Improved Soft-<i>k</i>-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Zhu, Botao; Bedeer, Ebrahim; Nguyen, Ha H.; Barton, Robert; Henry, Jérôme

doi:10.1109/jiot.2020.3031272

Cited by 51 publications

(25 citation statements)

References 43 publications

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“…Zhu et al had proposed a clustering algorithm based on a fast search and find of density peaks (CFSFDPs) [15] and Kernel Density Estimation (KDE) for the improvement of the selection of clustering process by employing a soft K-means clustering algorithm. The proposed algorithm improves the lifetime of the nodes when compared to the existing methodologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of the wireless sensor networks acts driven factor for network efficiency which can be effectively maintained by performing the clustering process effectively. More solutions and clustering algorithms have been offered by various researchers, but the concern of reduced efficiency in the routing process and network management still exists. This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks. The memetic algorithm employs a local searching methodology to mitigate the premature convergence, while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain (i.e.,) best cluster head in the wireless sensor networks. The proposed hybrid algorithm is compared with the state of art clustering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time, energy consumption, throughput, etc.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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“…In addition, the concept of multiline heads is used to balance energy consumption in clusters. Extensive simulation results under different network scenarios show that for small-scale WSNs with single-hop transmission, the proposed algorithm can delay the first node death, half node death and the last node death by an average compared to algorithms Different clustering to achieve higher performance [9].…”

Section: Releted Workmentioning

confidence: 99%

Provide A Method To Optimize the Reduction of Energy Consumption in Wireless Sensor Networks Using the Cuckoo Algorithm

Fard¹

2021

CRPASE

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Given the high importance of wireless sensor networks in various applications, it is necessary to examine the important challenges of these networks. The challenges of these networks are the short life of the nodes, which leads to shortening the life of the network and ultimately disrupting the network. One of the ways to increase efficiency and reduce energy consumption in the network is to use clustering methods. In this research, using clustering methods and selection of efficient clusters by DEEC algorithm and optimization of this algorithm using cuckoo algorithm. The results of the study showed an improvement in reducing energy consumption in this type of network, so that the energy remaining in the network after the simulation in routing with the new protocol is about 13% more than TTDD routing about 11% more than routing with APTEEN protocol. If we consider the life of the network when the first node in the network is turned off, in the new protocol, the first node is turned off 7 seconds later than the TTDD protocol and 13 seconds later than the APTEEN protocol. This means that, on average, the lifespan of the network has been increased by about 10% with the new protocol.

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“…These algorithms are also utilized in various classification and clustering-based problems which is mentioned in [7]. From paper [8]- [13], it is discussed that how to remove overlapping by using soft-K-means clustering. According to them, routing protocol clustering is considered the most desirable protocol in terms of indoor network coverage.…”

Section: Related Workmentioning

confidence: 99%

An integrated machine learning model for indoor network optimization to maximize coverage

Reza

Rifat²,

Ahmed³

2021

IJEECS

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Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.

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Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Cited by 51 publications

References 43 publications

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Provide A Method To Optimize the Reduction of Energy Consumption in Wireless Sensor Networks Using the Cuckoo Algorithm

An integrated machine learning model for indoor network optimization to maximize coverage

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