ArticlePeerJ. Computer science2022
A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks.
Article in PeerJ. Computer science, 2022. 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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6 citing papers in PubMed, 26 citations in OpenAlex.
- Analysis and comparison of two selected machine learning algorithms for enhancing wireless sensor network protocols.Scientific reports · 2026Article
- Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm.Sensors (Basel, Switzerland) · 2025Article
- A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- An Artificial Plant Community Algorithm for the Accurate Range-Free Positioning of Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- Leveraging Metaheuristic Unequal Clustering for Hotspot Elimination in Energy-Aware Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- Dynamic coefficient symmetric polynomial-based secure key management scheme for Internet of Things (IoT) networks.PeerJ. Computer science · 2023Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge. Methods: This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node. Results: Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.
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