Advances and Trends in Metaheuristic-Optimized Cluster-Based Routing for Energy-Efficient Wireless Sensor Networks: A Comprehensive Review

Abstract

Wireless Sensor Networks (WSNs) have become indispensable across application domains such as environmental monitoring, surveillance, industrial automation, and smart infrastructure. A critical ongoing challenge remains energy management, owing to the limited battery life of sensor nodes, resource constraints, and often inaccessible deployment locations. Cluster-based routing protocols — particularly those optimized via metaheuristic algorithms — have emerged as a highly promising paradigm to extend network lifetime, balance loads, and improve communication efficiency. This review provides a comprehensive synthesis of the developments in cluster-based routing for WSNs, with emphasis on classical clustering schemes, metaheuristic and nature-inspired optimisation algorithms (for example, PSO, GA, SHO), and the more recent hybrid protocols. We present a comparative analysis of protocol performance, scalability and trade-offs. We then chart emerging trends and identify open research challenges for adaptive, energy-efficient WSN solutions.

Introduction

Wireless Sensor Networks (WSNs) comprise numerous small, battery-powered sensor nodes that cooperatively sense, process, and wirelessly transmit environmental data (e.g., temperature, pressure, motion) to a central base station. Since these nodes are energy-constrained and often deployed in inaccessible areas, energy efficiency is the central design challenge.

Among various energy-saving strategies, clustering is widely used. In clustering, nodes are grouped into clusters, and each group elects a Cluster Head (CH) that aggregates and forwards data to the base station. This reduces redundant transmissions, improves scalability, and extends network lifetime. However, performance depends heavily on effective CH selection, cluster formation, re-clustering strategies, and routing paths.

Goals of Clustering

• Minimize total energy consumption through local data aggregation.

• Balance energy usage among nodes to prevent premature node death.

• Enhance scalability and simplify network management.

• Maintain coverage and adapt to node failures or topology changes.

Types of Clustering

• Homogeneous vs. Heterogeneous: Equal or varied node capabilities.

• Static vs. Dynamic: Fixed or periodically updated clusters.

• Single-hop vs. Multi-hop: Direct or relay-based CH-to-sink communication.

• Equal vs. Unequal: Uneven cluster sizes to solve “hot-spot” issues.

• Flat vs. Hierarchical: Multi-level clustering for large-scale systems.

Key Challenges

Efficient CH selection, adaptability to failures, managing communication overhead, scalability, heterogeneity, and multi-objective optimization (energy, latency, reliability, security) remain major challenges. Real-world conditions—like interference, node mobility, and energy holes—further complicate implementation.

Classical Clustering Protocols

1. LEACH (Low-Energy Adaptive Clustering Hierarchy) – Simple, probabilistic CH rotation; good for homogeneous networks but poor scalability.

2. HEED (Hybrid Energy-Efficient Distributed Clustering) – Considers residual energy and node density; more balanced but with higher control overhead.

3. TEEN/APTEEN – Threshold-based for event-driven applications; energy-efficient but not ideal for delay-tolerant systems.

Metaheuristic and Nature-Inspired Clustering

To overcome the limits of classical methods, metaheuristic algorithms inspired by natural or social systems are increasingly applied for near-optimal CH selection and routing.

1. Particle Swarm Optimization (PSO): Models swarm intelligence; effective and flexible but parameter-sensitive and may converge prematurely.

2. Genetic Algorithms (GA): Uses evolutionary operations (selection, crossover, mutation) for multi-objective optimization; offers robust solutions but has high computational cost.

3. Spotted Hyena Optimization (SHO-CH): Inspired by cooperative hunting; yields superior throughput and stability in heterogeneous networks but requires tuning and computational resources.

Conclusion

Metaheuristic-based clustering and routing protocols have significantly advanced the energy-management and network robustness of WSNs. Classical protocols such as LEACH and HEED laid the foundation for cluster-based routing, but are limited in heterogeneous, large-scale and dynamic settings. Metaheuristic and nature-inspired algorithms (PSO, GA, SHO, hybrid variants) significantly enhance network lifetime, throughput and scalability, especially when optimised for residual energy, cluster distribution and communication cost. Among these, SHO-CH and similar advanced algorithms show exemplary performance in simulations, making them promising for large-scale, heterogeneous deployments. Nevertheless, the next era of WSNs — characterised by ultra-dense node counts, mobility, energy-harvesting, IoT integration and multi-objective demands (latency, security, reliability) — demands unified AI-driven, hybrid and secure clustering paradigms. Real-world testbed validation, lightweight distributed intelligence, cross-layer design and QoS-aware optimisation will be pivotal to unlocking the full potential of WSNs in diverse applications.

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Copyright © 2025 Prakash C., Ananth K. R.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.