A Novel Energy-Aware Clustering (NEAC) protocol is proposed to improve the energy efficiency of WSNs over the Internet of Things (IoT) and the performance of the proposed method is demonstrated in improving network stability and energy balance efficiency, and increasing network lifetime.
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
This survey provides an exhaustive analysis of the existing routing protocols, clustering, optimization and energy management techniques in WSNs and their benefits and drawbacks, with emphasis on EH-WSNs, Cognitive WSNs, and EH-CWSNs.
A. V Anuja, B. Meena Preethi· International Journal of Sci...· 0 citations
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
Dr. Deepa, M. Mehfooza, Padmavathy Thiruppathi Raj· Microsystem Technologies· 0 citations
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.
I. Kamil, Goodness Adeleke Adetokun· International Journal of Lat...· 0 citations
The integration of Wireless Sensor Networks (WSNs) into Internet of Things (IoT) brings new challenges in terms of range and energy consumption. Specifically, energy consumption is of great importance for future IoT sensors. Therefore, a thorough study must be performed to understand and evaluate the impact of different parameters that affect sensor node energy consumption. However, LoRa backscatter has been seen as a promising technology for long-range low-power IoT applications. Hence, this paper addresses the modeling of the energy consumption of a LoRa backscatter-based sensor node. We first derive the energy model for single-node communication and extend it to multiple-sensor-node communication where several sensors concurrently transmit their information. Furthermore, we derive an energy model for a battery-free sensor node that relies on the energy harvested from ambient sources to drive its operation. The simulation results show that both the LoRa parameters (such as payload size), the number of nodes in the network, and the number of retransmissions can impact the total energy consumption of the sensor node. Additionally, a single node powered by a battery with a capacity of 225 mAh and a transmission of 20 bytes every 5 minutes has a lifetime of about 14 years.
Unknown authors· Computer Networks and Commun...· 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
