Energy Efficient Machine Learning Technique for Smart Data Collection in Wireless Sensor Networks

Soundari, A. Gnana; Jyothi, Vinayaka

doi:10.1007/s00034-019-01181-3

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…is significantly reduces the time delay of data transmission on the network and, at the same time, significantly improves the reliability of the network. However, the limitation lies in that the node devices that build the network must have specific computing and storage capabilities, and the running cost of the network is higher than that of the star network and the tree network [21,22].…”

Section: Wsn Based On Zigbee Technologymentioning

confidence: 99%

Construction of Carbon Audit and Verification System Framework Based on Intelligent Wireless Sensor Network

Guo

2022

Mobile Information Systems

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With the large amount of carbon emissions from many enterprises over the years, the global greenhouse effect is getting worse. In order to protect the climate and environment on which we depend, the world is taking active measures to reduce carbon emissions as much as possible. At present, the domestic carbon auditing and verification system is relatively late and immature, and the traditional system efficiency is relatively low. Wireless sensor network (WSN) is a particularly flexible distributed wireless communication network, which can coordinate the perception, processing, transmission, and collection of data of objects perceived within the network until the data content is transmitted to the user terminal. This paper aims to study and design an intelligent network model based on WSN so as to calculate and verify the carbon emissions of enterprises and countries. In this paper, an intelligent WSN model based on ZigBee technology is proposed to build an efficient, low-consumption, and high-precision carbon audit and verification system framework. In this paper, the system framework is referred to as ZWCA for short, that is, the carbon audit and verification system framework of WSN based on ZigBee. In addition, this paper compares the ZWCA framework with the traditional carbon audit and verification system in several groups to analyze the practical feasibility and advantages of the ZWCA framework. The test results show that the ZWCA framework proposed in this paper can audit and verify the carbon emissions of major enterprises very well, and the initial startup power of the ZWCA framework is only 1/2 of the traditional one. At the same time, its actual running power consumption is only 3/4 of that of the traditional audit and verification system framework, and the calculation accuracy is as high as 99%. Moreover, the number of node deaths has decreased, and the system stability has been significantly improved.

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Section: Wsn Based On Zigbee Technologymentioning

confidence: 99%

Construction of Carbon Audit and Verification System Framework Based on Intelligent Wireless Sensor Network

Guo

2022

Mobile Information Systems

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“…The MLS specifies the parent node of each node through growing a tree from a root node. At any time, the MLS adds an edge to the growing tree for reducing the relative load of the nodes, which currently have the maximum axial load while minimizing other nodes [20].…”

Section: Related Workmentioning

confidence: 99%

A firefly algorithm for power management in wireless sensor networks (WSNs)

Pakdel

Fotohi

2021

J Supercomput

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In Wireless Sensor Networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks.Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-FIREFLY is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink.The proposed method is evaluated with NS-2 simulator and compared with the Algorithm-PSO and Optimal clustering methods. The evaluation results show the efficiency of the EM-FIREFLY method in Maximum relative load and Network Lifetime criteria compared to other methods discussed in this article.

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“…Diwakaran et al [18] used the inherent correlations between the continuous observations of SNs and the data similarity measures of adjacent SNs to reduce data transmission. A new model based on monkey tree search behavior inspired by fauna was explored in [23], and the fuzzy reasoning mechanism was used to complete data collection and dissemination. Rida et al [24] utilized data aggregation techniques based on the Euclidean distance to reduce similar data.…”

Section: Related Workmentioning

confidence: 99%

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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Energy Efficient Machine Learning Technique for Smart Data Collection in Wireless Sensor Networks

Cited by 15 publications

References 17 publications

Construction of Carbon Audit and Verification System Framework Based on Intelligent Wireless Sensor Network

Construction of Carbon Audit and Verification System Framework Based on Intelligent Wireless Sensor Network

A firefly algorithm for power management in wireless sensor networks (WSNs)

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

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