Article: Data aggregation in wireless sensor networks using Bayesian-based data encryption and fragmentation modelling Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2026 Vol.18 No.2 pp.201 - 230 Abstract: This paper proposes a model for data fragmentation and modelling in wireless sensor networks (WSNs) using the Bayesian fuzzy clustering (BFC) technique. Initially, WSN nodes are simulated, and the shuffled shepherd squirrel search optimisation algorithm (SSSSOA) is accomplished for choosing the cluster head (CH). Later, route maintenance operation is performed using the link quality metrics. Hierarchical fractional bidirectional least-mean-square (HFBLMS) is employed for data reduction and data aggregation. After that, the security of the nodes is ensured during data transmission using trust metrics. Besides, the proposed BFC approach is used in the data fragmentation and modelling phase where the elliptic curve cryptography (ECC) encryption and the adaptive data partitioning process are performed. Finally, the decryption and the de-blocking operations are performed. The introduced BFC approach achieved a detection ratio of 81.13%, delay of 0.105 s, packet deliver ration (PDR) of 98.13%, and energy of 2.502 J. Inderscience Publishers - linking academia, business and industry through research

Title: Data aggregation in wireless sensor networks using Bayesian-based data encryption and fragmentation modelling

Authors: L. Rajesh; H.S. Mohan; M.K. Bindiya

Addresses: Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Visvesvaraya Technological University, Bengaluru, Karnataka, 560082, India ' Department of CSE (Data Science), RNS Institute of Technology, Bengaluru, Karnataka, India ' Department of Computer Science and Engineering, S.J.B. Institute of Technology, Bengaluru, Karnataka, 560060, India

Abstract: This paper proposes a model for data fragmentation and modelling in wireless sensor networks (WSNs) using the Bayesian fuzzy clustering (BFC) technique. Initially, WSN nodes are simulated, and the shuffled shepherd squirrel search optimisation algorithm (SSSSOA) is accomplished for choosing the cluster head (CH). Later, route maintenance operation is performed using the link quality metrics. Hierarchical fractional bidirectional least-mean-square (HFBLMS) is employed for data reduction and data aggregation. After that, the security of the nodes is ensured during data transmission using trust metrics. Besides, the proposed BFC approach is used in the data fragmentation and modelling phase where the elliptic curve cryptography (ECC) encryption and the adaptive data partitioning process are performed. Finally, the decryption and the de-blocking operations are performed. The introduced BFC approach achieved a detection ratio of 81.13%, delay of 0.105 s, packet deliver ration (PDR) of 98.13%, and energy of 2.502 J.

Keywords: wireless sensor networks; data aggregation; shuffled shepherd optimisation; squirrel search algorithm; Bayesian fuzzy clustering; BFC.

DOI: 10.1504/IJIIDS.2026.152767

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.2, pp.201 - 230

Received: 22 Feb 2024
Accepted: 19 Nov 2024
Published online: 10 Apr 2026 *

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Title: Data aggregation in wireless sensor networks using Bayesian-based data encryption and fragmentation modelling

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