Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Zhu, R. Y.; Yu, Mai; Li, Yuanli; Wang, Jun; Liu, Lu

doi:10.1155/2021/6664324

Cited by 3 publications

(5 citation statements)

References 34 publications

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“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years 25–33 . The PFF strategy is used in the sensor and aggregator devices 25 .…”

Section: Related Workmentioning

confidence: 99%

“…They eliminate the redundancy of data at the sensor node, then use a variety of techniques to lessen spatially similar data in the gateway. The authors in Zhu et al 27 suggest a deredundancy algorithm that works at two levels. The first level produces a dual‐metric distance, and to obtain clusters of similar nodes, the enhanced k‐means method first identifies redundant nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years. [25][26][27][28][29][30][31][32][33] The PFF strategy is used in the sensor and aggregator devices. 25 The Jaccard similarity is used by researchers in the sensor node to lower the redundancy of data, and set similarity in the aggregator node to minimize duplicated sets of data.…”

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confidence: 99%

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Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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Summary In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy.

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“…Several data reduction algorithms based on clustering techniques have been proposed in the past few years 25–33 . The PFF strategy is used in the sensor and aggregator devices 25 .…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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“…Wen et al [25] proposed a redundant node identification method based on local Delaunay triangular dissection and multi-node election dormancy mechanism. Zhu et al [26] proposed a multi-level hierarchical clustering de-redundancy algorithm to discriminate redundant sensing devices by multi-level clustering. However, this algorithm requires high computational power of sensing devices.…”

Section: Related Workmentioning

confidence: 99%

A redundant sensing device schedule method under event monitoring coverage constraints for edge computing

Chen

Long

Zhang

2022

Preprint

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Billions of sensing devices are deployed in the smart city to generate a large amount of sensing data for various applications. However, duplicated data caused by redundant deployed sensing devices makes it a great challenge to gather data with low cost and high efficiency. In this paper, a redundant sensing device schedule method is proposed through redundant sensing device identification scheme based on device redundancy and similarity (RSDI-DRS) under event monitoring coverage constraints. The key idea of the RSDI-DRS is to reduce the number of unnecessary working sensing device to decrease energy consumption and data load. Comparing to the previous researches, the innovation of RSDI-DRS is as follows: First, a novel sensing device redundancy and similarity calculation method is proposed. Second, the network connectivity is realized by the network connectivity discrimination scheme. In the proposed scheme, the redundant device set is established by calculating the sensing device redundancy score. Then, the device similarity score is calculated for the devices in the redundant device set, and the scheduling priority of the redundant device is obtained based on the similarity score and the remaining energy of the sensing device. The device with the maximum scheduling priority is scheduled to sleep. The process loops under the satisfaction of event coverage constraint. Meanwhile, the network connectivity is realized in the process. If an isolated sensing device occurs, the neighbor device with the minimum scheduling priority will not be scheduled to sleep. Simulation results show that the proposed scheme could prolong the network life by 16.30% ~ 31.18% and realized the connectivity of working sensing devices under the event monitoring coverage at the cost of the execution time growth compared with the scheme of Relocating Redundant Sensors-Redundant Node Identification (RRS-RNI).

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“…Most WSN applications use the IEEE 802.15.4 standard due to its hybrid capabilities. Some applications are sensitive in nature, and vulnerability in data delivery is unacceptable [5][6][7]. Due to its wide utility, the beacon-enabled mode of the standard remains in prime research.…”

Section: Introductionmentioning

confidence: 99%

A Novel QoS‐Oriented Intrusion Detection Mechanism for IoT Applications

Noorwali

Alvi

Khan

et al. 2021

Wireless Communications and Mobile Computing

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Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN-based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision-based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.

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Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Cited by 3 publications

References 34 publications

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

A redundant sensing device schedule method under event monitoring coverage constraints for edge computing

A Novel QoS‐Oriented Intrusion Detection Mechanism for IoT Applications

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