Hybrid Routing Topologies in Wireless Sensor Networks for Iot: A Survey of Cluster-Tree-Chain Architectures
TL;DR
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.
Abstract
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.