An Energy-Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks using Machine Learning

Debasis, Kumar; Sharma, Lakhan Dev; Bohat, Vijay Kumar; Bhadoria, Robin Singh

doi:10.1007/s11036-023-02109-7

Cited by 16 publications

(8 citation statements)

References 43 publications

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“…In the cluster head selection process within wireless sensor networks, the utilization of an artificial neural network (ANN) is incorporated in [29]. The designated geographical area undergoes subdivision into smaller regions, with each region having a cluster head (CH) selected through the application of the ANN.…”

Section: Related Workmentioning

confidence: 99%

ANN Based Clusterhead Selection Process in WSN

GANGAL,

SESLI,

HACIOGLU

2024

Preprint

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Wireless sensor networks often face a significant challenge due to their limited energy capacity. To address this challenge, numerous studies are currently underway to explore innovative ways to optimize energy usage. This study proposes an energy-efficient cluster head selection method that is supported by artificial neural networks for cluster-based sensor networks, thereby enhancing their energy efficiency. The neural network is placed in each sensor node. The nodes decide to be the cluster head according to the threshold they set using the ANN, without the need for centralized control. According to the simulation studies conducted, the ANN trained with 5-5-5 neurons survived for 3497 rounds and successfully transmitted 96757 packets to the sink. In contrast, the LEACH algorithm could only survive for 1773 rounds and transmitted 61766 packets to the sink under the same conditions. The study confidently concluded that there was a significant and noteworthy improvement in both the network lifetime and the number of packets sent to the base station. On the other hand, it was also observed that the proposed method showed a notable improvement when compared with similar recent studies conducted with the same parameters by a factor ranging from 3.63 to 6.26.

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Section: Related Workmentioning

confidence: 99%

ANN Based Clusterhead Selection Process in WSN

GANGAL,

SESLI,

HACIOGLU

2024

Preprint

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“…[ [24][25][26][27], [29][30], [32], [38] give the clustering algorithm using fuzzy and other optimization techniques. Krill herd algorithm-based optimized cluster head selection for wireless sensor networks is suggested in [28].…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Rajpoot,

Avtar,

Pandey

et al. 2023

Preprint

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Wireless Sensor Networks have become a vast field of study. In the past few years, it has been seen that the use of wireless sensor networks in various fields, such as health monitoring, landslide detection, agriculture field, etc., is increased. In such real-life applications, many sensor nodes are required for monitoring and collecting data from the environment, and these sensor nodes work autonomously. The network of complete sensor nodes that works on an application is termed WSN. In a WSN, clustering refers to dividing the sensor nodes into several groups called clusters. A cluster head is selected among all clusters, temporarily acting as the particular cluster's base station. This cluster head is chosen based on several factors, including the node's residual energy, distance from the base station, and distance from other sensor nodes. Mainly the cluster head is responsible for increasing the efficiency and scalability of the entire network. Utilizing the Ordered Weighted Averaging (OWA) operator, we have suggested an algorithm for cluster head selection that works better and uses less energy during data transmission. Simulation results showed that our suggested algorithm outperforms others and extends network lifetime by 5–12%.

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“…In the context of fire monitoring, timely reporting to the fusion center is crucial. Hence, one of the primary performance metrics for monitoring event-based Wireless Sensor Networks (WSNs) is the detection delay [ 32 ]. This metric measures the time elapsed between the physical occurrence of an event and the delivery of a sufficient number of packets to the fusion center.…”

Section: Analysis and Findings From Experimental Studymentioning

confidence: 99%

Probabilistic Detection of Indoor Events Using a Wireless Sensor Network-Based Mechanism

Al-Zabin,

Al-Wesabi,

Al Hajri

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.

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An Energy-Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks using Machine Learning

Cited by 16 publications

References 43 publications

ANN Based Clusterhead Selection Process in WSN

ANN Based Clusterhead Selection Process in WSN

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Probabilistic Detection of Indoor Events Using a Wireless Sensor Network-Based Mechanism

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