An Energy-Optimization Clustering Routing Protocol Based on Dynamic Hierarchical Clustering in 3D WSNs

Zhao, Zhidong; Shi, Duoshui; Hui, Guohua; Zhang, Xiaohong

doi:10.1109/access.2019.2923882

Cited by 37 publications

(18 citation statements)

References 27 publications

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“…RELATED WORK Several studies were conducted to improve the GH reconfiguration to prolong the network lifetime. In [19], a distance similarity index with a dual GH was discussed to minimize the computation. Similarly, [20] proposed a fitnessbased glowworm swarm with a fruit fly algorithm (FGF) to improve time delay in GH reconfiguration.…”

Section: IImentioning

confidence: 99%

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Ramasamy

Anisi

Jindal

2022

IEEE Trans. on Green Commun. Netw.

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Section: IImentioning

confidence: 99%

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Ramasamy

Anisi

Jindal

2022

IEEE Trans. on Green Commun. Netw.

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“…An improved dynamic hierarchical clustering technique was introduced in [15] to find the CH based on residual energy. But the higher data delivery rate was not minimized.…”

Section: Related Workmentioning

confidence: 99%

Resource Aware Gaussian Regressive Jarvis Patrick Clustering for Reliable Data Transmission in E-Health using WSN-IoT

Suguna¹

2021

TURCOMAT

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Wireless sensor network (WSN) includes numerous sensor nodes (SN) integrated with Internet of Things (IoT) play a crucial role in numerous applications. IoT links physical devices as sensor and forms whole network for sharing information. The IoT has been used in different domains. In this scenario, patients utilize wearable medical sensors to monitor medical parameters. This medical sensor is equipped with batteries and has limited energy. Therefore, the network lifetime enhancement is a major challenging issue. To prolong network lifetime, novel technique called Resource-efficient Gaussian process regressive Jarvis Patrick clustering (REGPRJPC) is introduced. At first, the IoT devices are used in SN for sensing and collecting the patient data. After data collection process, SN is grouped into diverse clusters using Jarvis Patrick clustering technique. Jarvis Patrick clustering is graph-based clustering to partition SN with help of Gaussian process regression function. The regression function analyzes the SN and performs the clustering process based on the estimated energy and bandwidth. After clustering process, cluster head (CH) is selected to enhance data transmission and minimizes delay. Source node transmits gathered data to their CH. Then CH finds nearest CH using time of flight method. Followed by, data transmission is performed from source to sink node via the cluster head. In this way, resource-efficient data transmission is performed in WSN. Numerical analysis indicates that the REGPRJPC technique efficiently improves the reliable patient data packet delivery and minimizes the loss rate, delay.

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“…In addition, there are other clustering methods. For example, the energy-optimization clustering routing protocol based on dynamic hierarchical clustering in 3D WSNs(3DHCP) [5] protocol adopts the clustering method of first calculating the optimal number of clusters in the network. It is dividing the network according to the optimal number of clusters and then finding suitable nodes in each region to become CHs.…”

Section: Relative Workmentioning

confidence: 99%

Routing Clustering Protocol for 3D Wireless Sensor Networks Based on Fragile Collection Ant Colony Algorithm

et al. 2020

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Compared with two-dimensional wireless sensor networks (2DWSNs), three-dimensional wireless sensor networks (3DWSNs) have higher node energy consumption and weaker load balancing. To alleviate these problems, we propose a new multi-hop routing clustering protocol for a wide range of 3DWSNs. In terms of clustering, nodes will run for cluster head (CH) by timing broadcast based on their remaining energy and their average distance from surrounding nodes. Two new identity nodes are added between the CH and the member nodes, the load transfer node responsible for transferring the energy consumption of the CH, and the secondary cluster head node (SCH) acting as the CH for the next round. In order to reduce energy consumption in key node areas, we also enable key nodes to communicate directly with the sink node. In the aspect of routing construction, SN generates the routing table corresponding to each CH through our proposed fragile ant colony algorithm. Because CH and SCH exist at the same time and have a certain replacement order, the SN only needs to obtain the location of each CH when the network is first clustered, and can continuously generate a new routing table corresponding to the CH, which reduces the energy consumption of the network in the routing path construction. We simulate this protocol, LEACH protocol, AZ-SEP protocol, and UCNPD protocol in a three-dimensional environment. The results show that this protocol is better than the other three protocols in terms of network lifetime and network load balance, respectively. INDEX TERMS 3DWSNs, clustering algorithm, routing algorithm, network lifetime, network load balancing.

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An Energy-Optimization Clustering Routing Protocol Based on Dynamic Hierarchical Clustering in 3D WSNs

Cited by 37 publications

References 27 publications

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

Resource Aware Gaussian Regressive Jarvis Patrick Clustering for Reliable Data Transmission in E-Health using WSN-IoT

Routing Clustering Protocol for 3D Wireless Sensor Networks Based on Fragile Collection Ant Colony Algorithm

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