Data Transmission Protocol for Reducing the Energy Consumption in Wireless Sensor Networks

Alhussaini, Rafal; Idrees, Ali Kadhum; Salman, Mahdi Abed

doi:10.1007/978-3-030-01653-1_3

Cited by 20 publications

(11 citation statements)

References 25 publications

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“…The experiments consider the following metrics: the deredundancy ratio, the deredundancy error, the influence of the amount of similar data clusters K 1 on the deredundancy ratio, and the energy consumption. The proposed HMDA will be compared with the TCDA, TSDA, and Dat algorithms [9]. The parameters and their values are shown in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…Second, the influence of K 1 on the deredundancy ratio in the second stage is analyzed. Finally, the energy consumption is analyzed with Dat [9], TCDA, TSDA, and HMDA. In the first stage, the sink performs clustering according to the nodes' coordinate positions by running the improved k-means clustering algorithm.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Considering that the data volume increases with unexpected ratios in WSNs, an integrated divide and conquer method with an enhanced k-means scheme was proposed [8], which removes redundant data from the collected measures. To save the limited energy of WSNs, a data transmission (Dat) protocol, which can reduce the data transmission cost inside each sensor node by removing redundant data to save energy while maintaining a suitable level of accuracy in the received readings at the sink, was presented [9]. To conserve energy and enhance the lifetime of a WSN, reducing the amount of data communicated by exploiting the temporal and spatial correlations of the sensed data is a suitable approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual-metric distance is employed, and redundant nodes are preliminarily identified by the improved k -means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

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Section: Methodsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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“…WSNs have recently gained significant attention for their implications found in various fields including ecosystem monitoring, health care, environment assessment, urban areas applications, control maintenance, and target tracking [1].…”

Section: Introductionmentioning

confidence: 99%

Distributed DBSCAN Protocol for Energy Saving in IoT Networks

Hameed

Idrees

2021

Lecture Notes in Electrical Engineering

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Sensor Networks form a crucial topic in research, as it seems to target a huge variety of uses in which it could be applied, such as healthcare, smart cities, environment monitoring, military, industrial automation, and smart grids. The clustering algorithms represent an essential factor in conserving power within energy-constrained networks. The selection of a cluster head balances the energy load within the network in a proper way, eventually contributing to the reduction of energy consumed, as well as the enhancement of network lifespan. This paper introduced a distributed DBSCAN protocol for saving the energy of Sensor devices in IoT Networks. This protocol is implemented on each IoT sensor device and the devices apply the DBSCAN algorithm to partition the network into clusters in a distributed way. The efficient periodic cluster head strategy is proposed based on certain criteria like remaining energy, number of neighbors, and the distance for each node in the cluster. The cluster head will be chosen in a periodic and distributed way to consume the power in a balanced way in the IoT sensor devices inside each cluster. The comparison results confirm that our protocol can conserve power and enhance the power conservation of the network better than other approaches.

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“…This point is very precarious in signal processing systems. 3,4 Since the data that have been collected through sensor nodes is usually redundant and includes a high correlation, 5,6 the adaptive data gathering method is therefore seen to be an essential processing procedure so as to save energy within WBSNs. This method mainly aims at decreasing the amount of data gathered in WBSNs, as well as permitting nodes to change their rate of sampling in light of the patient's dynamic risk level within the area of monitoring.…”

Section: Introductionmentioning

confidence: 99%

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Jaber

Idrees

2020

Int J Communication

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SummaryOne of the best and cheapest solutions for continuous and remote monitoring and evaluation of patient health is to use the WBSNs (wireless body sensor networks) due to its great role in decreasing the expenses of the health care system. In this type of network, the sensed vital signs are gathered by biosensor devices then transmitted to the coordinator for further processing and fusion. Since the limited resources for biosensor devices (energy, memory, and processing) in addition to the periodic transmission for the large volume of data, it is essential to optimize the data transmission to save energy while keeping the data accuracy at the coordinator. This work suggests an AREDaCoT (adaptive rate energy‐saving data collecting technique) which aims to energy‐efficient patient health monitoring in periodic WBSNs. The AREDaCoT works in terms of so‐called periods. Each period has two stages: removing redundancy and adapting the sampling rate. The first stage uses improved LED to remove the redundancy in measurement of vital signs, whereas the second stage applies two techniques, assesses the risk score according to the risk level of the patient and the CSr (calibrate sampling rate) fittingly with different ranges of risk level for the sensor sampling ratio to be adapted. The performance of AREDaCoT has been evaluated in light of multiple series of simulations on real health datasets being compared to an existing approach. The acquired results illustrate how AREDaCoT decreases the volume of gathered data; thus, a significant energy saving has been made whilst preserving data accuracy and integrity. Moreover, the percentage results of data reduction over the original data set and the score differences between them are both acceptable.

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Data Transmission Protocol for Reducing the Energy Consumption in Wireless Sensor Networks

Cited by 20 publications

References 25 publications

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Distributed DBSCAN Protocol for Energy Saving in IoT Networks

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

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