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Successful Integration of IoT and Blockchain technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE international conference on computing, Power, and Communication technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

internet of things (IoT) produces massive amounts of data that need to be processed and saved securely. the strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. the MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. the MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. the conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. these inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. the results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Performance Comparison of Sliding Free-standing Mode Triboelectric Nanogenerator (TENG) Based Sensors for IoT applications 5

Performance Comparison of Sliding Free-standing Mode Triboel...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Chithra, S. Shajahan, E.S. Zacharias, Joseph ISRO Liquid Propulsion Systems Centre Kerala Trivandrum India Govt. Engineering College Department of Applied Electronics and Instrumentation Engineering Kerala Kozhikode India College of Engineering Department of Electronics and Communication Engineering Kerala Trivandrum India

ISBN: (纸本)9798350334128

the internet of things (IoT) is a ground-breaking technology that aims to connect anything from anywhere at any time. the IoT has been influencing our lifestyle in many ways from home appliances that we can control with our smartphones to self-driving smart cars finding best travel routes based on traffic data. With the rapid development of IoT and sensor technologies, there is a huge demand for sensors with inbuilt power sources and human-machine interfaces (HMIs). Triboelectric Nanogenerator also known as TENG, is a novel device that converts mechanical energy into electrical energy by utilizing the energy wasted in our ambient environment thus eliminating the need for costly power supplies required to power the sensors. TENG can also act as a human-machine interface for supervising human-machine interactions. this paper presents the performance comparison of simple & low-cost TENG-based sensors for IoT applications. the paper evaluates the effectiveness of the freestanding mode TENG-based sensor towards the internet of things (IoT) applications. Experiments are conducted to show that TENG-based sensors are capable of generating signals for wireless control applications. © 2023 IEEE.

关键词： internet of things

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FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments 19

FLARE: Detection and Mitigation of Concept Drift for Federat...

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19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Chow, theo Raza, Usman Mavromatis, Ioannis Khan, Aftab Toshiba Europe Ltd Bristol Res & Innovat Lab Bristol Avon England Kings Coll London London England Waymap Ltd London England

ISBN: (纸本)9798350333398

Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is transmitted to a central server for training and inference purposes. On the other hand, Federated Learning migrates both tasks closer to the edge nodes and endpoints. this allows for a significant reduction in data exchange while preserving the privacy of users. Trained models, though, may under-perform in dynamic environments due to changes in the data distribution, affecting the model's ability to infer accurately;this is referred to as concept drift. Such drift may also be adversarial in nature. therefore, it is of paramount importance to detect such behaviours promptly. In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model's training behaviour and inference statistics, respectively. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods (over 5x reduction), is easily scalable to larger systems, and can successfully detect concept drift reactively with at least a 16x reduction in latency.

关键词： Federated Learning distributed deployment Concept Drift Model Robustness Scalable IoT Inference

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Content-Based Recommender System using Word Embeddings for Pedagogical Resources 5

Content-Based Recommender System using Word Embeddings for P...

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5th international conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Mediani, Chahrazed Harous, Saad Djoudi, Mahieddine Ferhat Abbas Setif-1-University LRSD Laboratory Computer Science Department Setif19000 Algeria University of Sharjah College of Computing and Informatics Computer Science Department Sharjah27272 United Arab Emirates TECHNE Universite de Poitiers Poitiers86073 France

ISBN: (纸本)9798350381450

Recommender Systems are important systems operating within a system to ensure how certain types of data are managed on the internet. these systems help users with overwhelming data and provide a better user navigation experience. this paper presents a content-based recommender system for online resources using deep learning. We have included some deep learning techniques to allow a good semantic understanding of educational resources. However, we have used a pre-Trained word2vec model owned by Google for the following three reasons: (1) Google is reliable;(2) the content of the Google news dataset is close to the content of the shared articles dataset;and (3) training a word2vec model is time-consuming and domain-independent itself. We have also used techniques for dimensionality reduction like t-distributed Stochastic Neighbor Embedding and Principal Component Analysis to reduce the dimensions of users and items vectors. Our approach aims to ameliorate the recommendations' accuracy and better satisfy the requirements of users. the results obtained when we tested our system are encouraging. © 2023 IEEE.

关键词： Recommender systems

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Securing Digital Integrity: Proposed Comprehensive Framework for Deepfake Detection and Blockchain Validation 5th

Securing Digital Integrity: Proposed Comprehensive Framework...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Jain, Anant Gaur, Adamya Gupta, Gauranshi Mishra, Shubhangi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819773701

this paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural Networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of Deepfake manipulation, while Blockchain ensures content integrity through timestamping and IPFS hashes. Leveraging transfer learning with EfficientNet-B1 architecture and dropout layers to prevent overfitting, the CNN model attains a 98.53% accuracy on testing data. Smart contracts are utilized to store social media content and AI verification results. the methodology integrates private storage on social media platforms, AI-based CNN verification, Blockchain timestamping, smart contract verification, and continuous improvement. By synergizing AI and Blockchain technologies, our approach aims to bolster defenses against deceptive content proliferation, enhancing the reliability of online media ecosystems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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IoT Based Alcohol Detection and Vehicle Control System 5

IoT Based Alcohol Detection and Vehicle Control System

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sapthami, I. Raju, V. V. Narasimha Vaithianathan, V. Chinnasamy, P. Kumaran, G. Ayyasamy, Ramesh Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad500043 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Kalavakkam Chennai603110 India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering TamilNadu Srivilliputtur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350389609

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the internet of things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. this study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. the system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. this sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates. © 2024 IEEE.

关键词： Highway accidents

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Resource Allocation Methods among Server Clusters in a Resource Permeating distributed computing Platform for 5G Networks 47

Resource Allocation Methods among Server Clusters in a Resou...

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47th IEEE-Computer-Society Annual international conference on Computers, Software, and Applications (COMPSAC)

作者： Sasaki, Daisuke Kashiwazaki, Hiroki Osaki, Mitsuhiro Nishiuchi, Kazuma Nakagawa, Ikuo Kikuchi, Shunsuke Kikuchi, Yutaka Hosoai, Shintaro Takase, Hideki Univ Tokyo Tokyo Japan Kindai Univ Osaka Japan Citynet Inc Kochi Japan Osaka Univ Osaka Japan SAKURA Internet Inc Tokyo Japan Kochi Univ Technol Kochi Japan

ISBN: (纸本)9798350326970

With the spread and development of 5G technology, network configurations with MEC (Multi-Access Edge computing) servers in the vicinity of 5G base stations are becoming more common. We have been researching and developing Giocci, a resource permeating distributed processing platform that offloads computation tasks on end devices to MEC servers and cloud servers. In this paper, we propose resource allocation methods to efficiently determine the server to which tasks are allocated in a network configuration that includes MEC servers. In order to construct the proposed methods, we first model the main functions of Giocci and define the resource allocation problem for this work. there are four proposed methods according to each objective and priority;a prioritized allocation by the average number of waiting tasks, by the communication delay, by the task response time, and by the cost of using computing resources. We initially implement these methods assuming that they are task allocation functions in Giocci. Experimental evaluations demonstrate that they could achieve appropriate resource allocation results for each objective. this research contributes to the smooth allocation of computational resources in 5G networks including MEC servers.

关键词： Resource Allocation Problem 5G MEC Server IoT

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Inferring Autoscale Information from NFV MANO for Launching Attacks - An Experimental Study with Cloudified 5G 5

Inferring Autoscale Information from NFV MANO for Launching ...

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5th conference on Cloud and internet of things (CIoT)

作者： Pc, Amogh Divakaran, Dinil Mon Gurusamy, Mohan Natl Univ Singapore Singapore Singapore Trustwave Chicago IL USA

ISBN: (纸本)9781665479646

Network function virtualization, softwaredefined networking, and cloud computing are the key technologies that enable dynamic, resource-efficient service provisioning in 5G networks. Auto-scaling mechanisms are an essential factor for efficient resource utilization and improved quality of experience in such networks. Autoscaling is also a defence against attacks such as distributed Denial of Service (DDoS) but with a price. Service providers can use different European Telecommunications Standards Institute (ETSI) stack-based Network functions virtualization (NFV) management and orchestration platforms for autoscaling and dynamic resource provisioning. However, such platforms pose a risk of side channel information leak between the orchestrator and cloud. Tapping this side channel information, an adversary can infer the autoscaling policy, scale-up/down, and cloud platform in order to launch an attack. We emulate a scalable 5G network OpenAirInterface5G bundle using Juju as a service. Using this open-source realistic 5G emulator testbed, we carry out experiments with various cloud platforms such as Openstack, Amazon Web Services (AWS), and Microsoft Azure clouds. We use MIT tale of many cities spatiotemporal load patterns that traces mobile traffic as dataset for our experiments. We show how an adversary could infer the resource scalability and type of cloud platform by analyzing as simple information as packet flow between the orchestration platform and the cloud. We use the opensource tool Zeek for intrusion detection and showed its effectiveness in detecting volume based DDoS attack in 5G. In order to evade the intrusion detection mechanism, we propose an algorithm and demonstrate a way to intelligently craft the DDoS attack with User Equipment (UE) bots.

关键词： Cloud computing 5G mobile communication Web services ETSI Urban areas Intrusion detection Denial-of-service attack

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24th international conference on Parallel and distributed computing, Applications and technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

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24th international conference on Parallel and distributed computing, Applications and technologies, PDCAT 2023

ISBN: (纸本)9789819982103

the proceedings contain 28 papers. the special focus in this conference is on Parallel and distributed computing, Applications and technologies. the topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for Software-Defined Satellite Networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing Systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in Optical Circuit Switched Networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless Sensor Networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;the Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

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Exploring IoT-Applications: A Survey of Recent Progress, Challenges, and Impact of AI, Blockchain, and Disruptive technologies 7

Exploring IoT-Applications: A Survey of Recent Progress, Cha...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Mahamuni, Chaitanya Vijaykumar Agnel Charities' Father Conceicao Rodrigues Institute of Technology Department of Computer Engineering Maharashtra Navi Mumbai India

ISBN: (纸本)9798350340600

the internet of things (IoT) has become a common part of our daily lives. It refers to a network of physical objects, such as home appliances and security systems, that can easily connect to the internet. By utilizing the internet, data storage, analytics, and sensors, IoT improves our daily needs and offers convenience in our fast-paced modern world. Alongside artificial intelligence, machine learning, and cloud computing, IoT has been a major trend in high-tech. It has great potential in areas like Industry 4.0, Smart Cities, and driverless cars. Advancements in technologies like artificial intelligence, blockchain, 5G, edge AI, and edge computing are driving new trends in IoT. this research paper presents a comprehensive survey of the latest applications of IoT, focusing on cutting-edge developments. the paper also explores the potential of incorporating disruptive technologies like artificial intelligence and blockchain to enhance IoT applications. It covers various aspects related to IoT application areas, including recent developments, significant challenges, and difficulties. © 2023 IEEE.

关键词： Blockchain

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