A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Muthukkumar, R.; Garg, Lalit; Maharajan, K.; Jayalakshmi, M.; Jhanjhi, N. Z.; Parthiban, S.; Saritha, G.

doi:10.7717/peerj-cs.1029

Cited by 14 publications

(8 citation statements)

References 94 publications

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“…The different types of sensor nodes will have different data priorities. In Muthukkumar et al, 21 the authors propose a genetic algorithm‐based energy‐aware multi‐hop clustering (GA‐EMC) scheme for HWSNs. In HWSNs, all the nodes have varying initial energies and typically have an energy consumption restriction.…”

Section: Related Workmentioning

confidence: 99%

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

Abdelhay

Morsy

Kishk

2023

Int J Communication

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Summary Information transmission is extremely challenging in underwater acoustic sensor networks (UASNs) because, in the acoustic channel, the packet loss is high compared to other channels due to low bandwidth and a long propagation delay. So, designing an efficient energy algorithm is a big challenge in the acoustic channel. In this paper, first, a cluster head (CH) election fitness function based on a hybrid particle swarm optimization and gravitational search algorithm (PSO‐GSA) is proposed for a three‐dimensional (3D) underwater sensor network (UASN). The proposed algorithm includes CH election, CH load, and neighbor CH distance for multi‐hop transmission (MH‐PSOGSA). Then, based on the proposed algorithm, a relay‐based traffic‐aware energy‐efficient routing protocol (R‐TAEERP) with a weight function is proposed to offload the heavy consumption of the CHs. MATLAB simulations were accomplished to compare the performance of MH‐PSOGSA and R‐TAEERP with the existing representative protocols. The heterogeneity in energy and traffic has been considered. The simulation results show better performance of the proposed algorithm than other existing representative protocols in terms of energy consumption, the performance of the stable region, and the lifetime of the network.

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Section: Related Workmentioning

confidence: 99%

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

Abdelhay

Morsy

Kishk

2023

Int J Communication

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“… Genetic Algorithm (GA): An optimization algorithm that emulates the mechanism of natural selection and can be used for parameter optimization in IoT systems [15].…”

Section:  Leach Algorithm: the Low Energy Adaptivementioning

confidence: 99%

Study of Clustering Technique Algorithms in IoT Networks

Soliman Soliman Deabes,

Mikheal,

Ibraheem Eid

et al. 2023

Engineering Research Journal (Shoubra)

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The Internet of Things (IoT) refers to a network of interconnected devices that operate on the internet facilitating seamless and efficient data exchange to improve human life. Energy consumption in the IoT network nodes is a major challenge. To overcome this challenge, clustering became a powerful data gathering in IoT applications that saves energy by organizing IoT nodes into clusters. The Cluster Head (CH) oversees all Cluster Member (CM) nodes in each group allowing for the creation of both intracluster and inter-cluster connections. There are many algorithms to improve the lifespan of the network, increase the number of active nodes, and extend the remaining energy time in IoT. These algorithms employ techniques such as clustering and optimization to enhance both the energy efficiency and overall performance of the network. In this paper, Low Energy Adaptive Clustering Hierarchy (LEACH), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Energy-Efficient Routing using Reinforcement Learning (EER-RL), and Modified Low Energy Adaptive Clustering Hierarchy (MODLEACH) algorithms will be studied and MATLAB code will be implemented, tested, and the results will be validated.

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“…Therefore, CH selection is a critical issue in clustering protocols, and is considered to be an NP-hard problem [ 5 ]. In the state-of-the-art clustering protocols, computational intelligence algorithms based on the genetic algorithm [ 6 ], gray wolf optimization [ 7 ], particle swarm optimization [ 8 ], bacteria foraging optimization [ 9 ], and fuzzy logic [ 10 ] are increasingly being used to find the optimal solution. In particular, compared with non-fuzzy clustering approaches [ 11 , 12 ], fuzzy-logic-based schemes can better handle the uncertainties inherent in clustering [ 13 ], and provide more flexibility and a better combination of input parameters, so as to achieve the optimal solution [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Wang

2023

Sensors

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Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors’ residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH’s number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

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A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Cited by 14 publications

References 94 publications

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

Study of Clustering Technique Algorithms in IoT Networks

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

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