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Energy-aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Software - Practice and Experience 2024年第2期54卷 109-148页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Department of Computer Engineering Qeshm Branch Islamic Azad university Qeshm Iran Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan Iran Research and Innovation Department Petanux GmbH Bonn Germany Zanjan Iran

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location-aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. © 2023 John Wiley & Sons Ltd.

关键词： Quality of service

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The Impact of SMS Phishing using Machine Learning Classifiers with Innovative Techniques

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Procedia computer science 2025年 260卷 608-615页

作者： Anisha Asirvatham C. Meenakshi Research Scholar Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Associate Professor Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

These days, machine learning has an amazing ability to present the results efficiently. Network technology computing systems have long served as a critical framework to supply machine learning with deterministic computing resources. This technique can be advantageous to networking. The application of machine learning in detection of phishing of SMS in a network is the main topic of this article. This not only stimulates new network applications but also aids in the resolution of certain obsolete network issues. The fundamental approach to describe how machine learning technology works in network model is summarized in this article. The SMS spam can be detected using the proposed system, where the feature extraction can be done using stemming and the different machine learning classifiers are used to evaluate the accuracy of the proposed system. The different machine learning classifiers used in this system are Logistic regression, linear regression, K Nearest Neighbors and Naïve Bayes. The new system SpamSMS is the ensemble method using Naïve Bayes and Stacking Algorithm which gives the maximum accuracy towards the provided dataset to check for the spam messages.

关键词： Network Intrusion Detection Neural Network Artificial Intelligence computer Network Machine learning classifiers

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Vulnerability Analysis and Exploitation Attacks on Smart Wearable Devices 2

Vulnerability Analysis and Exploitation Attacks on Smart Wea...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Khan, Shujahat Ali Bajwa, Hasan Raza Sundaram, Jawahar Pritika Shanmugam, Bharanidharan Energy and Resources Institute Faculty of Science and Technology Charles Darwin University Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India

ISBN: (纸本)9798350371314

Wearable devices are becoming more popular these days due to its unique features and its effectiveness. The evolution of Internet of Things (IoT) that combined health care devices and sensors has entirely changed the scenario in the healthcare infrastructure. This innovation will confidently improve the existing healthcare facilities and practices due to the continuous observing of patient's health even in the remote places. Regardless of their effectiveness, IoT devices are insecure and are vulnerable to security threats that should need to be overcome. The main issue is to protect from outside entities or attacks that may obstruct the advancement and put the individual's information at risk. This study focuses on outlining the vulnerabilities focusing on smartwatches in the healthcare IoT domain. Furthermore, it has been observed that a limited Bluetooth Low Energy (BLE) based smartwatches has a very weak or no security defense system making these devices vulnerable to various security threats such as Brute-force attacks, Man-in-The-Middle (MiTM) attacks, Denial of Service (DoS) attacks, and Phishing. Experimental testing has been performed to test the vulnerabilities in smartwatch devices to verify the data integrity concerns. By using Bluetooth sniffing tools, the security of the smart wearable devices has been tested to identify its weaknesses and to target the BLE devices. Attempts have been made to tamper its parameters by gaining access to the devices through Bluetooth connection. The overall result reveals that these devices are vulnerable to different exploits. This study provides the foundation to assess possible vulnerabilities in smart wearable devices. Furthermore, it highlights the loopholes existed in these devices which can be exploited. © 2024 IEEE.

关键词： Bluetooth

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Social Argumentation Systems

Journal of Applied Logics

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Journal of Applied Logics 2025年第3期12卷 251-294页

作者： Bikakis, Antonis Flouris, Giorgos Leite, Joao Patkos, Theodore Department of Information Studies University College London United Kingdom Institute of Computer Science FORTH Heraklion Greece NOVA LINCS NOVA University Lisbon Portugal

While a lot of research has been conducted on understanding and formalising the interplay of arguments within the context of Computational Argumentation, this research is not fully applicable to the types of arguments that populate the Social Web. In that context, arguments usually have the form of comments, opinions or reviews, and are the main ingredients of online discussion forums, social networks, online rating and review sites, debate portals and other online communities-the electronic version of word-of-mouth communication. As a result, voting and other forms of reaction to the provided comments or arguments (other than just "attacks") are allowed, features that are not normally considered in the classical literature on Computational Argumentation. In this chapter, we study extensions of argumentation frameworks that have been proposed to describe and understand the more complex types of interactions among arguments that can be found in the Social Web, and present the current state-of-the-art, as well as open problems. © 2025, College Publications. All rights reserved.

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Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

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computer Systems science & Engineering 2023年第4期45卷 701-714页

作者： Helen Josephine V L Ramchand Vedaiyan V.M.Arul Xavier Joy Winston J A.Jegatheesan D.Lakshmi Joshua Samuel Raj Business Analytics School of Business and ManagementChrist UniversityBangalore560029India Department of Computer Science and IT School of SciencesJain Deemed to be UniversityBangalore560027India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India College of Computer Studies University of Technology Bahrain18041Bahrain Institute of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123India Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India

The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum *** essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further *** dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the *** paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy *** proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy *** accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy ***,for improving the performance of the SOC estimation process,the HBFOA is *** addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall *** ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several *** experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

关键词： Internet of things energy systems state of charge estimation machine learning deep learning metaheuristics

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Review: Recent advancements on Artificial Intelligence

Review: Recent advancements on Artificial Intelligence

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Singh, Meeta Chahal, Poonam Bura, Deepa Srishty Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9781032669663

In the upcoming era, enhanced machines will be used as an alternate or it will increase human capabilities in each and every field. Artificial intelligence can be explained as enhanced features exhibited by software or machines. Artificial intelligence is being used by humans to enhance the technology or their reach. Artificial intelligence got the boom in the last two decades in its performance as being highly used in manufacturing and service systems. Multiple national and international research agencies are working hard in the area of artificial intelligence which has helped in rapid growth of a technology which is commonly known as expert system. AI will have to face some challenges like overcoming fears, creating public trust that are needed to be addressed and handled. This technology has increased the quality and efficiency of the employees working in these departments. © 2024 The Author(s).

关键词： Artificial intelligence

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Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

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Digital Communications and Networks 2024年第1期10卷 38-44页

作者： Mianjie Li Senfeng Lai Jiao Wang Zhihong Tian Nadra Guizani Xiaojiang Du Chun Shan School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhou510665China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Schoolof Electrical Engineering and Computer Science Washington State UniversityPullmanWA99164USA Department of Computer and Information Science Temple UniversityPhiladelphiaPA19122USA

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication *** that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of *** multiwatermarking process employs spread transform dither *** the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading ***,multiple watermarks can be simultaneously embedded into the same position of a multimedia ***,the multiple watermarks can be extracted without affecting one another during the extraction *** analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

关键词： B5G Multimedia information security Spread transform dither modulation Spreading vector measurement Unit maximum capacity

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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