Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69, 20.15 and 26.55 compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).
Citation: Zhonghua Lu, Min Tian, Jie Zhou, Xiang Liu. Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary Golden Jackal Optimization Algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12298-12319. doi: 10.3934/mbe.2023547
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Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69, 20.15 and 26.55 compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).
[1] |
T. Stephan, K. Sharma, A. Shankar, S. Punitha, V. Varadarajan, P. Liu, Fuzzy-logic-inspired zone-based clustering algorithm for wireless sensor networks, Int. J. Fuzzy Syst., 23 (2021), 506–517. https://doi.org/10.1007/s40815-020-00929-3 doi: 10.1007/s40815-020-00929-3
![]() |
[2] |
T. A. Alghamdi, Energy efficient protocol in wireless sensor network: optimized cluster head selection model, Telecommun. Syst., 74 (2020), 331–345. https://doi.org/10.1007/s11235-020-00659-9 doi: 10.1007/s11235-020-00659-9
![]() |
[3] | M. Umashankar, M. Ramakrishna, S. Mallikarjunaswamy, Design of high speed reconfigurable deployment intelligent genetic algorithm in maximum coverage wireless sensor network, in 2019 International Conference on Data Science and Communication (IconDSC), IEEE, Bangalore, India, (2019). https://doi.org/10.1109/icondsc.2019.8816930 |
[4] |
G. S. Gandhi, K. Vikas, V. Ratnam, K. S. Babu, Grid clustering and fuzzy reinforcement-learning based energy-efficient data aggregation scheme for distributed WSN, IET Commun., 14 (2020), 2840–2848. https://doi.org/10.1049/iet-com.2019.1005 doi: 10.1049/iet-com.2019.1005
![]() |
[5] |
Q. Zhang, C. Y. Chang, Z. Dong, D. S. Roy, Tcsar: Target coverage mechanism for sensors with adjustable sensing range in WRSNs, IEEE Sens. J., 22 (2021), 3756–3765. https://doi.org/10.1109/jsen.2021.3139731 doi: 10.1109/jsen.2021.3139731
![]() |
[6] |
W. H. Liao, B. Dande, C. Y. Chang, D. S. Roy, MMQT: Maximizing the monitoring quality for targets based on probabilistic sensing model in rechargeable wireless sensor networks, IEEE Access, 8 (2020), 77073–77088. https://doi.org/10.1109/access.2020.2989199 doi: 10.1109/access.2020.2989199
![]() |
[7] |
B. Dande, C. Y. Chang, W. H. Liao, D. S. Roy, MSQAC: Maximizing the surveillance quality of area coverage in wireless sensor networks, IEEE Sens. J., 22 (2022), 6150–6163. https://doi.org/10.1109/jsen.2022.3147230 doi: 10.1109/jsen.2022.3147230
![]() |
[8] |
M. A. Awadallah, A. I. Hammouri, M. A. Al-Betar, M. S. Braik, M. Abd Elaziz, Binary horse herd optimization algorithm with crossover operators for feature selection, Comput. Biol. Med., 141 (2022), 105152. https://doi.org/10.1016/j.compbiomed.2021.105152 doi: 10.1016/j.compbiomed.2021.105152
![]() |
[9] |
J. Piri, P. Mohapatra, An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection, Comput. Biol. Med., 135 (2021), 104558. https://doi.org/10.1016/j.compbiomed.2021.104558 doi: 10.1016/j.compbiomed.2021.104558
![]() |
[10] |
G. I. Sayed, M. M. Soliman, A. E. Hassanien, A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization, Comput. Biol. Med., 136 (2022), 104. https://doi.org/10.1016/j.compbiomed.2021.104712 doi: 10.1016/j.compbiomed.2021.104712
![]() |
[11] |
S. Thawkar, S. Sharma, M. Khanna, L. kumar Singh, Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer, Comput. Biol. Med., 139 (2022), 104968. https://doi.org/10.1016/j.compbiomed.2021.104968 doi: 10.1016/j.compbiomed.2021.104968
![]() |
[12] |
S. Chakraborty, A. K. Saha, S. Nama, S. Debnath, COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction, Comput. Biol. Med., 139 (2021), 104984. https://doi.org/10.1016/j.compbiomed.2021.104984 doi: 10.1016/j.compbiomed.2021.104984
![]() |
[13] |
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.23919/ccc52363.2021.9550421 doi: 10.23919/ccc52363.2021.9550421
![]() |
[14] |
A. Latha, S. Prasanna, S. Hemalatha, B. Sivakumar, A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks, Cognit. Syst. Res., 56 (2019), 14–22. https://doi.org/10.1016/j.cogsys.2018.11.006 doi: 10.1016/j.cogsys.2018.11.006
![]() |
[15] |
F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, S. Mirjalili, Henry gas solubility optimization: A novel physics-based algorithm, Future Gener. Comput. Syst., 101 (2019), 646–667. https://doi.org/10.1016/j.future.2019.07.015 doi: 10.1016/j.future.2019.07.015
![]() |
[16] |
I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, H. Chen, RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method, Expert Syst. Appl., 181 (2021), 115079. https://doi.org/10.1016/j.eswa.2021.115079 doi: 10.1016/j.eswa.2021.115079
![]() |
[17] |
I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, A. H. Gandomi, INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 195 (2022), 116516. https://doi.org/10.1016/j.eswa.2022.116516 doi: 10.1016/j.eswa.2022.116516
![]() |
[18] |
F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, W. Al-Atabany, Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simul., 192 (2022), 84–110. https://doi.org/10.1016/j.matcom.2021.08.013 doi: 10.1016/j.matcom.2021.08.013
![]() |
[19] |
F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Appl. Intell., 51 (2021), 1531–1551. https://doi.org/10.1016/j.jksuci.2022.07.022 doi: 10.1016/j.jksuci.2022.07.022
![]() |
[20] |
W. Jia, G. Qi, M. Liu, J. Zhou, A high accuracy localization algorithm with dv-hop and fruit fly optimization in anisotropic wireless networks, J. King Saud Univ. Comput. Inf. Sci., 34 (2022), 8102–8111. https://doi.org/10.1016/j.jksuci.2022.07.022 doi: 10.1016/j.jksuci.2022.07.022
![]() |
[21] |
J. Zhou, Y. Zhang, Z. Li, R. Zhu, Stochastic scheduling of a power grid in the presence of EVs, REs, and risk index with a developed lightning search algorithm, J. Clean. Prod., 364 (2022), 132473. https://doi.org/10.1016/j.jclepro.2022.132473 doi: 10.1016/j.jclepro.2022.132473
![]() |
[22] |
Y. Zhang, J. Xie, Y. Liu, C. Li, J. Xiao, H. Ma, et al., An immune chaotic adaptive evolutionary algorithm for energy-efficient clustering management in LPWSN, J. King Saud Univ. Comput. Inf. Sci., 34 (2022), 8297–8306. https://doi.org/10.1016/j.jksuci.2022.08.010 doi: 10.1016/j.jksuci.2022.08.010
![]() |
[23] |
Y. Liu, C. Li, J. Xiao, Z. Li, W. Chen, X. Qu, et al., QEGWO: Energy-efficient clustering approach for industrial wireless sensor networks using Quantum-related bioinspired optimization, IEEE Internet Things J., 9 (2022), 23691–23704. https://doi.org/10.1109/jiot.2022.3189807 doi: 10.1109/jiot.2022.3189807
![]() |
[24] | W. Liu, S. Yang, S. Sun, S. Wei, A node deployment optimization method of WSN based on ant-lion optimization algorithm, in 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), IEEE, Lviv, Ukraine, (2018), 88–92. https://doi.org/10.1109/idaacs-sws.2018.8525824 |
[25] |
L. Zhou, Y. Shan, Privacy-preserving, energy-saving data aggregation scheme in wireless sensor networks, J. Inf. Process. Syst., 16 (2022), 83–95. https://doi.org/10.4028/www.scientific.net/amm.635-637.878 doi: 10.4028/www.scientific.net/amm.635-637.878
![]() |
[26] |
A. Latha, S. Prasanna, S. Hemalatha, B. Sivakumar, A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks, Cognit. Syst. Res., 56 (2019), 14–22. https://doi.org/10.1016/j.cogsys.2018.11.006 doi: 10.1016/j.cogsys.2018.11.006
![]() |
[27] |
H. Wang, K. Li, W. Pedrycz, A routing algorithm based on simulated annealing algorithm for maximising wireless sensor networks lifetime with a sink node, Int. J. Bio-Inspired Comput., 15 (2022), 264–275. https://doi.org/10.1504/ijbic.2020.10030552 doi: 10.1504/ijbic.2020.10030552
![]() |
[28] |
T. Qasim, M. Zia, Q. A. Minhas, N. Bhatti, K. Saleem, T. Qasim, et al., An ant colony optimization based approach for minimum cost coverage on 3-d grid in wireless sensor networks, IEEE Commun. Lett., 22 (2018), 1140–1143. https://doi.org/10.1504/ijbic.2020.10030552 doi: 10.1504/ijbic.2020.10030552
![]() |
[29] |
A. Chowdhury, D. De, Energy-efficient coverage optimization in wireless sensor networks based on voronoi-glowworm swarm optimization-k-means algorithm, Ad Hoc Networks, 122 (2021), 102660. https://doi.org/10.1016/j.adhoc.2021.102660 doi: 10.1016/j.adhoc.2021.102660
![]() |
[30] | E. E. Tsiropoulou, S. T. Paruchuri, J. S. Baras, Interest, energy and physical-aware coalition formation and resource allocation in smart IoT applications, in 2017 51st Annual Conference on Information Sciences and Systems (CISS), IEEE, Baltimore, USA, (2017), 1–6. https://doi.org/10.1109/ciss.2017.7926111 |
[31] |
Y. Liu, C. Li, Y. Zhang, J. Xiao, J. Zhou, DCC-IACJS: A novel bio-inspired duty cycle-based clustering approach for energyefficient wireless sensor networks, J. King Saud Univ. Comput. Inf. Sci., 35 (2023), 775–790. https://doi.org/10.1016/j.jksuci.2023.01.015 doi: 10.1016/j.jksuci.2023.01.015
![]() |
[32] | M. Xu, J. Zhou, Y. Lu, Phgwo: a duty cycle design method for high-density wireless sensor networks, in 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE), IEEE, Fuzhou, China, (2019), 28–31. https://doi.org/10.1109/iciase45644.2019.9074127 |
[33] |
Y. Liu, J. Xiao, C. Li, H. Qin, J. Zhou, Sensor duty cycle for prolonging network lifetime using quantum clone grey wolf optimization algorithm in industrial wireless sensor networks, J. Sens., 2021 (2021), 1–13. https://doi.org/10.1155/2021/5511745 doi: 10.1155/2021/5511745
![]() |
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