Deep neural network-based clustering technique for secure IIoT

Mukherjee, Amrit; Goswami, Pratik; Yang, Lixia; Tyagi, Sumarga Kumar Sah; Samal, Umesh Chandra; Mohapatra, Sushanta Kumar

doi:10.1007/s00521-020-04763-4

Cited by 26 publications

(15 citation statements)

References 29 publications

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“…the comparison of optimal allocation of resources is shown with the other existing optimization methods, where the normalized value of power is presented with respect to number of simulations. The optimal allocation becomes less as the number of simulation increases due to the utilization of resources is maximum at the beginning of the process and our proposed hybrid NN optimization scheme outperforms other two methods: APSO [34] and Deep Neural Network (DNN) [36]. The efficiency of correlation or the achieved correlated information value after clustering is presented in Fig.…”

Section: ⅵ Simulation and Discussionmentioning

confidence: 96%

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Mukherjee

Goswami

Khan

et al. 2021

IEEE Internet Things J.

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The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). The Industrial equipment in 6G contains a large number of wireless sensors, which collect a large amount of data by sensing the surrounding environment, but the data is not always useful. The emergence of data mining has undoubtedly found a breakthrough point for extracting effective information from massive data. In the pursuit of lower latency, edge computing has also begun to develop. Eventually, 6G can make intelligent decisions in real-time and realize automated equipment operations. However, with the application of various technologies, the energy consumption of the system has increased, but the energy carried by the sensor is still limited. This paper addresses the energy consumption problem with a system model of industrial wireless sensor networks based on a multi-agent system (MAS) for industrial 6G applications. The method uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, we analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

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Section: ⅵ Simulation and Discussionmentioning

confidence: 96%

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Mukherjee

Goswami

Khan

et al. 2021

IEEE Internet Things J.

Self Cite

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“…Guo et al [13] attempted to increase DNN bandwidth. DNN technology and signal strength optimization was recommended by Mukherjee and colleagues [14] to improve QoS and network security in the industry IoT network. The DNN framework gains a significant amount of power by incorporating recurrent feedback.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The DNN framework gains a significant amount of power by incorporating recurrent feedback. According to Mou et al [14], (ReDNN) was developed with the intention of teaching itself how to comprehend the spectral, spatial, and temporal aspects of a picture. Images obtained by remote sensing were used for the investigation of this structure.…”

Section: Literature Reviewmentioning

confidence: 99%

An Examination of the Secure Chaos of 5G Wireless Communication Based on the Intelligent Internet of Things

Yonan¹

2022

IJRCS

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The implementation of an intelligent system for network control and monitoring that is built on an Internet of Things (IoT) is a focus of this line of research, with the end objective of improving the level of precision inside the network and its applications. You did indeed read it correctly; the system that is being referred to here is a deep neural network. The manner that it is constructed makes it possible for the layer that cannot be seen to contain more data. The application of element-modified deep learning and network buffer capacity control helps to improve the overall service quality that is provided by each sensor node. One method that can be applied to the process of instructing a machine to pay more attention includes deep learning in its various incarnations. The team was able to do calculations with a precision of 96.68 percent and the quickest execution time, thanks to the usage of wireless sensors. Using a sensor-based technique that has a brief implementation period, this piece has a degree of accuracy of 97.69 % when it comes to detecting and classifying proxies, and it does so using a method that is very efficient. On the other hand, our research represents a significant leap forward in comparison to earlier studies due to the fact that we were able to accurately identify and categorize a wide variety of invasions and real-time proxies.

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“…The simulation-based experimental results are proven to have significant improvements over other solutions. The authors in [31] proposed a novel clustering method based on power demand, which assures the security of data information in IIoT-based applications using machine learning. In a first step, from mutual information of the primary channel and eavesdropping channel, the security capacity of the system is calculated.…”

Section: Related Workmentioning

confidence: 99%

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

Rehman

Haseeb

Lloret

et al. 2021

Sensors

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In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

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Deep neural network-based clustering technique for secure IIoT

Cited by 26 publications

References 29 publications

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

An Examination of the Secure Chaos of 5G Wireless Communication Based on the Intelligent Internet of Things

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

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