A multi objective optimization in wireless sensor network using enhanced NSGA-3 algorithm
*Rishika YadavCorresponding authorryrishikayadav@gmail.comAffiliation 1Department of Computer Science & EngineeringGraphic Era Hill UniversityDehradun, Uttrakhand, IndiaAffiliation 2Department of Computer Science & EngineeringDr. A.P.J. Abdul Kalam Technical UniversityLucknow, Uttar Pradesh, IndiaView full profile → , Raghuraj Singhraghurajsingh@rediffmail.comDepartment of Computer Science & EngineeringHarcourt Butler Technical UniversityKanpur, Uttar Pradesh, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 07 Feb 2023
- Published Online:
- 15 Dec 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1727
- Pages:
- 1925–1937
Abstract
Keywords
Subject Classifications
References
[1] Reddy BSP, Rao CSP., A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. International Journal of Advanced Manufacturing Technology. 31(5–6) : 602–613 (2006).
[2] Lu J, Wang X, Zhang L, Zhao X., Fuzzy random multi-objective optimization-based routing for wireless sensor networks. Soft comput. 18(5) : 981–994 (2014).
[3] Gao T, Song JY, Zou JY, Ding JH, Wang DQ, Jin RC., An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks. Wireless Networks 22(1) : 135–157 (2016).
[4] Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L., A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems. IEEE Communications Surveys and Tutorials. 19(1) : 550–586 (2017).
[5] Deb K., Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing (2011).
[6] Sengupta S, Das S, Nasir MD, Panigrahi BK. Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng Appl Artif Intell. 26(1) : 405–416 (2013).
[7] Marcelloni F, Vecchio M., Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Inf Sci (N Y). 180(10) : 1924–1941 (2010).
[8] Chen L, Xu Y, Xu F, Hu Q, Tang Z., Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method. Applied Intelligence (2022).
[9] Bouzid SE, Bouzid SE, Seresstou Y, Raoof K, Omri MN, Mbarki M, Dridi C., MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm. IEEE Access. 8:105793–105814 (2020).
[10] le Berre M, Hnaien F, Snoussi H., Multi-objective optimization in wireless sensors networks. Proceedings of the International Conference on Microelectronics, ICM (2011).
[11] Chang Y, Yuan X, Li B, Niyato D, Al-Dhahir N., Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs. IEEE Access. 7 : 4913–4926 (2019).
[12] Yetgin H, Cheung KTK, Hanzo L., Multi-objective routing optimization using evolutionary algorithms. IEEE Wireless Communications and Networking Conference, WCNC. : 3030–3034 (2012).
[13] Singh K, Singh K, Son LH, Aziz A., Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks. 138 : 90–107 (2018).
[14] Syarif A, Benyahia I, Abouaissa A, Idoumghar L, Sari RF, Lorenz P., Evolutionary multi-objective based approach for wireless sensor network deployment. 2014 IEEE International Conference on Communications, ICC 2014 : 1831–1836 (2014).
[15] Jameii SM, Faez K, Dehghan M., Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks (2015). http://dx.doi.org/101155/2015/363815. doi:10.1155/2015/363815.
[16] Jameii SM, Faez K, Dehghan M., AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommun Syst . 2023 Feb 7] 61(3) : 515–530 (2016). https://link.springer.com/article/10.1007/s11235-015-0009-6.
[17] Deb K, Jain H., An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation. 18(4) : 577–601 (2014).
[18] Jain H, Deb K., An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation. 18(4) : 602–622 (2014).
[19] Shi B, Yue Z, Yin S, Metiaf A, Wu Q., Wireless Sensor Network Deployment Optimization Using Reference-Point-Based Non-dominated Sorting Approach (NSGA-III). J Phys Conf Ser 1284(1) : 012063 (2019). doi:10.1088/1742-6596/1284/1/012063.
[20] Chaudhari P, Garg S., Multi-objective optimization of maleic anhydride circulating fluidized bed (CFB) reactors. Chemical Engineering Research and Design. 141 (2019). doi:10.1016/j.cherd.2018.10.020.
[21] Syarif A, Benyahia I, Abouaissa A, Idoumghar L, Sari RF, Lorenz P., Evolutionary multi-objective based approach for wireless sensor network deployment. In: 2014 IEEE International Conference on Communications (ICC). p. 1831–1836 (2014).
[22] Konstantinidis A, Yang K, Zhang Q, Zeinalipour-Yazti D., A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Computer Networks. 54(6) : 960–976 (2010). doi:10.1016/j.comnet.2009.08.010.
[23] Chang Y, Yuan X, Li B, Niyato D, Al-Dhahir N., Machine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNs. IEEE Access. 7 : 4913–4926 (2018).
[24] Arram A, Ayob M., A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems. Comput Ind Eng. 133 : 267–274 (2019). doi:10.1016/j.cie.2019.05.012.
[25] Meena, N., & Singh, B., Firefly optimization based hierarchical clustering algorithm in wireless sensor network. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1717-1725 (2021).
[26] Arivudainambi, D., Pavithra, R., & Kalyani, P., Cuckoo search algorithm for target coverage and sensor scheduling with adjustable sensing range in wireless sensor network. Journal of Discrete Mathematical Sciences and Cryptography, 24(4), 975-996 (2021).
[27] Yuvaraj, D., Sivaram, M., & Navaneetha Krishnan, S., Intelligent detection of untrusted data transmission to optimize energy in sensor networks. Journal of Information and Optimization Sciences, 41(3), 799-811 (2020).
[28] Kumar, A., Singh, K., & Khan, T., L-RTAM: Logarithm based reliable trust assessment model for WBSNs. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1701-1716 (2021).
[29] Khan, T., & Singh, K., Resource management based secure trust model for WSN. Journal of Discrete Mathematical Sciences and Cryptography, 22(8), 1453-1462 (2019).
[30] Khan, Tayyab, and Karan Singh. “RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs.” Wireless Personal Communications 129, no. 2 : 953-991 (2023).
[31] Shariq, M., Singh, K., Lal, C., Conti, M., & Khan, T., ESRAS: An efficient and secure ultra-lightweight RFID authentication scheme for low-cost tags. Computer Networks, 217, 109360 (2022).
[32] Kumar, A., Singh, K., Khan, T., Ahmadian, A., Saad, M. H. M., & Manjul, M., ETAS: An efficient trust assessment scheme for BANs. IEEE Access, 9, 83214-83233 (2021).
[33] Khan, T., Singh, K., & Purohit, K. C., ICMA: An efficient integrated congestion control approach. Recent Patents on Engineering, 14(3), 294-309 (2020).
[34] Khan, T., Singh, K., Shariq, M., Ahmad, K., Savita, K. S., Ahmadian, A., ... & Conti, M., An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach. Computer Communications, 209, 217-229 (2023).




