An Improved IDAF-FIT Clustering Based ASLPP-RR Routing with Secure Data Aggregation in Wireless Sensor Network

Babu, M. Vasim; Alzubi, Jafar A.; Ramesh, S.; Patan, Rizwan; Ramachandran, Manikandan; Gupta, Deepak

doi:10.1007/s11036-020-01664-7

Cited by 79 publications

(34 citation statements)

References 15 publications

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“…An optimal route for sink mobility was identified using PSO. Distributed Autonomous Fashion integrated with Fuzzy If-then Rules (IDAF-FIT) algorithm was introduced in [34] for efficient clustering. During clustering, the CH was also selected using the if-then rule.…”

Section: A Few Recent Work Related To Our Proposal Is Given Belowmentioning

confidence: 99%

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Arya

Bagwari²,

Chauhan

2022

IEEE Access

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For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. The entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention to achieve efficient data communication towards the sink node via selected CH and increase the node reachability within the Cluster. An energy-efficient deep belief network (DBN) based routing protocol is developed in this proposed framework, which achieves better data transmission through the selected path. Due to this, the packet delivery ratio (PDR) gets improved. In this framework, the nodes in the whole network are initially grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes belonging to the particular Cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. Finally, the performance of the proposed deep network-based routing protocol is evaluated using different evaluation metrics: network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with a few existing algorithms. The proposed DBN routing protocol has achieved a better network lifetime among all these algorithms.

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Section: A Few Recent Work Related To Our Proposal Is Given Belowmentioning

confidence: 99%

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Arya

Bagwari²,

Chauhan

2022

IEEE Access

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“…The results showed that the weather would affect the number of beach visitors and help tourism marketers and managers make decisions. Babu et al [24] studied the secure data aggregation method in wireless sensor networks and proposed an adaptive source location privacy protection technology using random routing. The packet delivery rate of the proposed method was 20% higher than the existing methods, and the packet loss rate of the system was reduced by 15%.…”

Section: Recent Related Workmentioning

confidence: 99%

Artificial Intelligence Algorithms in Ice and Snow Tourism Promotion from Digital Technology

Zhi-liang

Guo

2022

Wireless Communications and Mobile Computing

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The present work is aimed at using the artificial intelligence (AI) algorithm to study the promotion and publicity of ice and snow tourism (IST). Firstly, the urgent needs of IST external publicity are analyzed based on digital technology. Besides, the dynamic vision sensor technology is used to collect data in the distributed Internet of Things structure of the IST publicity system. Then, the AlexNet algorithm is combined with the digital logic method. The research assumption is that the complete IST image based on AI can be formed according to the AlexNet algorithm. The improved AlexNet algorithm and Chi-square test implement the IST poster emotion recognition and IST publicity model. Then, the intelligent customer service of the IST publicity platform is studied. The results demonstrate that after 120 iterations, the accuracy of the sports-oriented publicity method based on AlexNet can reach about 75%. In addition, the accuracy of the recommended IST publicity algorithm based on AlexNet is close to 90% after 80 iterations. Therefore, the model’s accuracy is improved by at least 9.6% compared with the traditional method. The research has practical application value for the digital and intelligent development of the IST industry.

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“…Reference [13] developed a new data aggregation method of using the extreme learning machine technique to minimise redundant and invalid data, but the system failed to use the cryptographic technique for securing data aggregation. Reference [14] introduced the method called secure data aggregation based on principle component analysis (SDA-PCA) to increase confidentiality and integrity. The SDA-PCA decreased the pack delivery ratio, but the overhead could not be reduced.…”

Section: Introductionmentioning

confidence: 99%

Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

Saravanakumar¹,

Sundararajan²,

Dhanaraj³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes (key generation, signcryption and unsigncryption) is adopted to achieve secure data transmission between the sender and receiver. Firstly, a one-way function is executed to generate the public and private keys for each sensor node in the WSN. Secondly, digital signature generation and encryption are both performed. The sender node, which handles the signature generation and data encryption, forwards the data to the aggregator node. Then, the receiver verifies the data by using the sender's public key during data decryption. Thus, data aggregation security can be guaranteed. Finally, the authorised node aggregates the data with much higher data confidentiality and privacy. Performance analysis is conducted by simulating the proposed LCS-SICADNN and conventional models. Results of comparison indicate that the LCS-SICADNN can improve data aggregation security with higher throughput and lesser delay, packet drop and overhead compared with the conventional methods.

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An Improved IDAF-FIT Clustering Based ASLPP-RR Routing with Secure Data Aggregation in Wireless Sensor Network

Cited by 79 publications

References 15 publications

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Artificial Intelligence Algorithms in Ice and Snow Tourism Promotion from Digital Technology

Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

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