Jul 2026· International Journal of Science, Strategic Management and Technology· 0 citations
TL;DR
An Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity is proposed.
Abstract
The rapid growth of Internet of Things (IoT) applications has led to the deployment of large-scale sensor networks in smart cities, healthcare, agriculture, industrial automation, and environmental monitoring systems. However, the limited battery capacity of sensor nodes remains a major challenge affecting network lifetime and reliability. Frequent battery replacement increases maintenance costs and limits scalability, particularly in remote and inaccessible locations. This paper proposes an Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity. The proposed system continuously monitors residual node energy, communication quality, and environmental conditions to dynamically select optimal routing paths and operational states. Machine learning algorithms predict energy consumption patterns and network traffic conditions, enabling proactive resource management. Experimental analysis demonstrates significant improvements in network lifetime, packet delivery ratio, energy utilization efficiency, and communication reliability compared to conventional routing approaches. The proposed framework offers a sustainable and scalable solution for next-generation IoT sensor networks operating in energy-constrained environments.
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
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, Kalasani Siddhartha, Dasarinki Ramprasad· International Journal of Cre...· 0 citations
: Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications in environmental monitoring, smart agriculture, healthcare, industrial automation, and Internet of Things (IoT) systems. The current WSNs have limitations such as limited battery capacity, low network lifespan, spectrum scarcity, scalability problems, and poor communication quality. To address the constraint of limited energy, several technologies have been proposed for energy harvesting (EH) to ensure an operation of self-sustainable networks, which makes possible to tap into various sources of environmental energy, including sun, vibration, or radio frequency signals. Cognitive Radio (CR) technology has similarly proved to be a beneficial approach to spectrum utilization through dynamic spectrum access and intelligent channel selection. EH and CR technologies are combined to form Energy Harvesting Cognitive Wireless Sensor Networks (EH-CWSNs), which enable better energy efficiency, better spectrum utilization and longer network lifetime. However, the routing complexity, clustering efficiency, spectrum sensing, scalability, communication reliability and resource management are some of the problems that still remain with EH-CWSNs. 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. In addition, some current challenges and future research directions are outlined for the development of efficient and intelligent wireless sensor networks.
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
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Elkhatim Abuelysar Elmobarak Mohammed Ali· Islamic University Journal o...· 0 citations
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