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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Sneha Seth, Jyoti Hannah Jessie Rani, R. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect computing is a rising location of allotted computing that methods facts toward the cease person or tool, supplying low-latency responses to operations and analytics. It enables quicker and more dependable facts-extensive applications that can be personalized to consumer alternatives and needs. This paper discusses the design and implementation of real-time edge computing offerings for IoT-primarily based cloud networks, focusing on key components of the device structure, including carrier deployment, aid management, protection, and scalability. Furthermore, the paper describes the usage of area computing for IoT programs, which include video surveillance, domestic automation, and smart town solutions. Subsequently, the paper presents a survey of cutting-edge area computing companies, summarizing their offerings, business models, and services. © 2024 IEEE.

关键词： Analytics Applications Automation Business Models Computing Real-Time Services

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Multi-Agent Distributed DQN and Transfer Learning for Energy-Efficient Power Management in Solar Energy-Harvested Small-Cell Networks

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IEEE Internet of Things Journal 2025年第12期12卷 19590-19604页

作者： Cho, Hyebin Kim, Hyungsub Na, JeeHyeon Lim, Seung-Chan Lee, Howon Hankyong National University School of Electrical Electronic and Control Engineering Anseong17579 Korea Republic of Electronics and Telecommunications Research Institute Daejeon34141 Korea Republic of Hankyong National University School of ICT Robotics and Mechanical Engineering and IITC Anseong17579 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

The integration of solar energy harvesting into small-cell networks is a promising solution for achieving energy-efficient and sustainable wireless communications. However, the inherent variability and intermittency of solar energy, coupled with precise inter-cell interference management, significantly hinder efficient network operation. To resolve these challenges, we propose a multi-agent distributed deep Q-network (MA-DDQN) framework, where the distributed base stations learn the optimal transmit power control policies. To further enhance adaptability under varying solar conditions, we present a daily model transfer with a fine-tuning approach, enabling efficient deployment without extensive training overhead. Simulation results demonstrate that the proposed methods remarkably improve energy efficiency while maintaining robust adaptability under dynamic solar conditions, revealing their potential for sustainable small-cell network deployments. © 2014 IEEE.

关键词： Solar cell efficiency

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A Wireless and Battery-Free Wearable Pressure Sensing System for Human-Machine Interaction and Health Monitoring

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible electronics 2023年第6期2卷 439-447页

作者： Lee, Sang Won Pei, Xiaochang Rajendran, Jerome Esfandyarpour, Rahim University of California at Irvine Henry Samueli School of Engineering Department of Electrical Engineering and Computer Science Laboratory for Integrated Nano Bio Electronics Innovation IrvineCA92697 United States

Wearable devices have received tremendous interest in healthcare monitoring due to their significant advantages in terms of flexibility, noninvasiveness, wireless capability, and portability. However, they still face challenges, including the need for external power sources, bulky measuring instruments, and a lack of a wireless data transport system. Herein, we propose a PEDOT:PSS/MXene pressure sensing platform based on 2-D e-textile and screen-printed interdigitated electrodes (IDEs). Both materials improve the electrical conductivity, sensitivity, stability, flexibility, and mechanical durability of the pressure sensor. A near-field communication (NFC) readout system provides real-time wireless monitoring by transmitting signals from the sensor to a smartphone app, without external batteries. The pressure sensor has sensitivity, a wide detection range, excellent repeatability and stability, fast response/recovery time, and long-term durability. Long-term usability was verified through the bending test up to 21400 cycles. The flexible pressure sensor successfully monitored wrist pulse, physical movements, and various muscle movements in the body. Repeatable, stable, reliable, and regular signals were obtained wirelessly through the NFC readout system. Our sensing platform could be a promising solution for healthcare monitoring, rehabilitation therapy, and early medical diagnosis. In addition, it suggests a new platform for helping people with disabilities to communicate with others through human-machine interaction. © 2022 IEEE.

关键词： Pressure sensors

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Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT

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computer Systems Science & engineering 2023年第4期45卷 69-84页

作者： Ceren Baştemur Kaya Ebubekir Kaya Göksel Gökkuş Department of Computer Technologies Nevsehir Vocational CollegeNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey Department of Computer Engineering Faculty of Engineering and ArchitectureNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitectureNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey

It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)*** or soft computing methods are used for *** soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this *** study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for *** meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these *** data of a 250 W photovoltaic(PV)is used in the *** effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in *** training algorithms are compared in terms of solution quality and convergence *** strengths and weaknesses of these algorithms are *** is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training *** a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.

关键词： Optimization meta-heuristic algorithm neuro-fuzzy MPPT photovoltaic system

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Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

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computer Systems Science & engineering 2023年第9期46卷 3387-3401页

作者： A.L.Amutha R.Annie Uthra J.Preetha Roselyn R.Golda Brunet Department of Computational Intelligence SRM Institute of Science and TechnologyChennai603203India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India Department of Computer Science and Engineering Government College of EngineeringSalem636011India

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make *** are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power *** can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal *** normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power *** a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this *** proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the *** significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the *** proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

关键词： Smart Grid PMU data incremental learning classifying anomalies artificial intelligence

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Optimized Scheduling of Electric Vehicles Charging in Smart Grid using Deep Learning 4

Optimized Scheduling of Electric Vehicles Charging in Smart ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Joshua, K. Paul Ranga, Jarabala Prasad, P. Venkata Mallala, Balasubbareddy Rajendiran, M. Maranan, Ramya SCAD College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Ramachandra College of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Eluru India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This research presents an integrated research framework for electric vehicle charging infrastructures under smart grids. It employs Genetic Algorithms and machine learning models such as Artificial Neural Networks (ANNs), Decision Trees, and Recurrent Neural Networks. The GA is able to minimally stack waiting times strategically and, by the effective allocation of charging points to drivers at various stages in their route, maximizes usage. In contrast, requiring intensive accurate training on massive data sets are the ML methods, which can also predict future demand for efficient use of resources. Results of the method demonstrate the strength of these techniques, with ANNs reaching nearly 99% accuracy. By applying this comprehensive methodology, operational metrics such as waiting time for EV owners are significantly improved and sustainable power management becomes more feasible. These findings underscore the potential for artificial intelligence to revolutionize energy distribution-so that it balances consumer convenience with tighter environmental targets and ways of working even now taking shape concerning electric vehicle charging. © 2024 IEEE.

关键词： Smart power grids

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Intrusion Detection System for Big Data Analytics in IoT Environment

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computer Systems Science & engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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Privacy, Security, Trust and Applications in Internet of Things 6

Privacy, Security, Trust and Applications in Internet of Thi...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaklaf, Sliman K.A.A. Elmezughi, Abdurrezagh S. Naas, Salah Mohamed H. Ekreem, Nasser Bashir Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Azzaytuna University Faculty of Engineering Computer Engineering Department Tarhuna Libya

ISBN: (纸本)9798350321029

Internet of Things (IoT) is a network to connect anything with the Internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, security and privacy has emerged as a significant challenge for the IoT. Within the available Internet infrastructure, IoT devices collecting data through wireless technology. Now Days, IoT is research area with many devices are connected through internet. IoT concept brings a new challenge concerning privacy, trust, security, and reliability. To build smart systems, IoT technology has become important technology for people use. In this paper, IoT technologies, applications and characteristics will be briefly discussed. This paper gives a complete image of IoT security issues and its applications. The platform is required to secure exchange between consumers of their information and IoT devices. © 2023 IEEE.

关键词： Internet of things

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Color-image encryption scheme based on channel fusion and spherical diffraction

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Chinese Physics B 2022年第3期31卷 276-284页

作者： Jun Wang Yuan-Xi Zhang Fan Wang Ren-Jie Ni Yu-Heng Hu Sichuan University School of Electronics and Information EngineeringChengdu 610065China Department of Electrical and Computer Engineering University of Wisconsin-MadisonMadisonWIS3706USA

A secure encryption scheme for color images based on channel fusion and spherical diffraction is proposed in this paper. In the proposed encryption scheme, a channel fusion technology based on the discrete wavelet transformation is used to transform color images into single-channel grayscale images, firstly. In the process of transformation, the hyperchaotic system is used to permutate and diffuse the information of red–green–blue(RGB) channels to reduce the correlation of channels. Then the fused image is encrypted by spherical diffraction transform. Finally, the complex-valued diffraction result is decomposed into two real parts by the improved equal module decomposition, which are the ciphertext and the private key. Compared with the traditional color image encryption schemes that encrypt RGB channels separately, the proposed scheme is highly secure and robust.

关键词： channel fusion hyper-chaotic system asymmetric encryption spherical diffraction

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Remote Intelligent System for Monitoring and Control of Water Distribution Network Using Remote I/O Module for Smart City

Remote Intelligent System for Monitoring and Control of Wate...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Raj, C. Mohan Babu, W. Rajan Gomathi, V. Bharath, S. Jagatap, Govind Vishwanath Kumar, S. Siva Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sns College of Technology Department of Electrical and Electronics Engineering Coimbatore India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665474511

The need for drinking water is very essential in today's world. The government has been adopting a variety of measures to save drinking water and spend it economically. It is everyone's duty to save water and use it sparingly. Water is wasted due to breakage of the water pipe, excessive use of water, measurement in a faulty instrument and so on. The leakage of water from the pipeline and the usage of water is intelligently detected and measured by PLC - Remote I/O Module based system. Each house or utility is installed with intelligent flow meters and the inputs or readings are collected from the individual meters by water distribution board via the iOT. Faults and other abnormal conditions are identified instantaneously, and the health of the distribution system is monitored by SCADA. © 2023 IEEE.

关键词： Potable water

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