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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Husain, Saif O. Dixit, Krishna Kant Boob, Nandini Shirish Reddy, B. Ravali Kumar, Jaysheel Sharma, Smita Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts of data, increasing the need for effective anomaly detection mechanisms to ensure system reliability and security. Traditional methods often fall short due to the high dimensionality and dynamic nature of IoT data. This paper presents a deep learning-based approach for real-time anomaly detection in IoT systems, leveraging the power of neural networks to identify patterns and anomalies within complex datasets. By employing deep learning algorithms such as Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) networks, this framework can efficiently detect abnormal behaviors in real-time, offering a significant improvement over conventional methods. The proposed system is trained on labeled datasets collected from IoT devices, allowing it to distinguish between normal and anomalous activity. Furthermore, the model adapts to new data, enhancing its detection capabilities over time. Experimental results demonstrate that this approach achieves high accuracy and low false-positive rates, making it suitable for deployment in real-world IoT environments. The framework's scalability and adaptability highlight its potential for enhancing the security and reliability of IoT networks, particularly in critical applications such as smart cities, healthcare, and industrial IoT. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging IoT and AI in Precision Agriculture for Efficient Water Management

Leveraging IoT and AI in Precision Agriculture for Efficient...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Kumar, Yogendra Singla, Atul Thandra, Nagarjuna LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

Where water is limited the challenge of getting the best management of the resource become a major issue in farming. In this study, therefore, we focus on the following question: What role may AI and the IoT play in precision agriculture towards enhancing sustainability through the conservation of water resources? IoT devices that can provide information on the environmental conditions include;sensors for moisture level in the soil, and weather systems. To such data artificial intelligence algorithms are applied to automate the process of irrigation, determine possible water consumption and avoid excess. Real-time and historical data can also be evaluated using machine learning approaches of the system. From this it is then able to predict when crops will need water and then align their irrigation times from this information. Moreover, the analytic model using AI may find potential issues with irrigation and present mitigating steps. Juxtaposing IoT and AI, it is revealed in this research how the two technologies can improve water management decisionmaking with the aim of minimizing water depletion as well as over-irrigation. Integrated soil nutrients management and conservation agriculture are recommended and the overall atmospheric water scarce zone sustainable agricultural productivity is enhanced by the proposed framework while increasing water productivity. The study reveals that it is possible to steer the future of water management through the interconnection of IoT and AI in precision agriculture. It may therefore result in higher production, efficiency in resource utilization and ultimate sustainability. © 2025 IEEE.

关键词： Irrigation

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Hybrid Feature Selection Based Lightweight network Intrusion Detection System for MQTT Protocol 15

Hybrid Feature Selection Based Lightweight Network Intrusion...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Ariffin, Sharifah H. S. Mustaffa, Nordurratul Hikmah Dewanta, Favian Hamzah, Ida Wahidah Baharudin, Muhammad Ariff Abdul Wahab, Nur Haliza Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Johor Bahru Malaysia School of Electrical Engineering Telkom University Indonesia Universiti Teknologi Malaysia Faculty of Computer Johor Johor Bahru Malaysia

ISBN: (纸本)9798350316551

The proliferation of Message Queuing Telemetry Transport (MQTT) communication on the Internet of Things (IoT) has raised significant security challenges, a lightweight network Intrusion detection System (NIDS) model is needed to reduce the complexity of the process in IoT modules. This paper proposed a NIDS based hybrid feature selection, combining XGBoost and MaxPoolingID. The proposed algorithm enhances the MQTT-enabled IoT security by effectively capture the complex pattern and extracts relevant features. The algorithm is experiment in two scenarios which is the uniflow and biflow of the MQTT dataset. The results are shown using 4x4 confusion matrix. In both uniflow and biflow scenarios, the model achieves outstanding accuracy, precision, recall and F1-Score, exceeding 90%. The biflow scenario outperforms the uniflow across all matrixes, making it preferable for network data classification. © 2023 IEEE.

关键词： Internet of things

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Toward Efficient Coordination of Power Distribution network and Electric Vehicles: Deep Reinforcement Learning With Robust Reward Function

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8164-8176页

作者： Peiyue Li Sheng Chen Zhinong Wei Qiuwei Wu Guoqiang Sun Jiahui Jin School of Electrical and Power Engineering Hohai University Nanjing China School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast U.K.

The rapid increase in electric vehicle (EV) usage has led to an urgent need for coordinating EV charging activities with power distribution networks (PDNs) to accommodate the resulting redistributed electrical demand and implement greater operational flexibility for PDNs. However, current efforts relying on the implementation of EV charging price strategies to influence the decision-making activities of EV users at charging stations face challenges arising from the uncertainty of EV charging behavior and data privacy concerns. This work addresses these issues by developing a robust reinforcement learning approach that requires no personal EV user information to determine charging schedules and pricing strategies at charging stations, while the uncertainty in EV behavior is addressed by applying a robust reward function. The loss function is relaxed using Holder’s and Cauchy-Schwarz inequalities, which yields an upper bound for the loss caused by the worst case scenario and therefore enhances the computational efficiency of the solution process. Numerical results demonstrate that the proposed method contributes to balancing the PDN load and improving the utilization of fast charging stations (FCSs). Compared to deterministic and traditional robust methods, the proposed method reduces computation time in uncertain environments while ensuring moderate revenue of FCSs.

关键词： Pricing Partial discharges Transportation Electric vehicle charging Distributed power generation Costs Reactive power Power transmission lines Electrification Voltage

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Progressive edge-sensing dynamic scene deblurring

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Computational Visual Media 2022年第3期8卷 495-508页

作者： Tianlin Zhang Jinjiang Li Hui Fan School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Institute of ZhongKe Network Technology Yantai 264005China

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many *** recent years,the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively *** have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data *** new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred *** coarse-to-fine architecture restores the image structure,first performing global adjustments,and then performing local *** this way,not only is global correlation considered,but also residual information is used to significantly improve image restoration and enhance texture *** results show quantitative and qualitative improvements over existing methods.

关键词： image deblurring dynamic scenes multiscale edge features

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AI-based Secure Intrusion Detection Framework for Digital Twin-enabled Critical Infrastructure 14

AI-based Secure Intrusion Detection Framework for Digital Tw...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Patel, Tanisha Jadav, Nilesh Kumar Rathod, Tejal Tanwar, Sudeep Garg, Deepak Shahinzadeh, Hossein Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat Ahmedabad India SR University School of Computer Science and Artificial Intelligence Warangal506371 India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350349412

This paper discusses the importance of securing modern society's critical infrastructure in the face of physical and digital threats. It emphasizes the vulnerabilities introduced by programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems that highlight the potential of digital twins to enhance security. The paper presents a research contribution aimed at improving the integrity of data exchange among PLCs equipped with digital twins in critical infrastructure systems. It leverages artificial intelligence (AI) algorithms to detect and mitigate different security attacks, such as denial of service (DoS), injection attacks, and data tampering attacks. The proposed framework efficiently classifies the malicious and non-malicious data of digital twin-based critical infrastructure. Further, the trained AI model is deployed on a critical infrastructure's intrusion detection system that continuously monitors network traffic and system logs in real time, identifying unusual patterns or behaviors that may indicate an intrusion. Further, the performance of the proposed framework is evaluated using accuracy, log loss, and validation curves. © 2023 IEEE.

关键词： Critical infrastructures

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CS-Mixer: A Cross-Scale Vision Multilayer Perceptron With Spatial–Channel Mixing

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4915-4927页

作者： Cui, Jonathan Araujo, David A. Saha, Suman Kabir, Md Faisal Pennsylvania State University University ParkPA16802 United States Pennsylvania State University Harrisburg MiddletownPA17057 United States Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States School of Science Engineering and Technology Pennsylvania State University Harrisburg MiddletownPA17057 United States

Despite simpler architectural designs compared with vision transformers (ViTs) and convolutional neural networks, vision multilayer perceptrons (MLPs) have demonstrated strong performance and high data efficiency for image classification and semantic segmentation. Following pioneering works such as MLP-Mixers and gMLPs, later research proposed a plethora of vision MLP architectures that achieve token-mixing with specifically engineered convolution- or attentionlike mechanisms. However, existing methods such as S2-MLPs and PoolFormers typically model spatial information in equal-sized spatial regions and do not consider cross-scale spatial interactions, thus delivering subpar performance compared with transformer models that employ global token mixing. Further, these MLP token-mixers, along with most ViTs, only model one- or two-axis correlations among space and channels, avoiding simultaneous three-axis spatial–channel mixing due to its computational demands. We, therefore, propose CS-Mixer, a hierarchical vision MLP that learns dynamic low-rank transformations for tokens aggregated across scales, both locally and globally. Such aggregation allows for token-mixing that explicitly models spatial–channel interactions, made computationally possible by a multihead design that projects to low-dimensional subspaces. The proposed methodology achieves competitive results on popular image recognition benchmarks without incurring substantially more computing. Our largest model, CS-Mixer-L, reaches 83.2% top-1 accuracy on ImageNet-1k with 13.7 GFLOPs and 94 M parameters. © 2020 IEEE.

关键词： Mixers (machinery)

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Industrial IoT Based Condition Monitoring for Wind Energy Conversion System

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 654-664页

作者： Md Liton Hossain Ahmed Abu-Siada S.M.Muyeen Md Mubashwar Hasan Momtazur Rahman Department of Electrical and Computer Engineering Curtin UniversityPerthAustralia School of Science Engineering and ITFederation UniversityAustralia Edith Cowan University Australia

Wind energy has been identified as the second dominating source in the world renewable energy generation after *** and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system(WECS)including multilevel inverters(MLIs).The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link *** paper proposes a simplified proportional integral(PI)-based space vector pulse width modulation(SVPWM)to minimize the output waveform ripples,resolve the voltage balancing issue and produce better-quality output *** experiences various types of faults particularly in the DC-link capacitor and switching devices of the power *** faults,if not detected and rectified at an early stage,may lead to catastrophic failures to the WECS and continuity of the power *** paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real *** most wind power plants are located in remote areas or offshore,WECS condition monitoring needs to be developed over the internet of things(IoT)to ensure system *** this paper,an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

关键词： Asset management condition monitoring fault diagnosis industrial internet of things(IoT) wind energy conversion system

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Adversarial Attacks on Influence Maximization in Homogeneous Hypergraphs 10

Adversarial Attacks on Influence Maximization in Homogeneous...

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10th IEEE Smart World Congress, SWC 2024

作者： Lin, Tianwei Sun, Yanhao Wang, Zeyu Song, Nuan Xie, Kaige Wu, Jie Li, Dong University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Shenzhen Research Institute of Shandong University ShenZhen518000 China Shandong University School of Mechanical Electrical & Information Engineering WeiHai264209 China

ISBN: (纸本)9798331520861

Adversarial strategies targeting influence maximization within homogeneous hypergraphs is an essential area with applications in viral marketing, rumor control, and epidemic prevention. We define the Adversarial Influence Maximization Problem (AdvIMH) and propose a Deep Reinforcement Learning-based algorithm ADV-DRL for selecting attack nodes. Additionally, we introduce a hypergraph embedding algorithm HyperVec to obtain vector representations of hypergraphs. By modeling real-world high-order relationships in datasets such as Music and Restaurant hypergraphs, we demonstrate the effectiveness of ADV-DRL through extensive experiments. Results show that ADV-DRL significantly outperforms baseline algorithms in reducing influence spread, highlighting its potential to identify vulnerable nodes and strategically diminish information propagation in hypergraphs. This work not only broadens the scope of influence maximization applications but also offers an innovative approach for tackling adversarial scenarios in networked systems. © 2024 IEEE.

关键词： Adversarial machine learning

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A Key Distribution Scheme based on TLS 1.3 for IEC 61850-90-5 Synchrophasor Communication

A Key Distribution Scheme based on TLS 1.3 for IEC 61850-90-...

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2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy, PESGRE 2023

作者： Farooq, Shaik Mullapathi Asim Aftab, Mohd Suhail Hussain, S.M. Ustun, Taha Selim School of Computer Science & Engineering Vellore India Dhahran Saudi Arabia Electrical Engineering Department Dhahran Saudi Arabia Fukushima Renewable Energy Institute Koriyama Japan

ISBN: (纸本)9798350310573

With the amalgamation of information and communication technologies the legacy power system networks are evolving as smart grids. Through Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs), operators can monitor and control the smart grid network. Enhanced communication in smart grid results in increased vulnerability to cyber-attacks. Different security mechanisms based on symmetric cryptography are reported in the literature to secure synchrophasor communication against different attacks. These symmetric cryptographic algorithm-based security mechanisms require a mechanism to securely share the secret key. This paper proposes a Key Distribution Scheme (KDS) based on IEC 61850-90-5 standard which authenticates the end communicating entities and to share the secret key based on the latest Transport Layer Security (TLS) 1.3 protocol. Furthermore, the communication is protected with a shared symmetric session key. This paper presents the details of the mechanism for key sharing using the proposed KDS scheme for IEC 61850-90-5 multicast synchrophasor communication. Finally, Analysis on security and performance of the proposed scheme is reported. © 2023 IEEE.

关键词： IEC 61850-90-5 Key Distribution Scheme Phasor Measurement Unit (PMU) communication security Security attacks Smart Grid network

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