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A Comparison of the Key Size and Security Level of the ECC and RSA Algorithms with a Focus on Cloud/Fog computing 1

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7th international conference on Information and Communication technology for Intelligent Systems, ICTIS 2023

作者： Patel, Dhaval Patel, Bimal Vasa, Jalpesh Patel, Mikin Gujarat Changa India

ISBN: (数字)9789819937585

ISBN: (纸本)9789819937578

the rapid development of data generation and demand for real-time processing and analysis in the internet of things (IoT) has led to the emergence of new issues. In response, an extension of cloud computing, known as fog computing, aims to overcome these problems. It aims to bring computation, storage, and network services closer to the end-user by using a distributed computing infrastructure. With the ever-increasing need for secure communication in the digital age, the importance of encryption algorithms cannot be overstated. this study provides an analysis of two commonly used encryption techniques, Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA), with a specific focus on cloud/fog computing. the research compares the key size and security level of the ECC and RSA algorithms and evaluates their suitability for usage in resource-constrained fog computing environments. ECC, a newer technique, offers the same level of security as RSA but uses smaller key sizes, making it more resource-efficient. the research highlights that the choice of encryption technique for cloud/fog computing depends on the system’s specific requirements. the study concludes that ECC could be recommended for improved security and faster performance without putting unnecessary strain on the computing resources. RSA, on the other hand, has been widely accepted and proven its security over the years. the comparison shows that the choice of encryption algorithm for cloud/fog computing depends on the specific requirements of the system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： internet of things

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A Review on Patients Health Monitoring using IoT and Cloud Technologies 7

A Review on Patients Health Monitoring using IoT and Cloud T...

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7th international conference on Parallel, distributed and Grid computing, PDGC 2022

作者： Rani, S. Stella Pavan, T. Hemanth Vanathi, A. Devi, B.S. Kiruthika Aditya Engineering College Department of Cse A.P. Surampalem India Cl Educate Ltd New Delhi India

ISBN: (纸本)9781665454018

At present, the population is increasing day by day. Our traditional methods and human efforts are insufficient to meet the needs of every patient. As every patient needs continuous monitoring of their health status. At present, the observation of a patient's health is very important, and this monitored data is connected to a cloud and can alert the doctor by forwarding a message. Physicians can monitor patients' or elderly people's health conditions from a remote area by using the internet of things (IoT). And also, medical services can be provided to sick people or patients from remote areas based on their daily health status. With the wide range of sensors like pulses, heartbeat, blood pressure, ECG, and temperature sensors along with IoT, such system provides a sophisticated control for continuous monitoring of the health conditions of patients. the goal of this study is to provide a detailed investigation of the IoT health monitoring system that are in the literature. the various existing system is compared and contrasted based on the efficiency and its accuracy. the advantages and disadvantages of the system along with the experimental aspects are available in the comparative results. © 2022 IEEE.

关键词： internet of things

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Securing Cyber Infrastructure of IoT-Based Networks using AI and ML 7

Securing Cyber Infrastructure of IoT-Based Networks using AI...

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7th international conference on Parallel, distributed and Grid computing, PDGC 2022

作者： Mehta, Kapil Sood, Vandana Mohindru Singh, Jashanpreet Sharma, Divyam Chhabra, Pratham Chandigarh Group of Colleges Department of Computer Science and Engineering Landran Punjab Mohali India Chitkara University Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665454018

the internet of things (IoT) has emerged as one of the most advanced technologies, a subject of interest or field of study to researchers, and a sector with promising economic prospects. Numerous gadgets must be connected to one another and people in order for them to work. To handle its data interchange and processing, IoT needs a cloud computing environment. To achieve the automation process in many applications, artificial intelligence (AI) is required to evaluate the data stored in cloud infrastructure and make quick, accurate judgments. IoT paired with Machine Learning (ML) and Deep Learning (DL) models are used to increase the functionality of complicated applications. Cloud vulnerability and IoT device networking remain significant dangers, although AI is presently playing a significant role in enhancing traditional cybersecurity. Huge volumes of data are generated by the rapidly evolving IoT devices and networks in many different ways, which calls for careful authentication and security. One of the most efficacious methods for confronting cybersecurity breaches and encrypting data is artificial intelligence (AI). In this paper, we have investigated the effectiveness of Artificial Intelligence (AI), deep learning (DL) and machine learning (ML) techniques for IoT security. Additionally, this investigation sheds light on the AI roadmap for identifying risks based on attack types. © 2022 IEEE.

关键词： internet of things

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Blockchain Scalability Technologies 7th

Blockchain Scalability Technologies

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Xu, Nengxiang Cai, Jiahong Gong, Yinyan Zhang, Huan Huang, Weihong Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect system. the decentralized characteristics of blockchain have broad application scenarios, such as the internet of things, financial technology and other fields. However, the scalability of the current blockchain is seriously insufficient, such as limited throughput in performance, small storage capacity, and difficulty in scaling functions. this paper introduces the definition and technical classification of scalability, analyzes the current problems of scalability and briefly introduces the current main-stream expandable technologies such as sharding, on-chain and off-chain storage, off-chain payment channel and cross-chain technology from two aspects of performance and function, as well as the principles and ideas of these technologies. Finally, the research progress of current blockchain extension technology is summarized, and the problems faced by the current extension scheme are pointed out, which provides a direction for future research work. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications 7

A Model-Based Machine Learning Approach for Assessing the Pe...

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7th IEEE international conference on Smart internet of things, SmartIoT 2023

作者： Albshri, Adel Alzubaidi, Ali Solaiman, Ellis Newcastle University School of Computing United Kingdom University of Jeddah College of Computer Science and Engineering Saudi Arabia Umm Al-Qura University College of Computing Saudi Arabia

ISBN: (纸本)9798350316575

the recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a k nearest neighbour (k\text{NN}) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations;despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. the k\text{NN} model outperforms SVM by 5% and the ISO also demonstrates a reduction of 4% inaccuracy deviation compared to regular SO. © 2023 IEEE.

关键词： Blockchain

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Deep Learning Dependent DDOS Attack Sensing in Environment of Cloud computing 7

Deep Learning Dependent DDOS Attack Sensing in Environment o...

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Bhargava, R. Singh, Yash Pal Narawade, Navanath S Kalinga University Raipur India Parvatibai Genba Moze College of Engineering Pune India

ISBN: (纸本)9781665421683

By presenting virtualized with high-demand assets to end applicant having highest dynamicity, lower maintenance efforts, measurable, and decreased finance of infrastructure, Cloud computing became a revolutionary concept in IT technology. Such resources were controlled through various management companies and given across internet utilizing well knowledgeable rules of networking, formats and standards. Underlying schemes and pre-existing protocols that contains vulnerabilities and bugs which can assist in creating as situation where intrusion become less complicated by adversaries. DDoS (distributed Denial of Service) kinds of cyber assualt remains as ones of frequently occurring issues which can inflict complicated hazards and influencing performance of clouds. With respect to attack such as DDoS, Adversaries commonly applies unknowingly compromises computers (known 'zombies') through extracting benefits of unknown or known vulnerabilities and bugs in sending huge amount of chunks from such already-trapped zombies towards a server systems. these conditions will definitely consumes a large chunk of network bandwidth for communication of victim's cloud structures or in consuming large time span of servers. Hence, with the concerned research thesis, we calibrated and designed a DDoS sensing mechanism depending on suggested algorithm in replicating DDoS hazard. Such algorithm, combined with signature sensing methods, produces a decision tree in executing autonomous, significant detections of recurring pattern of attacks pertaining to DDoS flooding cyber attack. In validating our system under consideration, we have chosen other techniques of machine learning and relating to generated outcomes. © 2022 IEEE.

关键词： Denial-of-service attack

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Research on the Deployment of MEC Edge computing Platform Based on 5G Network in Power System 7

Research on the Deployment of MEC Edge Computing Platform Ba...

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7th international conference on Computer and Information Processing technology, ISCIPT 2022

作者： Wang, Long Dong, Wu Zhang, Guoyi Wang, Tao Liu, Xu China Southern Power Grid Digital Power Grid Research Institute Co. Ltd 510670 China Guizhou Power Grid Power Dispatching and Control Center 550002 China CSG Power Dispatching and Control Center 510670 China

With the rapid development of cloud computing, internet of things, AI and other technologies, edge computing technology has also developed rapidly. To be on the safe side, especially those mid-sized, larger utilities are more inclined to use edge computing. To this end, this paper conducts an in-depth discussion on the application of the MEC edge computing platform on 5G networks. A distributed power architecture based on distributed computing is proposed, and the distributed application of MEC is studied from two aspects: distributed application model and distributed service carrier. Experiments on the effect of deploying the live network pilot show that: the power system delay of the platform in the comparison case, the power system delay of the local forwarding platform is shortened by 50%, the deployment effect is faster, the delay is shorter, and the efficiency is higher. © 2022 Copyright for this paper by its authors.

关键词： 5G mobile communication systems

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Migrating Deep Learning Data and Applications among Kubernetes Edge Nodes 23

Migrating Deep Learning Data and Applications among Kubernet...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Mangkhangcharoen, Suchanat Haga, Jason Rattanatamrong, Prapaporn Thammasat University Department of Computer Science Pathum Thani Thailand National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (纸本)9781665494571

Many current IoT applications deployed at the edge use deep learning (DL) in their real-time processing and analytics. Not only inference but also training is moving to edge devices. DL application and dataset migration among these devices are mandatory for scenarios like node failure, user mobility or when nodes need to collaborate (e.g., distributed training). Container technologies and Kubernetes (K8s) are being increasingly adopted to manage infrastructure at the edge. Unfortunately, there is no built-in mechanism in K8s to support migration of stateful containers between its cluster nodes. the K8s cluster's master node generally launches a new fresh container in another node to replace the failed one. While there is an existing mechanism for migrating a Pod between K8s nodes, there is no past work investigating the migration of DL datasets and containerized DL applications among K8s cluster nodes. In this paper, we present our 1) comprehensive study on the effectiveness and limitations of existing checkpointing mechanisms for containerized DL applications and 2) our comparative performance study of several approaches in migrating DL datasets and applications in a K8s cluster. Our results show that migrating states of DL applications and restoring them from their previous states enables faster recovery (reducing training time by 10 to 73 percent) than re-running these models from the beginning regardless of the percentage of epochs that have completed. Additionally, our experimental results show that transferring a dataset between K8s workers using the K8s persistent volume with kubectl cp is generally suitable and efficient. However, when network latency is high, using our customized middleware with a feedback controller to migrate data in parallel can speed up total migration time compared to the K8s's persistent volume approach alone. © 2021 IEEE.

关键词： Training Deep learning Checkpointing Computers Containers Real-time systems internet of things

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A review of IoT architectures in smart healthcare applications 7

A review of IoT architectures in smart healthcare applicatio...

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7th international conference on Fog and Mobile Edge computing (FMEC)

作者： Arbaoui, Meriem Brahmia, Mohamed-El-Amine Rahmoun, Abdellatif Cole Super Informat LabRi SBA Lab Sidi Bel Abbes Algeria CESI Engn Sch CESI LINEACT UR 7527 Strasbourg France

ISBN: (纸本)9798350334524

the internet of things (IoT) is revolutionizing numerous industries, including healthcare services, known as the internet of Medical things (IoMT). A large amount of generated data in IoMT applications need to be transmitted, analyzed, and stored. Consequently, the cloud-only architecture was proposed as being the best-fit organizational infrastructure. Indeed, cloud capabilities of processing, networking, and storage are overwhelming properties that make it outperform classical solutions for decades when it comes to healthcare applications. Nevertheless, this architecture could not keep up with the evergrowing amount of biomedical data. One of the main drawbacks of cloud architecture is the large latency, which prevents it from delivering real-time alerts to save the patient's life in critical situations. In this context, edge and fog computing become good alternatives to reduce health data management complexity and latency and therefore increase their reliability. this paper proposes a review of the most recent healthcare applications based on edge and fog computing, respectively. the selected research works of this survey are classified into four categories depending on their use case application. Furthermore, a comparison study helps extract relevant insights from several recent papers in the current literature to highlight their methodologies and purposes. the main concern is to emphasize what features and properties are to be considered most when designing a distributed healthcare system in an edge-fog architecture. Finally, we present some challenges to be addressed in healthcare applications along with the uprising of new technologies.

关键词： Healthcare internet of things (IoT) internet of Medical things (IoMT) architecture cloud computing edge computing fog computing

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

the incorporation of edge computing has become essential in the big data and internet of things age to tackle issues related to latency, real-time processing, and data scale. this paper delves into the imperative need for edge computing within the context of big data environments. distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. the research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. the outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. this research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. the DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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