Routing Protocol LEACH-K Using K-Means Algorithm in Wireless Sensor Network

Gantassi, Rahma; Gouissem, Bechir Ben; Othmen, Jalel Ben

doi:10.1007/978-3-030-44038-1_27

Cited by 22 publications

(35 citation statements)

References 2 publications

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“…The termination criteria for the simulation is reached when there is a cluster where there is no node that qualifies to be elected as a cluster head. It can be seen from the figure that under the same residual energy threshold conditions, DEKCS outlasts LEACH and LEACH-K [5] (based on k-means and LEACH) protocols before the network becomes disconnected. Under the same network conditions, the termination criteria is reached after only 80 rounds for LEACH, compared to over 800 rounds for the DEKCS algorithm.…”

Section: Performance Evaluationmentioning

confidence: 98%

“…1) In-cluster position: The node that is nearest to most other nodes within the cluster is selected as cluster head, instead of the node nearest to the centre of the cluster [7,11,5]. This condition, which we term the proximity rule, is more useful than proximity of the potential cluster head to the centre of the cluster since our goal is to mininise energy consumed by sensors for transmitting to the cluster head, not to select the node at the centre of cluster.…”

Section: B Cluster Head Selectionmentioning

confidence: 99%

“…Increasingly, machine learning is employed to partition the network into clusters from which cluster heads are selected according predefined criteria. This can be done using methods such as k-means [5,6,7] and fuzzy c-means [7], etc., which find increasing use in wireless sensor networks, internet of things and crowd sensing applications. For such clustered networks, AUVs can be used to collect data from the network to avoid the high-power transmissions required to reach remote base stations [6,8].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose a new protocol called distanceand energy-constrained k-means clustering scheme (DEKCS) for clustering, cluster head selection and data retrieval to prolong the lifetime of wireless underwater sensor networks. DEKCS uses the k-means algorithm for clustering but unlike previous works based on k-means [5,9] where the node nearest to the k-means centroid is selected as cluster head, DEKCS selects the node that is closer to most nodes in the network as cluster head. This rule for selecting nodes closer to most other nodes as cluster heads has been termed the proximity rule because it ensures that nodes in each cluster are always in close proximity to their cluster head, thereby allowing them to transmit with much lower powers.…”

Section: Introductionmentioning

confidence: 99%

“…In Section III, we introduce the DEKCS protocol, which uses the dynamic elbow method to determine the optimal number of clusters required and selects cluster heads based on the position of the nodes in a cluster and their residual energy levels. We evaluate the performance of the proposed algorithm in Section IV, in terms of overall network energy consumption and compare the results with two benchmark algorithms based on LEACH and LEACH k-means clustering [5]. Finally, conclusions are drawn in Section V.…”

Section: Introductionmentioning

confidence: 99%

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DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

et al. 2021

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Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance-and energy-constrained k-means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on k-means clustering by 42%.

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Section: Performance Evaluationmentioning

confidence: 98%

Section: B Cluster Head Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

et al. 2021

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Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

Gouissem

Gantassi

Hasnaoui

2022

Int J Communication

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Hierarchical based routing is a kind of group-based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large-scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self-organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid-based k-means clustering protocol (named GBK), which combines grid-based routing with k-means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k-means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBK algorithm named GBK-R is also proposed to extend network stability of the GBK algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBK and GBK-R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBK clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.clustering algorithm, energy efficient algorithm, grid-based algorithm, k-means algorithm, large scale WSN, machine learning | INTRODUCTIONWireless sensor network (WSN) is a swiftly growing technological platform that takes, in the few last years, big attention for his application in various domains like military, agriculture, structural monitoring, health monitoring, and home networks. A WSN is composed of a number of small-size, low-power sensor nodes capable of detecting physical phenomena, which are constrained in energy supply, processing power, communication range, and bandwidth. 1

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Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Rajagopal

Bhanumathi

Sakthivel

2023

Int J Communication

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This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

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Routing Protocol LEACH-K Using K-Means Algorithm in Wireless Sensor Network

Cited by 22 publications

References 2 publications

DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

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