Multi-hop routing in IoT/WSN: Critical review and proposal of a conceptual framework for hybrid, multi-objective, and explainable protocols

Hybrid Routing Topologies in Wireless Sensor Networks for Iot: A Survey of Cluster-Tree-Chain Architectures

The rapid expansion of the Internet of Things (IoT) relies heavily on the deployment of Wireless Sensor Networks (WSNs), which are fundamentally constrained by limited battery capacity, restricted bandwidth, and high energy consumption during long-distance data transmission. Traditional flat and early hierarchical clustering protocols often struggle with uneven energy distribution, high propagation latency, and severe vulnerability to single points of failure. Consequently, there has been a significant paradigm shift toward advanced mathematical optimization and complex hybrid network topologies. This paper presents a comprehensive survey of state-of-the-art energy-efficient routing architectures in WSNs, systematically analyzing contemporary peer-reviewed methodologies. We critically examine the transition from probabilistic cluster head selection to highly deterministic meta-heuristic and swarm intelligence algorithms, highlighting the performance of Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), and Cat Swarm Optimization (CSO) in balancing network loads. Furthermore, the review evaluates advanced topological frameworks, comparing the limitations of standard chain routing against robust hybrid structures—such as Grid-Chains, Polar Coordinate mapping, and Minimum Spanning Tree (MST) partitioning—that are explicitly designed to minimize delay and ensure fault tolerance. To address the massive data requirements of modern IoT applications, we explore the integration of Compressive Sensing (CS) for simultaneous data sampling and compression, alongside hybrid tree-cluster topologies engineered for high-fidelity raw data collection. Finally, this survey discusses critical open challenges and identifies emerging paradigms, particularly predictive routing via Machine Learning and self-sustaining Energy-Harvesting WSNs (EH-WSNs), mapping the future trajectory toward perpetual, autonomous IoT sensory infrastructures.

A dynamic reward framework for scalable and efficient IoT-WSN routing using deep reinforcement learning.

A dynamic reward structuring framework within deep reinforcement learning to enable adaptive and balanced routing in IoT-WSNs and achieves significant performance gains, including approximately 30% improvement in energy efficiency, 25% reduction in latency, and 35% increase in network throughput compared with baseline methods.

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

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

Wireless Sensor Networks (WSNs) are fundamental to the expansion of the Internet of Things (IoT), yet severe node energy constraints remain the primary bottleneck for remote environmental monitoring where power infrastructure is unavailable. Because these devices rely on finite battery capacities, optimizing energy usage is critical for maximizing network longevity and architectural sustainability. Sourcing literature across the Scopus, IEEE Xplore, and Elsevier digital databases, this study executes a systematic review evaluating a final cohort of n=86 contemporary energy management frameworks published between 2020 and 2026. The analysis synthesizes advanced multi-tier optimization techniques, specifically focusing on hierarchical clustering methodologies, metaheuristic routing protocols, and advanced scheduling algorithms. Beyond traditional approaches, the technical findings investigate the cross-layer impacts of duty cycle scheduling, transmission power control, and sleep protocols on maintaining rigid network coverage and connectivity. Ultimately, this review identifies significant research gaps regarding topological fault tolerance and localized load imbalances near base stations. The findings highlight how the strategic integration of cohesive, cross-layer hybrid optimization strategies can mitigate active energy dissipation, providing actionable technical recommendations for future IoT-based WSN architectures.

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.

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.