Energy-Efficient and Coverage-Guaranteed Unequal-Sized Clustering for Wireless Sensor Networks

Gharaei, Niayesh; Al‐Otaibi, Yasser D.; Butt, Suhail; Sahar, Gul; Rahim, Sabit

doi:10.1109/access.2019.2950237

Cited by 20 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arghavani et al [22] studied an optimal multi-hop communications for a circular area surrounding a sink in which the network was partitioned into nearly the same size clusters and optimal number of clusters (namely, CH) was discussed under multi-hop routing. An Energy-efficient and Coverageguaranteed Unequal-sized Clustering (ECUC) scheme for the circular area was proposed in [23], its innovation lies in that the energy consumption after clustering was reduced by considering the node power consumption before clustering using the cluster size optimization. In [24], the cluster formation method is that the larger numbers of clusters were formed closer to BS and reduce as one moves away from the BS.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

Yao

Cui

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

Wireless sensor networks (WSNs), one of the fundamental technologies of the Internet of Things (IoT), can provide sensing and communication services efficiently for IoT-based applications, especially energy-limited applications. Clustering routing protocol plays an important role in reducing energy consumption and prolonging network lifetime. The cluster formation and cluster head selection are the key to improving the performance of the clustering routing protocol. An energy-efficient routing protocol based on multi-threshold segmentation (EERPMS) was proposed in this paper to improve the rationality of the cluster formation and cluster heads selection. In the stage of cluster formation, inspired by multi-threshold image segmentation, an innovative node clustering algorithm was developed. In the stage of cluster heads selection, aiming at minimizing the network energy consumption, a calculation theory of the optimal number and location of cluster heads was established. Furthermore, a novel cluster head selection algorithm was constructed based on the residual energy and optimal location of cluster heads. Simulation results show that EERPMS can improve the distribution uniformity of cluster heads, prolong the network lifetime and save up to 64.50%, 58.60% and 56.15% network energy as compared to RLEACH, CRPFCM and FIGWO protocols respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

“…. , w K * represent the cumulative probability of angle values of each cluster respectively, as shown in (23). Finally, the expression of the angle variance of inter-clusters nodes f 1 (T ) is shown in (24).…”

Section: ) Node Clustering Based On Variance Of Node Angles Andmentioning

confidence: 99%

Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

Yao

Cui

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Energy Efficient and Coverage-Guaranteed Unequal-Sized Clustering (ECUC) [17] is another scheme based on adjusting the size of clusters for optimizing network performance in terms of network lifetime. ECUC protocol uses unequal clustering with the BS positioned at center of the circular region where the whole circular region is divided into equalsized circular sectors (also called corona); each sector is further divided into an equal number of clusters.…”

Section: Related Workmentioning

confidence: 99%

Improved Unequal-Clustering and Routing Protocol

Alharbi

Kolberg

2021

IEEE Sensors J.

View full text Add to dashboard Cite

Increased network lifetime is a desired property of low-powered and energyconstrained Internet of Things (IoT) devices that are deployed in wireless network environments. Clustering is used as a technique in multiple solutions to improve overall network lifetime. Further variants in the clustering process are defined to optimize the results. One such variant is equal clustering, where all the clusters have the same size. However, this approach suffers from the issue of nodes closer to the base station (BS) dying out earlier. As an alternative, unequal clustering is proposed, where clusters close to the BS are of smaller size; thus, cluster heads (CHs) consume a substantial proportion of their energy for being acting as data forwarding nodes. In this paper, we propose an unequal clustering approach with the BS at the center of a circular area. The size of each cluster is fixed and computed based on the node density of the area. The number of clusters increases from outwards to inwards towards the BS. The results show considerable performance gain over selected benchmark works.

show abstract

“…IoT-based technologies have gained numerous growth in the development of smart cities and support to real-time communication systems [ 1 , 2 , 3 ]. Wireless sensor networks (WSN) enable low-power deployments, and they have become the dominating choice in the composition of IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

Haseeb

Rehman

Bahaj

et al. 2022

Sensors

View full text Add to dashboard Cite

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.

show abstract

Energy-Efficient and Coverage-Guaranteed Unequal-Sized Clustering for Wireless Sensor Networks

Cited by 20 publications

References 31 publications

Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

Improved Unequal-Clustering and Routing Protocol

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

Contact Info

Product

Resources

About