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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Trupti Shripad Tagare
Dayananda Sagar College of Engineering
Computer Science & IT Engineering

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Design and implementation of duty cycle-based futuristic clustering technique in WSN Trupti Shripad Tagare; Rajashree Narendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp951-959

Abstract

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.
Deep learning intrusion detection for software-defined networking using synthetic minority oversampling Prajwalasimha Sindugatta Nagaraja; Navya Rajashekara; Pushpa Bangalore Ramesh; Druva Kumar Siddaraju; Santhosh Kumar Ramachandragowda; Trupti Shripad Tagare
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3703-3711

Abstract

This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOTE) to address class imbalance in datasets like network security laboratory–knowledge discovery in databases (NSL-KDD). The method aims to accurately classify network traffic by learning temporal patterns of both normal and malicious activities. SMOTE is employed to balance the dataset, ensuring that underrepresented attack types receive adequate model attention, thereby improving model robustness. The proposed models (LSTM, GRU, and BiLSTM) are trained and evaluated on the NSL-KDD dataset, with hyperparameter tuning performed through RandomizedSearchCV for optimal performance. The results show a significant improvement in accuracy, precision, recall, and F1-score, with BiLSTM demonstrating the highest performance, achieving near-perfect classification results (99.5% accuracy). This method not only mitigates the issue of class imbalance but also leverages the power of RNNs for sequence modeling, offering a promising solution for effective intrusion detection in modern networks.
Co-Authors Druva Kumar Siddaraju Navya Rajashekara Prajwalasimha Sindugatta Nagaraja Pushpa Bangalore Ramesh Rajashree Narendra Santhosh Kumar Ramachandragowda