Improved Deep Convolutional Neural Network based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks
ABSTARCT :
Wireless Sensors Networks (WSN) is the self-configured wireless network which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes' (MNs') presence. Thus, to shun security threats, it is vital to find and isolate those MN. Consequently, this work proffered a solution for detecting MN in WSN utilizing SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission by means of choosing the Cluster Head (CH) centered on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the EKM algorithm clusters the Trusted Nodes (TN), and, the t-DSBO algorithm selects an individual CH for each cluster centered on those nodes' residual energy. The t-Distribution based Satin Bowerbird Optimization (t-DSBO) selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques.
EXISTING SYSTEM :
? Despite growing interest in deep learning in the mobile networking domain, existing contributions are scattered across different research areas and a comprehensive survey is lacking.
? They overview comprehensively existing efforts that incorporate deep learning into the IoT domain and shed light on current research challenges and future directions.
? We also review open-source libraries for deploying and training neural networks, a range of optimization algorithms, and the parallelization of neural networks models and training across large numbers of mobile devices.
? This novel framework demonstrates significantly higher sum rate and energy efficiency over existing approaches.
DISADVANTAGE :
? Machine learning middleware (MaML) tackled the problem of ontology heterogeneity. However, the potential problem in MaML is the overhead.
? However, accuracy problems might be associated with each of the machine learning algorithms.
? The SVM methods are far superior due to their efficient learning and enhanced performance in non-linear and complex network problems.
? To the best of our knowledge, it is the first time that the GANs algorithm has been used for solving the security problem in WSNs’ middleware.
? This problem is addressed through the proposed unsupervised learning.
PROPOSED SYSTEM :
• The proposed malicious or hidden node detection methodology stated in this paper is applied on WSN environment with Additive White Gaussian Noise (AWGN) channel state between sender and receiver nodes.
• The proposed methodology is evaluated with respect to energy consumption, throughput and delay metrics.
• The proposed methodology achieves 2.53ns as an average delay for the injection of ‘N’ number of malicious nodes.
• The performance analysis of proposed method in terms of energy consumption with respect to number of malicious nodes in WSN system environment.
ADVANTAGE :
? These attacks are capable of dropping packets or modify them, resulting in an impact in the performance of WSNs.
? The learning machine (LM) employs to map the estimates of network performance metrics with target SLA.
? These networks improved WSNs’ performance and optimization during iterative optimization and mutual confrontation.
? This practice resulted in an overall performance reduction, having more layers makes the network more difficult to optimize and more prone to overfitting.
? A high-performance, optimized architecture is obtained with three CNN layers to maintain accuracy of results while also minimizing overhead and overfitting.
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