Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

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Darshan B. D. 1,* Prashanth C. R. 2

1. Department of Electronics and Communication Engineering, S J B Institute of Technology, Kengeri, Bengaluru-560060, Karnataka, India

2. Department of Electronics & Telecommunication Engineering, Dr. Ambedkar Institute of Technology, Bengaluru-560056, Karnataka, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2023.05.09

Received: 17 Dec. 2022 / Revised: 27 Feb. 2023 / Accepted: 6 May 2023 / Published: 8 Oct. 2023

Index Terms

Cluster-Based Routing, Clustering Process, Dual-Discriminator Conditional Generative Adversarial Network, Hybrid Momentum Search Algorithm, Giza Pyramids Construction Algorithm, Cluster Head Selection


Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Cite This Paper

Darshan B. D., Prashanth C. R., "Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-Based Routing in WSN Assisted IoT", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.5, pp.96-112, 2023. DOI:10.5815/ijcnis.2023.05.09


[1]I. V. Pustokhina, D. A. Pustokhin, E. L. Lydia, M. Elhoseny, and K. Shankar, “Energy efficient neuro-fuzzy cluster-based topology construction with metaheuristic route planning algorithm for Unmanned Aerial Vehicles,” Computer Networks, Vol. 196, pp. 108214, Sep 2021.
[2]S. Gudla, and N. R. Kuda, “Learning automata-based energy efficient and reliable data delivery routing mechanism in wireless sensor networks,” Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 8, pp. 5759-5765, Sep 2022.
[3]D. Anitha, and R. A. Karthika, “DEQLFER - A deep extreme Q-learning Firefly Energy Efficient and high-performance routing protocol for underwater communication,” Computer Communications, Vol. 174, pp. 143-153, Jun 2021.
[4]D. L. Reddy, C. Puttamadappa and H. N. Suresh, “Merged glow-worm swarm with ant colony optimization for energy efficient clustering and routing in wireless sensor network,” Pervasive and Mobile Computing, Vol. 71, p. 101338, Feb 2021.
[5]R. Yarinezhad, and S. Azizi, “An energy-efficient routing protocol for the internet of things networks based on geographical location and link quality,” Computer Networks, Vol. 193, pp. 108116, Jul 2021.
[6]J. Anees, H.-C. Zhang, B. G. Lougou, S. Baig, and Y. G. Dessie, “Delay Aware Energy-efficient opportunistic node selection in restricted routing,” Computer Networks, Vol. 181, pp. 107536, Nov 2020.
[7]W. Fang, W. Zhang, W. Yang, Z. Li, W. Gao, and Y. Yang, “Trust management-based and energy efficient hierarchical routing protocol in Wireless Sensor Networks,” Digital Communications and Networks, Vol. 7, No. 4, pp. 470-478, Nov 2021.
[8]A. S. Sharma, and D. S. Kim, “Energy efficient multipath ant colony-based routing algorithm for mobile ad hoc networks,” Ad Hoc Networks, Vol. 113, pp. 102396, Mar 2021.
[9]P. Maheshwari, A. K. Sharma, and K. Verma, “Energy efficient cluster-based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization,” Ad Hoc Networks, Vol. 110, pp. 102317, Jan 2021.
[10]D. Gopika, and R. Panjanathan, “Energy efficient routing protocols for WSN based IOT Applications: A Review,” Materials Today: Proceedings, Nov 2020.
[11]V. Nivedhitha, A. G. Saminathan, and P. Thirumurugan, “DMEERP: A Dynamic multi-hop energy efficient routing protocol for WSN,” Microprocessors and Microsystems, Vol. 79, pp. 103291, Nov 2020.
[12]M. K. Singh, S. I. Amin, and A. Choudhary, “Genetic algorithm-based sink mobility for energy efficient data routing in wireless sensor networks,” AEU - International Journal of Electronics and Communications, Vol. 131, pp. 153605, Mar 2021.
[13]D. Wang, J. Liu, and D. Yao, “An energy-efficient distributed adaptive cooperative routing based on reinforcement learning in Wireless Multimedia Sensor Networks,” Computer Networks, Vol. 178, pp. 107313, Sep 2020.
[14]K. S. Shivakumar, and V. C. Patil, “An optimal energy efficient cross-layer routing in Manets,” Sustainable Computing: Informatics and Systems, Vol. 28, pp. 100458, Dec 2020.
[15]P. Vadicherla, and D. Vadlakonda, “Study on energy efficient routing protocols scheme in Heterogeneous Wireless Sensor Networks (Network & Mobility),” Materials Today: Proceedings, Vol. 47, pp. 4955-4958, Jan 2021.
[16]R. F. Mansour, S. Abdel-Khalek, S. M. Basha, M. M. Khayyat, B. M. E. Elnaim, and V. Shankar, “Adaptive parallel seeker optimization-based route planning for clustered WSN in smart cities,” Computers and Electrical Engineering, Vol. 102, pp. 108289, Sep 2022.
[17]I. V. Pustokhina, D. A. Pustokhin, E. L. Lydia, M. Elhoseny, and K. Shankar, “Energy efficient neuro-fuzzy cluster based topology construction with metaheuristic route planning algorithm for Unmanned Aerial Vehicles,” Computer Networks, Vol. 196, pp. 108214, Sep 2021.
[18]V. VM, “PROSD-EDGEIOT: Protected Cluster assisted SDWSN for tetrad edge-IOT by collaborative DDOS detection and mitigation,” Cyber-Physical Systems, pp. 1–30, Nov, 2021.
[19]G. M. Ram, and E. Ilavarsan, “Review on energy-efficient routing protocols in WSN,” Computer Networks, Big Data and IoT, pp. 851–871, Jan 2021.
[20]P. Mitra, S. Mondal, and K. L. Hassan, “Energy efficient rendezvous point-based routing in wireless sensor network with Mobile Sink,” Recent Trends in Computational Intelligence Enabled Research, pp. 279–293, Jan 2021.
[21]G. Arya, A. Bagwari. and D. S. Chauhan, “Performance Analysis of Deep Learning-based routing protocol for an efficient data transmission in 5G WSN communication,” IEEE Access, Vol. 10, pp. 9340–9356, Jan 2022.
[22]D. Agrawal, M. H. WasimQureshi, P. Pincha, P. Srivastava, S. Agarwal, V. Tiwari, and S. Pandey, “Gwo-C: Grey Wolf optimizer-based clustering scheme for WSNS,” International Journal of Communication Systems, Vol. 33, No. 8, pp. 55-68, May 2020.
[23]T. Vaiyapuri, V. S. Parvathy, V. Manikandan, N. Krishnaraj, D. Gupta, and K. Shankar, “A novel hybrid optimization for cluster‐based routing protocol in information-centric wireless sensor networks for IOT based Mobile Edge Computing,” Wireless Personal Communications, pp. 1-24, Jan 2021.
[24]S. Loganathan, and J. Arumugam, “Energy efficient clustering algorithm based on particle swarm optimization technique for wireless sensor networks,” Wireless Personal Communications, Vol. 119, No. 1, pp. 815-843, Jul 2021.
[25]G. P. Agbulu, G. J. Kumar, V. A. Juliet, and S. A. Hassan, “PECDF-CMRP: A power-efficient compressive data fusion and cluster-based multi-hop relay-assisted routing protocol for IOT Sensor Networks,” Wireless Personal Communications, pp. 1-23, Jun 2022.
[26]K. Juneja, “Design of a novel degree load‐balanced and Fuzzy Ant Colony optimization protocol for optimizing the clustering architecture in WSN,” International Journal of Communication Systems, Vol. 34, No. 18, pp. 4997, Dec 2021.
[27]N. Malisetti, and V. K. Pamula, “Energy efficient cluster-based routing for wireless sensor networks using Moth Levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm,” Microprocessors and Microsystems, Vol. 93, pp. 104593, Sep 2022.
[28]R. Sharma, V. Vashisht, and U. Singh, “EETMFO/Ga: A secure and energy efficient cluster head selection in wireless sensor networks,” Telecommunication Systems, Vol. 74, No. 3, pp. 253-268, Jul 2020.
[29]G. Kaur, P. Chanak, and M. Bhattacharya, “Energy-efficient intelligent routing scheme for IOT-enabled wsns,” IEEE Internet of Things Journal, Vol. 8, No. 14, pp. 1144-11449, Jan 2021.
[30]M. B. Shyjith, C. P. Maheswaran, and V. K. Reshma, “Optimized and dynamic selection of cluster head using energy efficient routing protocol in WSN,” Wireless Personal Communications, Vol. 116, No. 1, pp. 577-599, Jan 2020.
[31]B. Pitchaimanickam, and G. Murugaboopathi, “A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks,” Neural Computing and Applications, Vol. 32, No. 12, pp. 7709-7723, Jun 2019.
[32]K. Suresh Kumar, and P. Vimala, “Energy Efficient Routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks,” Computer Networks, Vol. 197, pp. 108250, Oct 2021.
[33]J. Ma, H. Xu, J. Jiang, X. Mei, and X.-P. Zhang, “DDcGAN: A dual-discriminator conditional generative adversarial network for Multi-Resolution Image Fusion,” IEEE Transactions on Image Processing, Vol. 29, pp. 4980-4995, Mar 2020.
[34]M. Dehghani, and H. Samet, “Momentum search algorithm: A new meta-heuristic optimization algorithm inspired by Momentum Conservation Law,” SN Applied Sciences, Vol. 2, No. 10, pp. 504-511, Oct 2020.
[35]S. Harifi, J. Mohammad zadeh, M. Khalilian, and S. Ebrahimnejad, “Giza pyramids construction: An ancient-inspired metaheuristic algorithm for optimization,” Evolutionary Intelligence, Vol. 14, No. 4, pp. 1743-1761, Dec 2020.