International Journal of Computer Networks and Applications

S. Arockia Jayadhas ¹, S. Emalda Roslin ²

Affiliations
1 Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, IN
2 Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, IN

Abstract

The routing performance in Wireless Multimedia Sensor Network (WMSN), which transfers and receives multimedia content like a scalar, audio and video, is often affected by malicious nodes and residual nodes. An external attacker modifies the characteristics function of the node, and thus the node becomes malicious nodes in WMSN. These malicious nodes will affect the functionalities of its surrounding nodes and prevent routing through it and other nodes. Hence, the detection and mitigation of malicious nodes are essential to improve the routing efficiency in WMSN. The conventional methods mainly used machine learning algorithms to identify the malicious nodes in WMSN, which provided low accuracy and consumed more detection time as the main drawbacks. The proposed methodology resolves these drawbacks of the conventional algorithms in this paper. This paper presents an efficient method for detecting and mitigating the malicious nodes using feature index, which is optimized by a Genetic Algorithm (GA). The optimized feature set is classified by the modified LeNET deep learning classification approach. Even though conventional deep learning architectures provide a high classification rate for malicious node detection, it consumes a high detection time to identify malicious nodes. This drawback is overcome by modifying the internal layers of the existing LeNET architecture into parallel, and the dense layers in the existing LeNET architecture are replaced by Fuzzy C Means (FCM) algorithm. The performance of the proposed methodology is analyzed with respect to misclassification rate, precision, recall, accuracy and F1-score parameters.

Keywords

Wireless Multimedia Sensor Network, Malicious Node, Detection, Mitigation, Classification, Genetic Algorithm, FCM.

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Varadala Sridhar ¹, S. Emalda Roslin ¹

Affiliations
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, IN

Abstract

The rising wireless service constraints and user compactness have to lead the progress of 6G communication in the modern days. The benefit of 6G over the presented technologies is a huge support for mixed applications and mobility maintenance. Device to Device (D2D) data transmission in 6G has great attention since it gives a better data delivery rate (DDR). Recently, several methods were established for D2D data transmission. However, energy consumption was not considered to improve the network throughput. To handle such problems, an artificial intelligence technique called Deep Neural Regressive Tangent Transfer Classifier (DNRTTC) model is introduced in this research for D2D data transmission in a 6G system. The designed method includes several layers to attain energy-efficient D2D data transmission. The primary layer is the input layer and it includes several mobile nodes as input. Nodes are transmitted to the hidden layer one. For each node, energy, received signal strength, and connection speed of each mobile node is calculated. Then the similarity analysis is done in the following layer where each node is analyzed with its threshold value. The result is sent to the output layer where the better resource mobile nodes are identified by using the activation function. This leads to attaining energy-efficient D2D data transmission in 6G. Results illustrate that the DNRTTC outperformed compared to conventional methods with better energy efficiency, packet delivery ratio, and throughput.

Keywords

Artificial Intelligent, Device to Device Data Transmission, 6G Network, Energy Efficiency, Deep Neural Network, Mobile Nodes, Activation Function

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