Faculty Publications
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Publications by NITK Faculty
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Item Enhanced mobility aware routing protocol for Low Power and Lossy Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Sanshi, S.; Jaidhar, C.D.Due to the technological advancement in Low Power and Lossy Networks (LLNs), sensor node mobility becomes a basic requirement for many extensive applications. Routing protocol designed for LLNs must ensure real-time data transmission with minimum power consumption. However, the existing mobility support protocols cannot work efficiently in LLNs as they are unable to adapt to the change in the network topology quickly. Therefore, we propose an Enhanced Routing Protocol for LLNs (ERPL), which updates the Preferred Parent (PP) of the Mobile Node (MN) quickly whenever the MN moves away from the already selected PP. Further, a new objective function that takes the mobility of the node into an account while selecting a PP is proposed. Performance of the ERPL has been evaluated with the varying system and traffic parameters under different topologies similar to most of the real-life networks. The simulation results showed that the proposed ERPL reduced the power consumption, packet overhead, latency and increased the packet delivery ratio as compared to other existing works. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.Item Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.It is crucial to detect anomalous use of electrical power in critical systems to prevent malfunctions or hazards, ensure operational security, and optimize the energy economy. Since anomalies in critical systems can serve as early warning systems for potential issues or threats that could lead to severe failures, it becomes strategically crucial to discover them as soon as possible. This study proposes and suggests a novel technique for anomaly identification in industrial critical systems using a household appliance's electrical power consumption dataset in the absence of a dedicated critical system or industrial equipment dataset. The study looks at the ability of a deep learning (DL) model trained on household data to identify anomalous patterns in large-scale industrial equipment's power use. Convolutional neural network (CNN) is used in this work to analyze anomalous electrical power use based on micro-moments. In this work, an appliance-level dataset is employed for experimentation. 10 × 10 appliance-wise grayscale images are generated from numeric dataset with and without the instance-wise N-gram approach. The effectiveness of the proposed approach is evaluated and compared it with other ML and DL models used earlier. The experimental findings showed that the proposed approach worked better than other models. Compared to images created without the instance-wise N-gram approach, the performance of the proposed approach with images created with N-gram is superior. © 2001-2012 IEEE.Item Anomalous Electrical Power Consumption Detection in Household Appliances via Micro-Moment Classification(Institute of Electrical and Electronics Engineers Inc., 2025) Nayak, R.; Jaidhar, C.D.The detection of anomalous power consumption is critical for improving energy efficiency, particularly with the increasing demand in buildings. This study explores Convolutional Neural Network-based models by transforming 1-dimensional micro-moment labeled data into 2-dimensional matrices to capture both temporal and spatial consumption patterns. Three architectural variants are investigated: a conventional Deep Convolutional Neural Network (DCNN), a Depthwise Separable Convolutional Neural Network (DS-CNN), and a Depthwise Separable Residual Convolutional Neural Network (DSR-CNN). Unlike earlier studies, this work incorporates hyperparameter tuning, statistical validation, and cross-validation, resulting in the evaluation of over 450 model configurations. The results indicate that while the DCNN consistently achieves the highest accuracy, the DS-CNN achieves comparable performance with significantly reduced parameters and computational cost, making it suitable for real-time and resource-constrained environments. Model complexity analysis and statistical tests confirm the robustness of the findings. Finally, a systematic model selection strategy is presented, identifying the DS-CNN as the most balanced solution for effective and efficient anomaly detection in smart grid applications. © 2020 IEEE.Item Data-driven models for electricity theft and anomalous power consumption detection: a systematic review(Springer, 2025) Nayak, R.; Jaidhar, C.D.To maintain the effectiveness, dependability, and security of modern energy systems, analyzing and detecting anomalies in energy usage, such as electricity theft and unusual power consumption, is crucial as Smart Grid (SG) technologies become increasingly common. This survey paper comprehensively reviews the literature on energy consumption analysis and detection, focusing on detecting electricity theft and anomalous power consumption. The works that are considered in this paper are classified based on Machine Learning (ML), Deep Learning (DL), and hybrid models, to identify electricity theft and unusual power usage. Privacy preservation-based methodologies in the context of energy consumption research and summarize the survey articles. Furthermore, datasets used in electricity theft and anomalous power consumption detection, applications, challenges, and limitations related to detecting abnormal power usage and electricity theft are also discussed, and suggested future research paths to push the boundaries of this field of work. This survey study offers a thorough overview of current research trends and directions in energy consumption analysis and detection by synthesizing ideas from various studies. It benefits researchers, practitioners, and policymakers in the energy sector. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.