2026· International Journal of Latest Technology in Engineering, Management & Applied Science· 0 citations
TL;DR
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.
Abstract
Wireless sensor networks (WSNs) are crucial in various scientific, industrial and infrastructure monitoring applications, in which the sensing, processing, and communication tasks are performed by the distributed sensors, without the presence of any human beings. WSNs have various drawbacks, among which is the power limitation of sensor nodes, which impacts the life of the network and its reliability, due to their battery power. The main problem is the selection of cluster heads (CHs) as CH selection in conventional protocols may be ad hoc/Random which may cause energy imbalance and network failure, which is very common in conventional protocols. The main idea of this study is to present 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. The proposed MO model is tested against three baseline models: single-cluster arbitrary selection (SA), multi-cluster arbitrary selection (MA), and single-cluster optimal selection (SO) and tested over 50, 100 and 200 network sizes. Residual energy, number of alive nodes, total energy consumption, network stability and PSO convergence rate are used to evaluate the performance. In all three sizes of the network, the MO model always consumes less energy than the baseline models (up to 52% in the three networks at 200 nodes, when the number of alive nodes is half that of the MO model at equivalent simulation rounds) and significantly outperforms all three baseline models in terms of normalized size stability (up to 1.00 in the three networks at 100 nodes, compared with at most 0.30 for the baseline models). The obtained results indicate that multi-clustering and joint optimization of CH selection, taking into account multiple energy relevant criteria yields substantial, stable gains in energy-efficiency and life-time of the WSN in comparison to arbitrary CH selection and a single criterion CH selection scheme.
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
In modern times, wireless sensor networks (WSNs) are necessary for remote system surveillance and information exchange. Despite the advantages of WSNs, node lifetime and data loss limit their use. Several researchers use energy‐efficient protocols to solve the aforementioned issues in WSNs. However, the majority of authors focused exclusively on the energy‐efficient model for solving the issues. Yet, this results in high energy consumption and repetitive processes. So, this research developed a cluster head (CH) selection method using the fuzzy model for effective data transmission. The main contribution of the proposed work is to perform secure data transmission within the WSN by reducing and balancing energy consumption. Initially, the necessary network attributes are gathered from the available resources. The adaptive fuzzy C‐means clustering (AFCMC) concept is employed in clustering the WSN. In the cluster‐based WSN, CH is optimally chosen with the support of the improved black widow optimization algorithm (IBWOA). Here, the parameter in AFCMC is optimally tuned by the same IBWOA. The tuned parameters include the fuzziness parameter (2–20), epsilon (1–10), and the number of iterations (10–100). Subsequently, the data transmission is conducted with the support of the deep state–action–reward–selection algorithm (Deep SARSA). In this, the suggested Deep SARSA helped to perform the data transmission securely through the shortest path and improved the detection of malicious nodes. The newly designed approach is compared with the solutions in the literature. The established IBWOA‐AFCMC models in terms of residual energy are 16.66%, 21.73%, 19.14%, and 55.55% more effective than KMC, hierarchical clustering, biclustering, and FCM in the 100th number of nodes. This dual‐layer approach significantly enhances network longevity and data integrity in dynamic WSN environments.
Rupa Kesavan, Yaashuwanth Calpakkam, Vijayaraja Loganathan et al.· International Journal of Com...· 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
Wireless sensor networks (WSNs) are sophisticated monitoring systems that gather environmental data via wireless sensors. Numerous wireless sensors that have been placed to monitor and gather data on a particular environment make up these networks. Typically, a base station or central node wirelessly collects the data prior to analysis. As the battery in wireless sensors is both non-replaceable and non-rechargeable, it represents a key element. As a result, optimizing energy consumption in WSN has become a growing concern. One of the key challenges is consequently the creation of effective protocols for communication in WSNs. In this article, we provide a novel MSA (Mosquito Swarm Algorithm) technique for cluster formation and data routing. Simulations indicate that our proposed algorithm conserves the energy of the nodes and keeps them running for a greater number of survival rounds compared to LEACH (Low-Energy Adaptive Clustering Hierarchy) by a difference of 70.14%, PSO-R (Particle Swarm Optimization with routing) by a difference of 4.76%, and BA-R (Bat Algorithm with routing) by a difference of 2.52%. Our algorithm provides the highest throughput, surpassing LEACH by approximately 6.38%, PSO-R by almost 1%, and BA-R by 12.67%. Simulations indicate that our algorithm is highly effective at extending network longevity and increasing throughput, making it the preferred option to lower energy consumption in WSNs.
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
Wireless Sensor Networks (WSNs) use resource-constrained sensor nodes to continuously monitor ambient conditions and serve as data sinks for various Internet of Things (IoT) applications. However, maximizing Energy Efficiency (EE) while maintaining reliable data delivery remains a significant challenge. Existing clustering and routing algorithms struggle with issues such as uneven energy consumption, premature node failures, and poor network performance. Additionally, many existing metaheuristic schemes are not adaptable to dynamic network environments, which leads to ineffective energy management. To address the above issues and enhance the energy efficiency of IoT-based WSN, this research introduced a novel Energy-Aware Cluster-Optimized Intelligent Routing (EACO-IR) protocol. The EACO-IR protocol performs in three stages: stable cluster formation, Cluster Head (CH) selection, and energy-aware route finding. At first, stable clusters are formed using the Hybrid Fuzzy–Density Adaptive Kronecker Clustering (HF-DAC) algorithm. Subsequently, CHs are optimally selected using the Mutation-Enhanced Armadillo–Devil Optimization (MEADO) based on a multi-objective function for the Base Station (BS). Finally, the Multi-level Energy-Aware Attention Transformer- Based Reinforcement Learning is introduced to create intra and inter-cluster data travel ways to minimize communication overhead from SNs to the BS. Experimental results show that the proposed protocol yields an average throughput of 4 Mbps, an average Packet Delivery Ratio (PDR) of 98.71%, and an end-to-end latency of 0.06 seconds, outperforming current state-of-the-art clustering and routing algorithms. Ultimately, this framework establishes a highly adaptable template for deploying self-optimizing, long-lasting IoT architectures capable of supporting real-time data streaming without premature network degradation.
P. Kumbhar, A. Naik· International Journal of Ele...· 0 citations
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