for Wireless Sensor Networks, Journal of Network and Systems Management, 25 (2017), 591-611. Google Scholar |
[20] |
J. So and H. Byun, Load-Balanced Opportunistic Routing for Duty-Cycled Wireless Sensor Networks, Ieee Transactions on Mobile Computing, 16 (2017), 1940-1955. Google Scholar |
[21] |
Z. Y. Sun, Y. X. Shu, X. F. Xing, W. Wei, H. B. Song and W. Li, LPOCS: A Novel Linear Programming Optimization Coverage Scheme in Wireless Sensor Networks, Ad Hoc & Sensor Wireless Networks, 33 (2016), 173-197. Google Scholar |
[22] |
Z. Y. Sun, Y. S. Zhang, Y. L. Nie, W. Wei, J. Lloret and H. B. Song, CASMOC: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks, Wireless Networks, 23 (2017), 1201-1222. Google Scholar |
[23] |
G. K. C. Thevar and G. Rohini, Energy efficient geographical key management scheme for authentication in mobile wireless sensor networks, Wireless Networks, 23 (2017), 1479-1489. Google Scholar |
[24] |
L. Wang, P. H. Kao and M. T. Wu, Using Partial Coverage Strategy to Prolong Service Time of a Cluster Based Wireless Sensor Network, Journal of Internet Technology, 18 (2017), 371-377. Google Scholar |
[25] |
D. S. Wang, M. Zhang, Z. Li, C. Song, M. X. Fu, J. Li and X. Chen, System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm, Optics Communications, 399 (2017), 1-12. Google Scholar |
[26] |
M. Wazid and A. K. Das, A secure group-based blackhole node detection scheme for hierarchical wireless sensor networks, Wireless Personal Communications, 94 (2017), 1165-1191. Google Scholar |
[27] |
C. L. Yang and K. W. Chin, On nodes placement in energy harvesting wireless sensor networks for coverage and connectivity, Ieee Transactions on Industrial Informatics, 13 (2017), 27-36. Google Scholar |
[28] |
J. Yick, B. Mukherjee and D. Ghosal, Wireless sensor network survey, Computer Networks, 52 (2008), 2292-2330. Google Scholar |






Parameter | Value |
Sensing field | |
Coverage radius | 5m |
Number of targets | 10-60 |
Initial population size | 60 |
Mutation rate | 3 % |
Parameter | Value |
Sensing field | |
Coverage radius | 5m |
Number of targets | 10-60 |
Initial population size | 60 |
Mutation rate | 3 % |
Working state | Energy cost(mA) |
Active | 13.58 |
Transmitting | 14.41 |
Receiving | 9.37 |
Working state | Energy cost(mA) |
Active | 13.58 |
Transmitting | 14.41 |
Receiving | 9.37 |
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