Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-7· 0 citations· 19 references
Abstract
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.
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.
Vishwajit K. Barbudhe, Shruti Dixit· International journal of com...· 0 citations
Wireless sensor networks (WSNs) have gained significant attention in recent research due to their potential in various applications. However, energy conservation remains a critical challenge, as the nodes within these networks operate on non-replaceable batteries. This paper introduces a cluster-based proactive routing protocol designed for three-tier energy-heterogeneous WSNs, aiming to enhance network lifetime and energy efficiency. The proposed protocol incorporates a modified threshold-based cluster head (CH) selection mechanism, which considers both the energy levels and distances of sensor nodes (SNs) relative to the base station (BS). This approach prioritizes nodes with higher energy and closer proximity to the BS, increasing their likelihood of being elected as CHs. Furthermore, this protocol minimizes the formation of unnecessary CHs, particularly for SNs with low remaining energy, thereby reducing energy waste. Moreover, the proposed protocol is modified to address the challenge of long transmission distances for low-power nodes by strategically deploying them in specific network zones, thus further optimizing energy efficiency. Simulation results demonstrate that the modified protocol outperforms existing protocols such as SEP, IMPLEACH, and EEECA-THWSN in terms of total network lifetime, stability period (first node death), half-node death, last node death, throughput, and normalized remaining energy. Numerically, the proposed protocol with normal zone improves network performance, achieving an increase of up to 27% in network lifetime and throughput compared to other protocols.
F. A. Mohamed, E. Hassan, M. Dessouky et al.· Scientific Reports· 0 citations
A Wireless Sensor Network (WSN) refers to the network of spatially dispersed sensors that records and monitors physical conditions of an environment and forwards the obtained data to Base Station (BS). These networks are widely employed in applications like smart cities, environmental monitoring, and industrial automation. However, minimizing delay in energy-efficient clustering and routing is challenging because energy constraints often require sensors to enter low-power states, which causes delays in communication. Therefore, effective energy management and selection of optimal multi-hop routing paths are essential to minimize delays and prevent network congestion. This research proposes Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO) to optimize energy efficiency and reduce delay via effective clustering and routing in WSN. Logistic mapping and symmetric adaptive division population are the di strategies, which are used for population initialization and balancing exploration, which enhance diversity and effectively search the solution space. Distance between Cluster Head (CH) and BS, intra-cluster distance, node degree, average delay during transmission, and residual energy are the multi-objectives, which are utilized as fitness functions for CH and route path selection. MO-DSGGO achieves less delay of 0.176 ms and reduced energy consumption of 7.2 J for scenario 2 with 100 nodes and network size of [Formula: see text] mts and obtains throughput of 95% in scenario 7 with 250 nodes and [Formula: see text] in MATLAB R2020b. These improvements represent clear superiority over existing methods like Energy Optimization Routing by applying an improved Artificial Bee Colony (EOR-iABC) and Energy Optimization Approach Medium access control Routing Cross-Layer (EOAMRCL). Also, the proposed method obtains better convergence analysis that represents rapid and more stable performance. MO-DSGGO achieves longer node lifetime of 97.272% with Packet Delivery Ratio (PDR) of 95.38%, and throughput of 14,297 BPS compared to Energy Efficient Lifetime-aware Cluster-based Routing (EELCR) that demonstrates more reliable and efficient network utilization.
E. Madhankumar, K. Selvaraj, Dae-Ki Kang· Scientific Reports· 0 citations
With the evolution from Fifth Generation towards Beyond 5G and 6G (Sixth Generation), achieving energy efficiency has become an important objective in the design of wireless communication systems. The rise in the network density and complexity call for low-energy approaches to facilitate sustainability and longer lifetimes for networks, particularly for Wireless Sensor Networks (WSN). Present work is initiated for energy optimization based on Ant Colony Optimization, that is motivated by the natural behavior of ants. To cope with dynamic and resource-aware settings, Machine Learning (ML) mechanisms are embedded for smart decision-making and optimization. Tree based algorithms like Decision Trees, and Machine learning algorithms like K-Nearest Neighbor have been successfully implemented for wireless applications. ACO was selected for its low computational complexity and distributed and adaptive routing mechanism and has been tested previously in various WSN experiments for increased data delivery and energy efficiency. The findings presented are based on these experiences aligning with the changing demands of 5G and B5G.
Sahiti Vankayalapati, P. Karthik, Gumma Venkata Naga Jahnavi Yadav et al.· International Conference on...· 0 citations
The longevity of the network depends on making the correct route decisions. The term "optimal routing" in wireless sensor networks refers to selecting the best path among the available routes in order to reduce energy consumption and increase network lifetime. Numerous optimization algorithms considering parameters like energy remaining in a node, node distance, network topology, and link quality when choosing the best routes. In order to enable data aggregation and energy-efficient routing through the CH, optimal routing techniques are typically implemented in cluster-based WSNs. In Heterogeneous WSNs (HetWSNs), reliability studies pertain to the network's capacity to deliver information to the base station. Extreme weather conditions, sensor node battery depletion, hardware and software malfunctions, and other factors all have an impact on a heterogeneous WSN's reliability. This article examines the application of optimal routing to both chain-based HetWSNs and also focuses on reliability studies in HetWSNs with chain-based connections that use optimal routing. For five levels of energy-based HetWSNs, an algorithm known as ORB-PEGASIS (Optimal Routing Based Power Efficient Gathering in Sensor Information System) is created. Our suggested ORB-PEGASIS algorithm increases the network lifetime of the HetWSNs by, according to a comparison of the obtained results with various algorithms, including PEGASIS-E by 68.42%, PEGASIS by 112.8%, PEG-ACO by 60.43%, and IEEPB-KMO by 58.47%.
Prashanth G. S., Suprith P. G., Yashavanthakumar T. R.· International Journal of Wir...· 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