Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1523-1530· 0 citations· 21 references
Abstract
Wireless Sensor Networks (WSN) are significant for various applications, however ensuring data security and energy consumption remains a critical challenge. The conventional methods lacked sufficient security, exhibited communication overhead, and energy inefficiencies. Therefore, this research proposes the Distributed Fractional Hawk Optimization (DtFHO) algorithm to address the limitations in cluster head selection for secure WSN routing. The integration of fractional theory improves the convergence speed and exploitation balance in cluster head selection. To secure the data routing, a blockchain network is employed, which maintains a transparent record of routing paths while preventing malicious node entries. Furthermore, the modified End-to-End Homomorphic encryption enables secure data sharing without decrypting sensitive information at intermediate nodes. Through considering the multimetric factors, the DtFHO algorithm offers a secure routing path, making it highly effective for large-scale and sensitive network scenarios. The DtFHO showcases a robust performance by achieving a minimum transaction time of 2.013 seconds, memory usage of 347.95 Kilobytes, Gas usage of 345.84 Kilobytes, encryption time of 2.012 seconds, and a maximum throughput ratio of 0.748, normalized energy of 0.766 Joules, with 153 alive nodes compared to the conventional methods.
The results of statistical validation using the Wilcoxon Signed-Rank test confirm that both the reduction in energy consumption and network longevity offered by ECHVM are highly significant, justifying ECHVM as a mathematically sound, permanent, scalable security framework suitable for resource-constrained, dense Internet of Things (IoT) and smart sensing applications for next-generation communication systems.
A. A. Jasim, Noor Riyadh Issa, Hisham A. Shehadeh et al.· Scientific Reports· 0 citations
Simulation results suggest that the proposed EGWO-CS-based approach offers significant improvements in packet delivery ratio, energy efficiency, trust precision, and network lifetime compared to conventional GWO, GWO-CS, and other standard schemes.
Cailing Cheng, Hui Zhang, Li Fei et al.· Computing· 0 citations
Cognitive Radio Networks (CRNs) are a new era of wireless communication systems that enable secondary users to access spectrum bands opportunistically when primary users are not using them. Although a CRN can provide high-quality service and enhance spectrum utilization, there are still some important urgent problems to be solved, such as insecure communication, malicious nodes participating in the network, and unreliable routing performance. However, none of the existing security and communication schemes can achieve trusted entity validation, shortest-path optimization, and communication reliability simultaneously in CRNs. To address these challenges, this paper presents the AI assisted Blockchain Decentralized Zero Trust Authentication (BDZTA) approach for secure communication in CRN. Initially, the proposed Trust-Energy Aware Transmission Node Assessment (TEATNA) method is employed to identify the reliable transmission nodes. Then, the Adaptive Graph Neural Network (AGNN) model is used to classify the optimal shortest communication path by capturing the dynamic topological structure among cognitive radio nodes. After optimal route selection, Quantum-Inspired Whale Optimization with Elliptic Curve Cryptography (QIWO-ECC) approach is utilised for key generation and lightweight data encryption. Subsequently, the BDZTA approach is used to enable secure, decentralized communication through continuous identity verification. Finally, the Proof of Authority Verification (PoAV) scheme is used to validate authorized communication entities and ensure secure participation in transactions. The integrated framework significantly improves secure communication, trusted routing, Packet Delivery Ratio (PDR), energy efficiency, end-to-end delay, and energy consumption. The results of the experimental analysis show that the proposed approach achieves the best performance among existing schemes, thereby providing strong, reliable, and intelligent communication in CRNs.
T. Sundar, A. Senthilkumar· International journal of com...· 0 citations
The Internet of Medical Things (IoMT) has numerous prospective applications for remote health monitoring in the medical field. The two main issues that significantly affect IoMT’s performance are end-to-end latency and energy efficiency. An energy-efficient architecture and reliable packet communication are necessary for design and implementation of an application. It is very crucial to transmit accurate packet with real time and reliable manner. A cluster-based and energy-efficient routing protocol using the Blockchain-enabled joint trust (FG-WHO-BWOA) method in IoMT has been presented to address these problems. It finds the network’s optimal cluster head (CH) and ensures secure packet communication. Here, the firefly and grey wolf optimization (FG-WHO) technique is applied to precisely pick CH. The vulnerable or malicious node appears in a cluster following the selection of CH. In order to identify the trusted path among the various routes, the multi-objective Beluga Whale Optimization Algorithm (BWOA) is suggested. Finally, the block chain receives the optimal chosen trust channels for safer and more reliable network transmission. Castalia Simulator is used to perform the simulation. High throughput, high efficiency, long network lifetime, high packet delivery ratio, low latency, low consumption of energy, low packet loss ratio and security are all attained by the suggested FG-WHO-BWOA. The performance validates that, in contrast to the current approaches, the mentioned strategy performs better.
Sunayana Das, Tusharkanta Samal, N. R. Samal et al.· Scientific Reports· 0 citations
Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
Narendra Kumar Dewangan, M. Msahli· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
