ETH‐LEACH: An energy enhanced threshold routing protocol for WSNs

Chithaluru, Prem Kumar; Khan, Mohammad S.; Kumar, Manoj; Stephan, Thompson

doi:10.1002/dac.4881

Cited by 35 publications

(18 citation statements)

References 45 publications

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“…In Reference 17, the adaptive ranking and threshold approach are taken into account for evading the wastage of resources, hidden terminal problems, extending the lifetime, and optimizing sleeping time. Time‐division multiple access mechanisms are exploited in the suggested work to reduce the packet collision.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Padmanaban¹,

Periasamy²,

Prasanth

2022

Concurrency and Computation

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The advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT-enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy-efficient IoT-enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy-efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service.The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub-master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state-of-the-art clustering models.

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Section: Related Workmentioning

confidence: 99%

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Padmanaban¹,

Periasamy²,

Prasanth

2022

Concurrency and Computation

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“…The sensors sense the data and forward it to the destination [9]. A WSN is mostly used in military wars, education, healthcare, etc.…”

Section: Related Workmentioning

confidence: 99%

“…The data packets are not being forwarded to the destination and are dropped on their way to the destination due to the malicious nodes. Therefore, to resolve the above-mentioned security issues, the authors propose a central management routing protocol in which the intermediary node acts as a gateway and third party [9]. However, central authority or third party does not provide a fair secure model.…”

Section: Related Workmentioning

confidence: 99%

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Exploiting Blockchain and RMCV-Based Malicious Node Detection in ETD-LEACH for Wireless Sensor Networks

Khan

Sajid

Rauf

et al. 2022

Wireless Communications and Mobile Computing

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In this paper, a routing protocol based on energy temperature degree-low energy-adaptive clustering hierarchy (ETD-LEACH) is proposed. In the protocol, nodes consume less energy when transmitting data, which improves the network lifetime. The proposed protocol selects the cluster heads (CHs) on the bases of degree, temperature, and energy to perform routing. Moreover, for solving the issue of a single point of failure, the blockchain is utilized. The data transactions are also housed in the blockchain, which is deployed on the CHs and BSs, as, in blockchain, multiple nodes take part. Therefore, to perform a consensus between them, a proof-of-authority (PoA) consensus mechanism is used in the underlying work. In the blockchain, the secure hashing algorithm-256 (SHA-256) is used for secure hashing of data transactions. Furthermore, malicious nodes are detected during the routing using the real-time message content validation (RMCV) scheme in the ETD-LEACH protocol. The proposed model is evaluated under the denial-of-service (DoS) attack, the man-in-the-middle (MITM) attack, and the smart contract analysis performed by the Oyente tool. The performance of the proposed model is evaluated through simulations. The ETD-LEACH and energy threshold-low energy-adaptive clustering hierarchy (ETH-LEACH) protocols are compared using different parameters like number of alive nodes, energy consumption, throughput, and delay. ETD-LEACH consumes less energy and has a better network lifetime as compared to ETH-LEACH. In addition, the RMCV-ETD-LEACH network performance is better than that of both DoS-ETD-LEACH and MITM-ETD-LEACH. Moreover, PoA transaction cost is less than that of proof of work. Also, the execution time of SHA-256 is less than the execution time of SHA-512. Moreover, the value of the packet delivery ratio (PDR) is found to be 89.9% and 99.9% with and without the malicious nodes, respectively.

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“…Wireless sensor networks (WSNs) have evolved rapidly in recent years, and have become one of the critical areas for research in network applications [1]. Their low cost and ease of deployment have enabled them to be used in various smart sensor devices in our lives, such as in-vehicle communications, smart homes, and remote monitoring.…”

Section: Introductionmentioning

confidence: 99%

Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

Yao¹,

Yang²,

Yin³

et al. 2022

IEEE Access

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With the popularity of wireless networks, wireless sensor networks (WSNs) have advanced rapidly, and their flexibility and ease of deployment have resulted in more security concerns, making it critical to research network intrusion prevention for WSNs. Denial of service (DoS) is a common network attack, achieving its goal by bringing down the target network. A DoS attack on WSNs devices with limited resources would be fatal. This paper proposes a method based on principal component analysis (PCA) and a deep convolution neural network (DCNN) for DoS traffic anomaly detection in WSNs, based on the vulnerability of WSNs to attacks and the limited storage space of their devices. Compared with the conventional deep learning structure, the proposed model has a lightweight structure and more effective feature extraction capability, which can effectively detect network abnormal traffic in WSNs devices with limited storage capacity. To assure the effectiveness of the proposed model, receiver operating characteristic (ROC) curves, various classification metrics, and confusion matrices are used to verify the classification results of the model. Through experimental comparison, the proposed model, with small model size, outperforms other mainstream abnormal traffic detection models in terms of classification effect.

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ETH‐LEACH: An energy enhanced threshold routing protocol for WSNs

Cited by 35 publications

References 45 publications

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Exploiting Blockchain and RMCV-Based Malicious Node Detection in ETD-LEACH for Wireless Sensor Networks

Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

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