Abstract
The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes’ location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%.
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References
Aziz TI, Protik S, Hossen MS, Choudhury S, Alam MM (2019) Degree-based balanced clustering for large-scale software defined networks. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, pp 1–6
Bera S, Misra S, Vasilakos AV (2017) Software-defined networking for internet of things: a survey. IEEE Internet Things J 4(6):1994–2008
Chaudhry R, Kumar N (2022) A multi-objective meta-heuristic solution for green computing in software-defined wireless sensor networks. IEEE Trans Green Commun Netw
Chaurasiya SK, Biswas A, Nayyar A, Zaman Jhanjhi N, Banerjee R (2023) DEICA: a differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks. Int J Commun Syst 36(5):e5420
Debasis K, Sharma LD, Bohat V, Bhadoria RS (2023) An energy-efficient clustering algorithm for maximizing lifetime of wireless sensor networks using machine learning. In: Mobile networks and applications, pp 1–15
Fogli M, Giannelli C, Stefanelli C (2022) Software-defined networking in wireless ad hoc scenarios: objectives and control architectures. J Netw Comput Appl 203:103387
Haque IT, Abu-Ghazaleh N (2016) Wireless software defined networking: a survey and taxonomy. IEEE Commun Surv Tutor 18(4):2713–2737
Heller B, Sherwood R, McKeown N (2012) The controller placement problem. ACM SIGCOMM Comput Commun Rev 42(4):473–478
Hoang D, Yadav P, Kumar R, Panda S (2010) A robust harmony search algorithm based clustering protocol for wireless sensor networks. In: 2010 IEEE International Conference On Communications Workshops. IEEE, pp 1–5
Jiao S, Wang C, Gao R, Li Y, Zhang Q (2021) Harris hawks optimization with multi-strategy search and application. Symmetry 13(12):2364
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4. IEEE, pp 1942–1948
Killi BPR, Rao SV (2017) Capacitated next controller placement in software defined networks. IEEE Trans Netw Serv Manage 14(3):514–527
Kobo HI, Abu-Mahfouz AM, Hancke GP (2017) A survey on software-defined wireless sensor networks: challenges and design requirements. IEEE Access 5:1872–1899
Kobo HI, Abu-Mahfouz AM, Hancke GP (2019) Efficient controller placement and reelection mechanism in distributed control system for software defined wireless sensor networks. Trans Emerg Telecommun Technol 30(6):e3588
Kumar N, Vidyarthi DP (2018) A green routing algorithm for IoT-enabled software defined wireless sensor network. IEEE Sens J 18(22):9449–9460
Lee YF, Shen CC (2007) A transaction-based approach to over-the-air programming in wireless sensor networks. In: 2007 International Symposium on Communications and Information Technologies. IEEE, pp 1377–1382
Li F, Xu X, Han X, Gao S, Wang Y (2019) Adaptive controller placement in software defined wireless networks. China Commun 16(11):81–92
Liao J, Sun H, Wang J, Qi Q, Li K, Li T (2017) Density cluster based approach for controller placement problem in large-scale software defined networkings. Comput Netw 112:24–35
Luo T, Tan HP, Quek TQ (2012) Sensor openflow: enabling software-defined wireless sensor networks. IEEE Commun Lett 16(11):1896–1899
Modieginyane KM, Letswamotse BB, Malekian R, Abu-Mahfouz AM (2018) Software defined wireless sensor networks application opportunities for efficient network management: a survey. Comput Electric Eng 66:274–287
Mousavi SK, Fazliahmadi S, Rasouli N, Faragardi HR, Fotouhi H, Fahringer T (2019) A budget-constrained placement of controller nodes for maximizing the network performance in SDN-enabled WSNS. In: International Conference on Communication, Management and Information Technology (ICCMIT)
Narwaria A, Mazumdar AP (2023) Software-defined wireless sensor network: a comprehensive survey. J Netw Comput Appl 103636
Narwaria A, Mazumdar AP, Kalla G (2021) C3hac: a controller placement approach for SDWSN. In: TENCON 2021–2021 IEEE Region 10 Conference (TENCON). IEEE, pp 935–940
Ramteke R, Singh S, Malik A (2022) Optimized routing technique for IoT enabled software-defined heterogeneous WSNS using genetic mutation based PSO. Comput Stand Interfaces 79:103548
Rivera G, Porras R, Sanchez-Solis JP, Florencia R, García V (2022) Outranking-based multi-objective PSO for scheduling unrelated parallel machines with a freight industry-oriented application. Eng Appl Artif Intell 108:104556
Seyyedabbasi A, Kiani F, Allahviranloo T, Fernandez-Gamiz U, Noeiaghdam S (2023) Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms. Alex Eng J 63:339–357
Tasgetiren MF, Sevkli M, Liang YC, Gençyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 2. IEEE, pp 1412–1419
Wang Y, Zhang H, Zhang G (2019) cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm Evol Comput 49:114–123
Xiang W, Wang N, Zhou Y (2016) An energy-efficient routing algorithm for software-defined wireless sensor networks. IEEE Sens J 16(20):7393–7400
Yao G, Bi J, Li Y, Guo L (2014) On the capacitated controller placement problem in software defined networks. IEEE Commun Lett 18(8):1339–1342
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Narwaria, A., Soni, K. & Mazumdar, A.P. A position and energy aware multi-objective controller placement and re-placement scheme in distributed SDWSN. J Supercomput (2024). https://doi.org/10.1007/s11227-024-05899-z
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DOI: https://doi.org/10.1007/s11227-024-05899-z