2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 27 references
TL;DR
The proposed Intelligent and Interoperable Cat Swarm Optimizer (2I-CSO) is the first introduced for bio-inspired WSN clustering protocols and achieves faster convergence, lower computational cost, and competitive network lifetime compared to standard CSO and the Emperor Penguin Optimizer.
Abstract
Wireless sensor networks (WSNs), fundamental building block of IoT, are subject to several constraints because of the finite non-rechargeable energy resources available in the nodes. The selection of Cluster Head (CH) plays a critical role in determining the energy balance in a network. Conventional methods such as the LEACH algorithm choose CH randomly with a probability mechanism that might lead to choosing weak nodes as CHs and thereby fail prematurely. The biologically -inspired optimization methods, such as PSO and CSO, help to enhance CH selection using a global approach. However, these methods suffer from the following three major shortcomings: 1) random switching between the exploration and exploitation stages, 2) lack of intelligence during the formation of clusters, and 3) growing exponentially complex search space of CHs.This study proposes an Intelligent and Interoperable Cat Swarm Optimizer (2I-CSO), a protocol designed to address these limitations simultaneously. 2I-CSO also introduces an interoperable configuration mechanism based on LEACH’s hierarchical architecture, where the Base Station maintains a centralized energy configuration table shared with Cluster Heads and member nodes, ensuring network-wide parameter consistency and enabling the intelligent stopping mechanism. Experiments conducted on five well-known TSPLIB test cases and WSN simulations demonstrate that 2I-CSO outperforms individual metaheuristics. Simulation results on a custom web-based platform further show that 2I-CSO achieves faster convergence, lower computational cost, and competitive network lifetime compared to standard CSO and the Emperor Penguin Optimizer (EPO). To the best of our knowledge, the proposed intelligent stopping condition is the first introduced for bio-inspired WSN clustering protocols.
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
A multi-cluster WSN model with optimal CH selection (the MO model) in which the CHs are selected by a particle swarm optimization (PSO) algorithm, by solving a multipurpose optimization problem based on three criteria: residual energy, distance between candidate CHs and the base station, and intra-cluster distance.
I. Kamil, Goodness Adeleke Adetokun· International Journal of Lat...· 0 citations
Simulations conducted in MATLAB R2019a validate that the proposed MOPSO outperforms existing algorithms such as LEACH, LEACH-FL, LEACH-FC, KM-PSO, EECHS-ARO, HSWO, and EECHIGWO by mitigating premature convergence and enhancing CH selection accuracy.
Monisha Gupta, Chandrasekar Vadivelraju· Review of Computer Engineeri...· 0 citations
The next-generation Intelligent Transportation Systems (ITS) use Vehicular Ad Hoc Networks. This facilitates real-time vehicle-roadside infrastructure communication. However, maintaining stable cluster topologies and guaranteeing consistent Quality of Service is problematic because of VANETs intrinsic high mobility and adaptable topology. In this paper, proposed a Self-Adaptive Marine Predators Algorithm (SAMPA) with an Aquila Optimizer-based Multi-Objective Routing (AOMOR) strategy for Cluster Head Selection (CHS). Instead of demanding all nodes to maintaining links over the entire network, the local interactions among clusters facilitates in lowering the routing complexity. SAMPA, a nature-inspired metaheuristic algorithm, models the foraging strategies of marine predators in their search for prey. It is employed here to select the most suitable CHs by solving a Multi-Objective Optimization (MOO) problem that balances key metrics, including Node Density (ND), Residual Energy (RE), mobility, Link Quality (LQ), and Connectivity Degree (CD). For the routing process, AOMOR employs the hunting strategies of Aquila (eagle) species, which ivolnve searching, swooping, and attacking behaviors, to determine the optimal communication path. Mean Routing Load (MRL), Packet Delivery Ratio (PDR), throughput, End-to-End (E2E) delay, and Control Packet Overhead (CPO) represent several of the objectives, are taken into account by QoS-aware routing. To further enhance adaptability, the Digital Twin technology is integrated in the suggested method, which facilitates real-time analysis of network activities, traffic prediction, and informed decision-making for routing strategies. Proposed structure in improving the efficiency of CHS and improving QoS provisioning in VANETs are demonstrated by evaluating its performance using important metrics such as PDR, Packet Loss Ratio (PLR), E2E delay, throughput, and Average Residual Energy (ARE).
Unknown authors· international journal of eng...· 0 citations
Internet of Things (IoT) involves a large number of interconnected sensor nodes, which sense, communicate, and become active in data processing in resource-constrained settings. This paper proposes a hybrid grey wolf optimization and squirrel search algorithm (GWO-SSA) for optimal cluster head (CH) selection in an IoT network to enhance energy efficiency, reduce delay, and prolong network lifetime. The proposed model integrates SSA into GWO to enhance exploration and exploitation balance, enabling efficient selection of CHs based on temperature, delay, energy, load, and cost function. Experimental results demonstrate that GWO-SSA significantly outperforms existing methods such as GA, ACO, PSO, IPSO, GWO, SSA, and BCO. The proposed approach reduces temperature by 11.93 % and delay by 32.82 % compared to GA, while achieving an energy efficiency improvement of 22.61%. Additionally, the number of alive nodes increased by 21.27%, indicating a substantial enhancement in network lifetime. Load handling capability is improved by 12.70 %, and the cost function is reduced by 15.35 %, confirming effective optimized performance. The GWO-SSA approach provides a robust, scalable, and energy-efficient cluster solution for an IoT environment. The significant improvements across multiple performance metrics validate the effectiveness of the hybrid approach, making it suitable for real-time and large-scale sensor network applications.
N. Ramireddy, K. Prakash· International journal of mat...· 0 citations
While wireless energy transfer offers a practical solution to the problem of energy scarcity in Wireless Sensor Networks (WSNs), the efficient management of Wireless Rechargeable Sensor Networks (WRSNs) in smart environments still faces two main inhibiting challenges: the inefficiency of single-charger architectures in large-scale deployments (lack of scalability) and the absence of adaptive, real-time scheduling mechanisms. Motivated by this, this paper introduces the Objective-Oriented Intelligent Charging Coordinator (OOICC), an innovative scheduling and charging resource allocation framework designed to overcome these limitations through a comprehensive two-layer strategy. By fundamentally rethinking network management, OOICC first dynamically determines the optimal number of Mobile Chargers (MCs) required for scalable coverage, moving beyond the conventional limitations of single chargers. Second, it establishes a hybrid offline-online decision-making architecture that synergistically combines strategic planning with operational adaptability. At the core of OOICC lies a formally defined multi-objective optimization framework that incorporates designer-specified characteristics into a comprehensive ten-fold objective function. This ensures the balanced pursuit of critical metrics such as charging latency, node survival rate, and network throughput. The coordinator operates through an intelligent workflow whereby the offline phase employs fuzzy clustering guided by a metaheuristic optimizer to create an optimal initial network partition and MC assignment. This framework actively interacts with an online phase, where an intuitive fuzzy logic engine performs context-aware charging scheduling by processing live network data in real-time. Through extensive simulations, OOICC demonstrates superior and consistent performance compared to established benchmarks. The method achieves significant improvements in charging response time, node survival rate, energy consumption efficiency, network stability, and data throughput. Quantitatively, the proposed framework outperforms the TSFM, CFMCRS, FLCSD, ESS, and NJNP methods by 8%, 48%, 68%, 73%, and 89%, respectively, confirming its efficacy as a robust and scalable solution for next-generation WRSN management.
Fakhrosadat Fanian, M. Kuchaki Rafsanjani, A. Borumand Saeid· Scientific Reports· 0 citations
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