ArticleScientific reports2025
Energy efficient multi hop clustering using Artificial Bee Colony metaheuristic in WSN.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- SMA-WSN: Failure-Aware Slime-Mold-Inspired Clustering and Per-Hop Versus End-to-End Delivery Analysis in Agricultural Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- Analysis and comparison of two selected machine learning algorithms for enhancing wireless sensor network protocols.Scientific reports · 2026Article
- Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility.Scientific reports · 2026Article
- Multi-Factor Cost Function-Based Interference-Aware Clustering with Voronoi Cell Partitioning for Dense WSNs.Sensors (Basel, Switzerland) · 2026Article
- Adaptive and scalable energy aware clustering for mobile wireless sensor networks using a density driven approach.Scientific reports · 2025Article
- Improving the functionality of wireless sensor networks through the use of reinforcement learning and metaheuristic based energy efficient system.Scientific reports · 2025Article
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Authors and funding
3 authors.
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Abstract
Wireless sensor networks (WSNs) have garnered considerable interest for their ability to gather and transmit data in applications including environmental monitoring, industrial automation, and military surveillance. The constrained energy supply of sensors, frequently dependent on non-rechargeable batteries, presents a significant issue in the design and efficacy of these networks. This paper introduces EEM-LEACH-ABC, a novel energy-efficient clustering and routing protocol for WSNs using the Artificial Bee Colony (ABC) algorithm. The protocol integrates three main mechanisms of region-based energy-aware clustering using network partitioning, optimized multi-hop communication paths, and a hierarchical tree structure for efficient data aggregation. ABC dynamically selects Cluster Heads (CHs) and routing paths based on key parameters including residual energy, transmission distance, Cluster Head Ratio (CR), and multi-objective weighting coefficients. Simulation results under different scenarios - including centralized, edge, and corner base station placements - show that EEM-LEACH-ABC outperforms existing protocols such as MHCRP, SBOA, and HChOA in terms of First Node Death (FND), Half Node Death (HND), Packet Delivery Ratio (PDR), and energy consumption. Specifically, the protocol achieves up to 216% improvement in FND and 29% increase in packet delivery at the base station. Furthermore, the protocol adapts to interference, node failures, and mobile sensor nodes, thereby ensuring robustness and scalability in real-world deployments. Parameters are automatically optimized using ABC to minimize energy imbalance and increase network lifetime.
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