2026· International journal of research and scientific innovation· 0 citations
TL;DR
Simulation analyses conducted in NS-3 demonstrate that the proposed integrated, multi-tier optimization framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
Abstract
Wireless Sensor Networks (WSNs) are increasingly deployed in resource-constrained and hostile environments for critical applications such as environmental monitoring, industrial automation, and structural auditing. However, their real-world efficacy is severely limited by battery energy constraints and high vulnerability to routing-layer security threats. Conventional cryptographic solutions incur excessive computational overhead, while standard energy-efficient routing protocols often neglect security. To resolve this trade-off, this paper proposes an integrated, multi-tier optimization framework combining three novel algorithms: Proficient Secure Cluster Head Selection (PSCH), Total Path Trust Assessment Routing (TPTAR), and Energy-Proficient, Secure, and Trust-Based Shortest Path Optimized Routing (EST-SPOR). The proposed architecture utilizes fuzzy logic for cluster-head selection, a dynamic trust evaluation model combining direct and reference trust metrics, and an optimized shortest-path routing mechanism based on Ant Colony Optimization (ACO) principles. Simulation analyses conducted in NS-3 demonstrate that the framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
The results validate the utility of the proposed approach in the provision of trust-based, energy-efficient, and secure routing in the dynamic MWSNs and demonstrate that the given framework outcompetes the existing ones.
Ms.D. Swapna, Dr.M.Nagaratna· Journal of Intelligent Decis...· 0 citations
This study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach, offering a scalable and energy-efficient solution for large-scale sensor networks.
Manisha Chandrakar, A. Hasan· International Journal of Wir...· 0 citations
The 5G-enabled Wireless Sensor Networks (WSN) use the increased capabilities of 5G technology to represent the next version of conventional WSN environments. WSN performance may be affected by interference from high-density 5 G networks. The novel Hummingbird-based Graph Bernoulli Binomial Trust Management Network (HBbGBBTMN) proposed in this research is to enhance smart, secure, and energy-saving routing in 5G-enabled wireless networks. Python is initially used to simulate and model the network under consideration, accounting for fluctuating network conditions and dynamic node dynamics. An improved Hummingbird algorithm is used to detect and remove nodes with high energy consumption, thereby minimizing routing inefficiencies and avoiding suspicious behavior. To protect data integrity and prevent route disruption, malicious nodes are continuously detected and removed. The trust of the remaining nodes is calculated using the Bernoulli-Binomial distribution, which estimates each node's trust based on its previous packet-forwarding history. Such trust mechanisms are combined with node energy levels as well as network dynamics to form a fitness function that identifies optimal routing patterns. The suggested system is validated through extensive performance analysis, including measurements of packet delivery ratio, throughput, packet drop rate, delay, and malicious node prediction accuracy. The findings indicate that in decentralized wireless environments, HBbGBBTMN significantly enhances network efficiency, security, and dependability.
S. M., Mrinal Sarvagya· International Journal of Inf...· 0 citations
Wireless Sensor Networks (WSNs) are increasingly employed in various applications, that require efficient, energy-saving and secure transmitting mechanisms. Clustering is a key technique in WSN to enhance scalability, reduce energy consumption, and manage communication. However, tradi-tional clustering algorithms often lack adaptability to alterable wireless environments. This study presents an optimized secure clustering scheme for Wireless Sensor Networks that employs a fuzzy inference system to perform adaptive and secure cluster head selection. The proposed scheme is designed for WSN with a static topology, where sensor nodes remain fixed after de-ployment. This assumption matches Internet of Medical Things (IoMT) applications, such as patient monitoring systems and smart hospital infrastructure, where sensors operate in fixed locations. The proposed fuzzy inference system evaluates parameters of sensor nodes to select optimal cluster heads while mitigating malicious node participation. Next, a genetic algorithm was applied to optimize the parameters of the fuzzy inference system, including the membership function shapes and rule base indices, in order to enhance the accuracy of the proposed clustering scheme. MATLAB simulations demonstrate that the proposed scheme effectively identifies compromised sensor nodes and prevents them from assuming the cluster head role. The security validation confirms that the scheme achieves a high detection rate against internal attacks while extending overall network lifetime and maintaining the low latency. This study provides a feasible solution for secure and intelligent clustering in resource- constrained WSN.
Vincent Karovič, O. Semenova, M. Prytula et al.· Infocommunications journal· 0 citations
The core research goal of this paper is to optimize dynamic routing protocols to improve the performance of the classic LEACH protocol in heterogeneous WSNs through a real-time adaptive scheme, which relies on two core methods: a cluster head selection mechanism based on the residual energy criterion, and a priority hop count strategy.
Vishwajit K. Barbudhe, Shruti Dixit· International journal of com...· 0 citations
MANETs for the Internet of Things (IoT) are evolving to the next level of robust connectivity. However, the MANET-IoT ecosystem faces three main challenges during routing: energy efficiency, security, and reliability. By using various frameworks and technologies to provide an end-to-end solution, Bayesian Directed Acyclic Graph based Mobile Adhoc Networks (BDAG-MANET) and Holochain Mobile Adhoc Networks (Holochain-MANET) address those problems with MANET data transfer. BDAG-MANET uses the Cube Hash and Bimodal Lattice Signature Scheme (BLISS) algorithms, respectively, to perform multifactor authentication to the mobile nodes depending on the security credentials. NS-3.26 simulation tool is used to implement PSO (particle swarm optimization) based on Trusted Adhoc on Demand Vector Routing (TAODV) to ensure security. The numerous properties of the mobile nodes in the Holo chain-MANET environment must be used to verify their validity to the trusted authority, utilizing the Improved Bimodal Lattice Signature Scheme (IBLISS) algorithm. The proposed work uses the Improved Fuzzy C-Means (IFCM) algorithm to perform intelligent clustering on authorized mobile nodes based on many factors.
Preethi D, S. Aswath, Bhuvaneshwari V et al.· 2026 International Conferenc...· 0 citations
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