Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Wireless Sensor Networks (WSNs) are generally constrained by their built-in energy limits, leading to very short operational lifespans. Moreover, the node energy reserves of homogeneous WSNs are far lower than those of heterogeneous WSNs. For this reason, energy-efficient routing is a core optimization direction for this type of network. 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. We propose to enhance energy efficiency 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. It is a widely agreed conclusion in the field that hierarchical routing for heterogeneous WSNs outperforms flat routing and location-based routing in both energy efficiency and scalability. This paper also implements three supporting technologies: topology control, adaptive routing, and power management. These technologies reduce energy consumption through multi-hop clustering and optimized relay selection, ultimately supporting more flexible routing strategies, extending the network's operational cycle, and meeting the application requirements in the Internet of Things and information technology fields.
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
Sushma Priyadarshini, A. T· International journal of com...· 0 citations
A cluster-based proactive routing protocol designed for three-tier energy-heterogeneous WSNs, aiming to enhance network lifetime and energy efficiency, and modified to address the challenge of long transmission distances for low-power nodes by strategically deploying them in specific network zones.
F. A. Mohamed, E. Hassan, M. Dessouky et al.· Scientific Reports· 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
A distributed K-means clustering algorithm integrated with a modified low energy adaptive clustering hierarchy-improved energy distance protocol for clustering and CH selection achieves improved network lifetime, energy balancing, and scalability while maintaining competitive throughput performance compared with existing protocols.
Md. Yasin Arafat, M. Drieberg, A. A. Aziz et al.· Engineering Research Express· 0 citations
Modern technological systems rely heavily on Wireless Sensor Networks (WSNs), which support many kinds of applications, including but not limited to: (1) environmental monitoring (2) medical monitoring and (3) smart city/infrastructure development. One major problem with extending the overall life of the network is the limited power source of the sensor nodes; therefore, energy-efficient communication has become one of the primary research areas. The direction of this work is a GA-based routing mechanism developed to minimize energy usage within WSNs. The proposed methodology employs evolutionary operators (e.g., selection, crossover, and mutation) that will adaptively develop low-energy routing paths and provide for an even distribution of traffic across all nodes. Also, node clustering, dynamic data aggregation, and multi-objective optimization are all methods used to improve network energy efficiency while not compromising the reliability of the data or the stability of the network. The simulations performed show that the proposed GA-based routing protocol offers improved power consumption, packet delivery rate and overall longevity of the network when compared to traditional methodologies such as LEACH or other energy aware GA methods. Additionally, the implementation analysis indicates a very high level of adaptability to node movement and variable traffic patterns, suggesting that it is robust and scalable. Thus, this research will help promote the development of energy-efficient wireless sensor networks (WSNs) as well as lay a solid foundation for future intelligent routing and effective resource management within future WSNs.
T. Sarkar, Manik Rakhra· 2026 International Conferenc...· 0 citations
Energy-efficient and robust routing remains a critical problem in wireless sensor networks (WSNs), where limited energy resources and dynamic topologies hinder performance. To address this challenge, a novel cluster-based routing protocol FGOGNN is proposed in this paper, which integrates a Fungal Growth Optimizer (FGO) for adaptive cluster head (CH) selection and a Graph Neural Network (GNN) for inter-cluster routing. The FGO simulates fungal growth processes, ensuring balanced and energy-efficient CH selection while preventing premature convergence. In the routing phase, the GNN dynamically adapts routing paths by leveraging node energy, connectivity, and directional edge features, offering low computational overhead while maintaining accuracy. Extensive simulations show that FGOGNN outperforms existing routing protocols, extending network lifetime by up to 75% and improving throughput by 38%, with a reduction in end-to-end delay. These results demonstrate FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.
Huang-shui Hu, Shuo Liu, Qier Kang et al.· Symmetry· 0 citations