Unequal clustering in wireless sensor network: a review

Al-Ani, Khalid W.; Abdullah, Fairuz; Yossuf, Salman

doi:10.11591/ijeecs.v22.i1.pp419-426

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The evaluation of the method [20] can be done on the basis of the found clusters and the quality of knowledge [21]- [23] extracted for good decision-making. However the choice of the density of the attractors and the noise threshold [24] are determining factors for a good measurement of evaluation.…”

Section: Discussion and Evaluationmentioning

confidence: 99%

Density-based classification with the DENCLUE algorithm

Hassani

Falih

Bouikhalene

2021

IJEECS

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<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

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Section: Discussion and Evaluationmentioning

confidence: 99%

Density-based classification with the DENCLUE algorithm

Hassani

Falih

Bouikhalene

2021

IJEECS

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“…The better solution for the limited energy in WSN is to equip the sensor with high-capacity batteries. 4 But, the battery performance has reached its choke point and is difficult to improve.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, sensors are powered by energy‐constraint batteries, and it is impractical for replacing the battery due to the expensive cost and enormous quantity of sensors. The better solution for the limited energy in WSN is to equip the sensor with high‐capacity batteries 4 . But, the battery performance has reached its choke point and is difficult to improve.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic with honey badger algorithm for cluster‐based multi‐hop routing protocol for wireless sensor networks

Soundararajan,

Kurangi,

Basha

et al. 2024

Int J Communication

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SummaryIn the last decades, wireless sensor networks (WSNs) have appeared as an active research area owing to their high applicability in different application areas like healthcare, automation, and the military. Several issues have been encountered in the WSNs, including energy consumption, deployment of sensor nodes, routing algorithms, energy efficiency, cluster head (CH) selection, and robustness. Cluster‐based routing is one of the energy‐efficient solutions in the WSN. At the same time, the hot spot problem remains a challenging problem, which needs to be addressed in the design of energy‐efficient solutions for WSNs. Motivated by the abovementioned discussion, c The comprehensive comparison study pointed out the betterment of the FLHBA‐CMHR technique in terms of different measures. The FLHBA‐CMHR technique obtains a maximum half‐node die (HND) of 1629 nodes, while the LEACH, FUCA, URBD, DEFL, and E‐FUCA techniques attain decreased HND of 793, 1085, 1092, 1139, and 1344 nodes correspondingly.

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“…The nodes, on contrary, are commonly put in inaccessible and hostile atmospheres, making it impossible or difficult to replenish energy or substitute batteries. The WSN's energy efficiency must be prioritized for extending its life as much as possible [4].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Hotspot Problem Using Chaotic Salp Swarm Algorithm for Energy Efficient IoT Assisted Wireless Sensor Networks

A. Gayathri

2023

IJRITCC

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Wireless Sensor Networks (WSN) and Internet of Things (IoT) continued to be pro-active study due to their far reaching applications and also a crucial technology for ubiquitous living. In WSN, energy awareness becomes a significant design problem. Clustering can be defined as a renowned energy-efficient method and renders a lot of benefits like energy competence, less delay, scalability, and lifetime; but it resulted in hot spot problems. To sort out this problem a method called unequal clustering is designed. In unequal clustering, the cluster size differs with the Base Station (BS) distance. In this study, a new Chaotic Salp Swarm Algorithm Based Unequal Clustering Approach (CSSA-UCA) methodology to resolve hot spot issues in IoT-assisted WSN is proposed. The major objective of the CSSA-UCA methodology lies in the effectual identification of CHs and unequal cluster sizes. To accomplish this, the CSSA-UCA technique initially derives the CSSA by the incorporation of chaotic notions into the conventional SSA. At the same time, a fitness function incorporating multiple input parameters was considered for unequal cluster construction. A wide range of experimental result analyses is performed to exhibit the supremacy of the CSSA-UCA technique. The experimental results stated that the CSSA-UCA technique proficiently balances energy accretion and improves the network lifetime.

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Unequal clustering in wireless sensor network: a review

Cited by 5 publications

References 17 publications

Density-based classification with the DENCLUE algorithm

Density-based classification with the DENCLUE algorithm

Fuzzy logic with honey badger algorithm for cluster‐based multi‐hop routing protocol for wireless sensor networks

Mitigating Hotspot Problem Using Chaotic Salp Swarm Algorithm for Energy Efficient IoT Assisted Wireless Sensor Networks

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