Jul 2026· International Journal of Science and Research (IJSR)· 0 citations· 30 references
TL;DR
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.
Abstract
: 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.
Wireless Sensor Networks (WSNs) are extensively used in environmental monitoring, healthcare, industrial automation, military surveillance, and smart infrastructure. However, the limited battery life of sensor nodes restricts their long-term operation, particularly in remote areas where battery replacement is difficult and expensive. Energy harvesting offers a sustainable solution by converting ambient energy sources such as solar, thermal, vibration, wind, and radio frequency (RF) energy into electrical power for sensor nodes. This study reviews various energy harvesting techniques, their operating principles, advantages, limitations, and suitability for WSN applications. It also highlights the importance of integrating energy harvesters with energy storage devices and power management systems to ensure reliable operation. A framework involving energy source assessment, harvesting module selection, power conditioning, storage management, and adaptive duty-cycle control is presented to optimize energy utilization. Results indicate that solar energy provides the highest power output under favorable conditions, while vibration and RF harvesting are effective in indoor and industrial environments. Hybrid energy harvesting systems offer greater reliability by combining multiple energy sources. Overall, energy harvesting enhances network lifetime, reduces maintenance costs, and enables sustainable, self-powered WSNs, making it a key technology for future Internet of Things (IoT) and smart environment applications.
Rahul D. Mehta, Priya Kapoor· International Journal of Mod...· 0 citations
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.
Dabbeta Ganapathi Dabbeta Ganapathi, Halavath Vijaya Halavath Vijaya, P. K. P Kavitha· 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
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
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
