Energy Optimization Strategies in IoT-Based Wireless Sensor Networks: A Systematic Review

Energy-Aware Cluster-Optimized Intelligent Routing Protocol for WSN-based IoT Networks

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.

Greenhouse Monitoring and Control Systems: A Review of Internet of Things and Wireless Sensor Network Technologies

The convergence of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has strengthened smart greenhouse management by enabling continuous environmental monitoring, automated control, and more informed use of agricultural resources. This review examines and synthesises research on IoT- and WSN-enabled greenhouse systems published between 2015 and 2026. Peer-reviewed studies retrieved mainly from IEEE Xplore and Google Scholar were analysed across sensing technologies, system architecture, communication methods, network topology, control approaches, cloud–edge computing, energy management, and performance assessment. The reviewed studies show that achieving strong performance in one area often requires compromises elsewhere, particularly among communication range, energy demand, reliability, scalability, latency, computational capacity, and cost. ZigBee is particularly suitable for low-power, short-range mesh networks, whereas LoRa better supports wider-area deployments. Clustered and hybrid network structures offer favourable scalability and reliability, while cloud–edge architectures combine scalable analytics with rapid local processing. Intelligent control improves prediction, adaptability, and resource use, whereas renewable energy and intelligent energy management can reduce reliance on conventional power sources. Practical deployment, however, remains constrained by sensor reliability, energy limitations, communication instability, interoperability, cybersecurity risks, and limited long-term validation. Future studies should place greater emphasis on energy-autonomous systems, edge intelligence, secure and interoperable architectures, digital twins, adaptive autonomous control, and realistic evaluation of emerging communication technologies for greenhouse applications. Overall, future smart greenhouses are likely to depend increasingly on integrated architectures that combine energy efficiency, adaptive control, reliable connectivity, and security to support sustainable operation.

An Enhanced LEACH-Based Dynamic Routing Framework for Energy-Efficient Heterogeneous Wireless Sensor Networks

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.

ARFOR: adaptive random forest and owl optimization for energy-efficient routing in WSN-IoT

Towards Sustainable IoT: An AI-Driven Framework for Enhanced Energy Harvesting in Wireless Sensor Networks

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.

A comprehensive survey on connectivity for IoT sensor networks

Recent demand for Internet of things (IoT) based sensing devices has increased in many significant areas, such as greenhouse monitoring, remote patient monitoring, military applications, wild-life tracking and habitat monitoring, precision agriculture, etc. Moreover, some critical issues are present in the perpetual sensor network like node energy consumption rate, coverage/connectivity, quality of service, network throughput and charging cycle capacity of sensor node batteries. Thereby, various network issues can arise, especially regarding coverage holes, proper connectivity, and network life-time. In this paper, we have studied these problems well and have shown the necessary constraints for the connectivity of the IoT sensor network. Moreover, we have also presented various helpful IoT applications and energy harvested IoT sensor networks. Any network can perform well for a long time because of energy management which is very crucial. This paper presents various feature scopes used in wireless sensor networks (WSNs). It also illustrates distinct approaches like relay node placement, efficient communication, machine-learning systems i.e. reinforcement learning, and heuristic techniques. It also discusses real-world deployments to highlight practical connectivity challenges and failures.