Figure 1.
An example of node deployment scheme
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August & September
2019, 12(4&5): 887-900.
doi: 10.3934/dcdss.2019059
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.
Keywords:
Wireless sensor network,
energy efficient coverage,
intelligent optimization algorithm,
genetic algorithm,
fitness function.
Mathematics Subject Classification:
Primary: 58F15, 58F17; Secondary: 53C35.
Citation:
Yang Chen, Xiaoguang Xu, Yong Wang. Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete & Continuous Dynamical Systems - S,
2019, 12
(4&5)
: 887-900.
doi: 10.3934/dcdss.2019059
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
Figure 2.
Initial node deployment for different schemes
Figure 3.
Coverage ratio for various number of sensors
Figure 4.
Maximum moved distance for various number of sensors
Figure 5.
Network lifetime with various number of sensors
Figure 6.
Network lifetime with various number of targets
Table 1.
Simulation settings
| 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 % |
Table 2.
Energy cost in this experiment
| 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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