International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 1064KB)
AI-based Secure Cluster Formation and Reliable Data Transmission for Wireless Sensor Networks
PDF (1064KB), PP.32-49
Views: 0 Downloads: 0
Author(s)
Srinivasamurthy. R.
1,*
Prameela kumari. N.
1
Nikhath Tabassum
1
Index Terms
Secretary Bird Optimisation, Wireless Communication, Cluster Head, Energy Consumption
Abstract
Clustering in wireless sensor networks (WSNs) offers numerous desirable properties, including load balancing, energy conservation, and distributed key management. Secure Clustering requires it to detect compromised nodes and remove them from clusters during setup. Suppose some nodes are attacked and pass the filtering. In that case, they can modify some nodes to adopt a different clustering perspective, as well as initiate new clusters to degrade the overall cluster quality. To address these issues, a new method, Secretary Bird with Self-Organizing Maps (SBWSOM), has been designed to detect and eliminate malicious nodes while efficiently providing data. First, the appropriate sensor nodes were constructed in Python. Second, the malicious node was located and destroyed, and the Cluster Head (CH) was picked based on parameters such as remaining energy, network level, and base station (BS) location. Furthermore, the data rates of chosen CHs have been confirmed and sent to empty nodes. Lastly, the values compared and studied were Latency, throughput, packet delivery ratio (PDR), energy consumption, and transmission loss. The evaluation of this proposal demonstrated improved data transfer, with a throughput of 0.91, an energy consumption of 0.46 mJ, and a packet delivery ratio of 96.3%. Also, the transmit loss was 4.20%, and Latency was 6.04 ms. Overall, this method performed well, with significant improvement over previous models.
Cite This Paper
Srinivasamurthy. R., Prameela kumari. N., Nikhath Tabassum, "AI-based Secure Cluster Formation and Reliable Data Transmission for Wireless Sensor Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.4, pp.32-49, 2026. DOI:10.5815/ijitcs.2026.04.03
Reference
[1]G.P. Dubey, S. Stalin, O. Alqahtani, A. Alasiry, M. Sharma, A. Aleryani, M.T.H. Alouane, "Optimal path selection using reinforcement learning based ant colony optimization algorithm in IoT-Based wireless sensor networks with 5G technology," Comput. Commun. vol. 212, pp. 377-389, 2023. https://doi.org/10.1016/j.comcom.2023.09.015
[2]Z. Li, H. Liu, C. Zhang, G. Fu, "Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data," Water Res. vol. 250, pp. 121018, 2024. https://doi.org/10.1016/j.watres.2023.121018
[3]E. Alinezhad, V. Gan, V.W. Chang, J. Zhou, "Unmanned Ground Vehicles (UGVs)-based mobile sensing for Indoor Environmental Quality (IEQ) monitoring: Current challenges and future directions," J. Build. Eng. pp. 109169, 2024. https://doi.org/10.1016/j.jobe.2024.109169
[4]M. Yuan, Y. Geng, B. Lin, H. Tang, Y. Yang, "Optimization of indoor temperature sensor deployment in large spaces for multiple building operation scenarios using the genetic algorithm," J. Build. Eng. vol. 96, pp. 110446, 2024. https://doi.org/10.1016/j.jobe.2024.110446
[5]S. Kumari, A.K. Tyagi, "Wireless sensor networks: An introduction," Digital Twin and Blockchain for Smart Cities. 495-528, 2024. https://doi.org/10.1002/9781394303564.ch21
[6]M. Papaioannou, G. Mantas, F.B. Saghezchi, G. Kambourakis, F. Gil‐Castiñeira, R. S. de la Cámara, J. Rodriguez, "Threat Landscape for 6G‐Enabled Massive IoT," Security and Privacy for 6G Massive IoT.pp. 1-34, 2025. https://doi.org/10.1002/9781119988007.ch1
[7]A. Anjum, P. Agbaje, A. Mitra, E. Oseghale, E. Nwafor, H. Olufowobi, "Towards named data networking technology: Emerging applications, use cases, and challenges for secure data communication," Future Generation Comput Syst. vol. 151, pp. 12-31, 2024. https://doi.org/10.1016/j.future.2023.09.031
[8]S. Sivakumar, J. Logeshwaran, R. Kannadasan, M. Faheem, D. Ravikumar, "A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0," Energy Sci. Eng. vol. 12, no. 3, pp. 835-859, 2024. https://doi.org/10.1002/ese3.1657
[9]B. Yamini, G. Pradeep, D. Kalaiyarasi, M. Jayaprakash, G. Janani, G.S. Uthayakumar, "Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)," Measurement: Sensors. vol. 33, pp. 101098, 2024. https://doi.org/10.1016/j.measen.2024.101098
[10]Y. Qiu, L. Ma, R. Priyadarshi, 'Deep learning challenges and prospects in wireless sensor network deployment," Arch. Comput. Methods Eng. vol. 31, no. 6, pp. 3231-3254, 2024. https://doi.org/10.1007/s11831-024-10079-6
[11]V. Sharma, R. Beniwal, V. Kumar, "Multi-level trust-based secure and optimal IoT-WSN routing for environmental monitoring applications," J. Supercomput. vol. 80, no. 8, pp. 11338-11381, 2024. https://doi.org/10.1007/s11227-023-05875-z
[12]M. Saadati, S.M. Mazinani, A.A. Khazaei, S.J.S.M. Chabok, "Energy efficient clustering for dense wireless sensor network by applying graph neural networks with coverage metrics," Ad Hoc Netw. vol. 156, pp. 103432, 2024. https://doi.org/10.1016/j.adhoc.2024.103432
[13]C. Prakash, L.P. Singh, A. Gupta, S.K. Lohan, 'Advancements in smart farming: A comprehensive review of IoT, wireless communication, sensors, and hardware for agricultural automation," Sensors and Actuators A: Physical, vol. 362, pp. 114605, 2023. https://doi.org/10.1016/j.sna.2023.114605
[14]S. El Khediri, A. Selmi, R.U. Khan, T. Moulahi, P. Lorenz, 'Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache's," Ad Hoc Netw. vol. 158, pp. 103473, 2024. https://doi.org/10.1016/j.adhoc.2024.103473
[15]H. Bashiri, H. Naderi, "LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches," Social Netw. Analysis Mining. vol. 14, no. 1, pp. 102, 2024. https://doi.org/10.1007/s13278-024-01268-z
[16]A.G. Oskouei, N. Samadi, J. Tanha, "Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering," Appl. Soft Comput. vol. 161, pp. 111712, 2024. https://doi.org/10.1016/j.asoc.2024.111712
[17]Rekha, R. Garg, "K-Lion ER: meta-heuristic approach for energy efficient cluster based routing for WSN-assisted IoT networks," Cluster Comput. vol. 27, no. 4, pp. 4207-4221, 2024. https://doi.org/10.1007/s10586-024-04280-2
[18]R.S. Raj, L.K. Hema, "Dynamic clustering optimization for energy efficient IoT Network: A simple constrastive graph approach," Exp Syst Appl. vol. 264, pp. 125875, 2025. https://doi.org/10.1016/j.eswa.2024.125875
[19]I. Surenther, K.P. Sridhar, M.K. Roberts, "Enhancing data transmission efficiency in wireless sensor networks through machine learning-enabled energy optimization: A grouping model approach," Ain Shams Eng. J. vol. 15, no. 4, pp. 102644, 2024. https://doi.org/10.1016/j.asej.2024.102644
[20]S. Aminizadeh, A. Heidari, M. Dehghan, S. Toumaj, M. Rezaei, N.J. Navimipour, M. Unal, "Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service," Artif. Intell. Med. vol. 149, pp. 102779, 2024. https://doi.org/10.1016/j.artmed.2024.102779
[21]Z Cai, Z. Gu, K. He, "A self-adaptive density-based clustering algorithm for varying densities datasets with strong disturbance factor," Data Knowl. Eng. vol. 153, pp. 102345, 2024. https://doi.org/10.1016/j.datak.2024.102345
[22]L Sahoo, S.S. Sen, K. Tiwary, S. Moslem, T. Senapati, "Improvement of wireless sensor network lifetime via intelligent clustering under uncertainty," IEEE Access. vol. 12, pp. 25018-25033, 2024. DOI: 10.1109/ACCESS.2024.3365490
[23]K. Haseeb, F.F. Alruwaili, A. Khan, T. Alam, A. Wafa, A.R. Khan, "AI assisted energy optimized sustainable model for secured routing in mobile wireless sensor network," Mob. Netw. Appl. vol. 29, no. 3, pp. 867-875, 2024. https://doi.org/10.1007/s11036-024-02327-7
[24]H. Ahmad, M.M. Gulzar, S. Aziz, S. Habib, I. Ahmed, "AI-based anomaly identification techniques for vehicles communication protocol systems: Comprehensive investigation," research opportunities and challenges, Intern. Things. pp. 101245, 2024. https://doi.org/10.1016/j.iot.2024.101245
[25]P. Parthasarathi, S.N. Sangeethaa, S. Nivedha, "Secure Group Communication Among IoT Components in Smart Cities. In Recent Advances in Energy Systems," Power and Related Smart Technologies: Concepts and Innovative Implementations for a Sustainable Economic Growth in Developing Countries (pp. 513-532). Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-29586-7_20
[26]G. Zhu, Z. Lyu, X. Jiao, P. Liu, M. Chen, J. Xu, P. Zhang, "Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G," Sci. China Inf. Sci. vol. 66, no. 3, pp. 130301, 2023. https://doi.org/10.1007/s11432-022-3652-2
[27]S.K. Gupta, S. Patel, P.K. Mannepalli, S. Gangrade, "Designing Dense-Healthcare IOT Networks for Industry 4.0 Using AI-Based Energy Efficient Reinforcement Learning Protocol," In Industry 4.0 and Healthcare: Impact of Artificial Intelligence (pp. 37-58), Singapore: Springer Nature Singapore, 2023. https://doi.org/10.1007/978-981-99-1949-9_3
[28]X. Xue, R. Shanmugam, S. Palanisamy, O.I. Khalaf, D. Selvaraj, G.M. Abdulsahib, "A hybrid cross layer with harris-hawk-optimization-based efficient routing for wireless sensor networks," Symmetry. vol. 15, no. 2, pp. 438, 2023. https://doi.org/10.3390/sym15020438
[29]J. Vellaichamy, S. Basheer, P.S.M. Bai, M. Khan, S. Kumar Mathivanan, P. Jayagopal, J.C. Babu, "Wireless sensor networks based on multi-criteria clustering and optimal bio-inspired algorithm for energy-efficient routing," Appl. Sci. vol. 13, no. 5, pp. 2801, 2023. https://doi.org/10.3390/app13052801