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BEI-TAB: Enabling Secure and distributed Airport Baggage Tracking with Hybrid Blockchain-Edge System 21

BEI-TAB: Enabling Secure and Distributed Airport Baggage Tra...

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21st IEEE international conference on Communication technology, ICCT 2021

作者： Wang, Fei Si, Pengbo Sun, Enchang Su, Yuzhao Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9781665432061

Global air transport carries about 7.3 billion pieces of luggage each year and up to 56 percent of travelers prefer obtaining real-time baggage tracking information throughout their trip. However, the traditional baggage tracking scheme is generally based on optical scanning and centralized storage systems, which suffers from low efficiency and information leakage. In this paper, a blockchain and edge computing based IOT system for tracking of airport baggage (BEI-TAB) is proposed. Through the combination of radio frequency identification technology (RFID) and blockchain, real-time baggage processing information is automatically stored in blockchain. In addition, we deploy Interplanetary File System (IPFS) at edge nodes with ciphertext policy attribute-based encryption (CP-ABE) to store basic baggage information. Only hash values returned by the IPFS network are kept in blockchain, enhancing the scalability of the system. Furthermore, a multi-channel scheme is designed to realize the physical isolation of data, and to rapidly process multiple types of data and business requirements in parallel. To the best of our knowledge, it is the first architecture that integrates RFID, IPFS, CP-ABE with blockchain technologies to facilitate secure, decentralized, and real-time characteristics for storing and sharing data for baggage tracking. We have deployed a testbed with both software and hardware to evaluate the proposed system, considering the performances of transaction processing time and speed. © 2021 IEEE.

关键词： Blockchain

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Internet of Things Security: Requirements, Attacks on SH-IoT Platform

Internet of Things Security: Requirements, Attacks on SH-IoT...

引用

international conference on Sciences and Techniques of Automatic Control and Computer Engineering (stA)

作者： Mona A. El. zuway Hend M. Farkash College of Electrical and Electronics Technology-Benghazi Benghazi Libya

ISBN: (纸本)9781665482622

Smart building security systems typically consist of sensors and controllers that monitor power operating systems, alarms, camera monitoring, access controls, and many other important information and security systems. These systems are managed and controlled through online platforms. A successful attack on one of these platforms may result in the failure of one or more critical intelligent systems in the building. In this paper, the security requirements in the application layer of any IoT system were discussed, in particular the role of IoT platforms in dealing with the security problems that smart buildings are exposed to and the extent of their strength to reduce the attacks they are exposed to, where an experimental platform was designed to test the presence of security vulnerabilities and This was done by using the Zed Attack Proxy (ZAP) tool, according to the OWASP standards and security level assessment, and the importance of this paper comes as a contribution to providing information about the most famous IoT platforms and stimulating work to explore security concerns in IoT-based platforms.

关键词： Cloud computing Smart buildings Reliability engineering Sensor systems Security Internet of Things Smart devices

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A multi agent broker framework and decision support for enhanced cloud computing discovery 21

A multi agent broker framework and decision support for enha...

引用

21st international Arab conference on Information technology, ACIT 2020

作者： Hafez Rady, Mohamed Galal Elgamel, Mohamed Shaheen Badawy, Osama Mohamed College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9781728188553

It is remarkable that the tremendous expansion in the world of cloud computing in recent times, and in light of the Covid 19 pandemic that hit the entire world, the challenge has become more difficult and the intense focus on moving to the world of cloud computing is a necessity not a luxury. Hence, the biggest and most difficult challenge from both sides emerged. Therefore, this study attempts to provide an intelligent search scope to meet the clients' needs in finding the best service provider for their financial capabilities at the right time. It also gives a greater opportunity for service providers to display their services more clearly, by introduce multi-agent framework with decision support approach and recommendation concept, also a proposed cloud service discovery testbed introduced with management tools and an efficient $GUI$ dashboard. © 2020 IEEE.

关键词： Quality of service

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Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs 18

Cloud-assisted Road Condition Monitoring with Privacy Protec...

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18th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Da, Lemei Wang, Yujue Ding, Yong Qin, Bo Zhou, Xiaochun Liang, Hai Wang, Huiyong Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Cyberspace Secur Res Ctr Pengcheng Lab Shenzhen Peoples R China Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn Minist Educ Beijing Peoples R China Shenzhen Olym Informat Secur Technol Co Ltd Shenzhen Peoples R China Guilin Univ Elect Technol Sch Math & Comp Sci Guilin Peoples R China

ISBN: (纸本)9781665464574

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： vehicular ad hoc networks (VANETs) privacy protection authentication cloud computing equality test

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Chanel Case and Bag Brand and Its Color Design Based on Semiotics Theory in the Background of Big Data

Chanel Case and Bag Brand and Its Color Design Based on Semi...

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international conference on Multi-modal Information Analytics, MMIA 2021

作者： Huang, Xiaohua Shao, Yueyun He, Shiqi School of Design Ningbo Tech University Ningbo Zhejiang315100 China Faculty of Innovation and Design City University of Macau Macau999078 China Zhejiang Fashion Institute of Technology Ningbo Zhejiang315211 China

ISBN: (纸本)9783030748104

Since the 21st century, under the background of the Internet and big data era, Chanel cases and bags brand designers pay more attention to the trend of the design elements of the symbol, especially the application of color design. This paper applies the semiotic theory to study the application of its color design, and analyzes the reason why Chanel cases and bags keep the top of the brand. The research method uses questionnaire survey to understand the psychological factors and aesthetic taste of consumers who choose Chanel luggage brand. From the results of the questionnaire to get Chanel bags color matching, style design related data, hope to discuss Chanel bags color design, for China’s current luggage brand construction can provide some reference basis. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Estimate Initial and Final Data Bounding for Big Data Progressive Sampling in Parallel computing Environment 1st

Estimate Initial and Final Data Bounding for Big Data Progre...

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1st international conference on intelligent Systems in computing and Communications, ISCComm 2023

作者： Aradhya, B. C. Yathish Dinesh, H.A. Lokesh, M.R. Visvesvaraya Technological University-Research Resource Center Jnana Sangama VTU Main Rd Visvesvaraya Technological University Belagavi Karnataka Machhe590018 India Tumakuru Karnataka is affiliated to Visvesvaraya Technological University Karnataka Belagavi572106 India Department of Information Science and Engineering A J Institute of Engineering and Technology Mangalore India

ISBN: (纸本)9783031756078

Big Data Progressive Sampling requires initial and final data bounding values to generate optimal number of samples in order to train any learning algorithm. Any learning algorithm can be trained for minimal hypothesis space. Approximating data bounding to Rademacher Averages of a given hypothesis space and learning algorithm ensure final data bounds with Ε-Approximation. This final Data bounding value serves as stopping criterion for Progressive Sampling. statistically optimal sample size estimation technique ensure minimal initial sample size. In this work, we propose a methodology to approximate initial and final Data bounding to Progressive Sampling using local Rademacher in big data Hadoop environment with experimental values along with computational parameters. Also the performance of learning algorithm to various highly complex big datasets for their corresponding initial minimal value and final Data bound to Rademacher Averages are experimentally shown in Hadoop distributed environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Dynamic Deployment of DNN Inference Tasks Based on distributed Proximal Policy Optimization 1

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1st international Artificial Intelligence conference, IAIC 2023

作者： He, Wenchen Li, Yitao Wang, Liqiang National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029 China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

With the widespread adoption of edge computing, Deep Neural Networks (DNN) inference tasks are gradually deployed on edge computing nodes. The inference and decision-making process of intelligent services is moved to the edge side, reusing edge resources to provide ubiquitous services. However, during the service process, due to constrained edge resources or factors such as terminal mobility, DNN inference tasks may experience long delays or service interruptions, affecting the timeliness and continuity of the services. To address the problem of deteriorated communication conditions and reduced data transmission efficiency during terminal mobility, which leads to decreased service quality or even interruptions, a dynamic deployment method for DNN inference tasks based on distributed proximal policy optimization (DPPO) is proposed. Building upon an edge-terminal collaborative architecture for dynamic deployment of DNN inference tasks, this method takes into account the terminal’s location, communication conditions, and the availability of resources in accessible edge nodes. The process involves DNN model caching, inference computation offloading, as well as communication and computation resource allocation. The experimental results demonstrate that the proposed method can adapt to the dynamic environment of the edge and achieve the integration and on-demand allocation of edge multidimensional resources, effectively ensuring service continuity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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A distributed Identity Authentication Scheme for Differential Fault Attack

A Distributed Identity Authentication Scheme for Differentia...

引用

international conference on Communication technology (ICCT)

作者： Yundi Deng Haoxiang Luo School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665432078

Aiming at the problem of limited computing resources in the current Internet of Things terminals, this paper proposes a method to split ID for distributed identity authentication. This paper takes SM4 as 128-bit encryption algorithm and GIFT as 64-bit encryption algorithm, and analyzes the security of them in the case of differential fault attack. At the same time, we calculated that the 128-bit length ID can be split into two sub-IDs of 64-bit length for authentication, and each sub-ID can be sent 19 times, which can offset the security risks caused by the reduction of grouping length caused by the encryption algorithm. Finally, we simulate the time delay of 128-bit authentication and 64-bit distributed authentication, which further proves the superiority of distributed authentication in the environment with limited computing resources.

关键词： Ciphers Delay effects conferences Authentication Communications technology Encryption Internet of Things

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TransFiLM: An Efficient and Lightweight Audio Enhancement Network for Low-Cost Wearable Sensors

TransFiLM: An Efficient and Lightweight Audio Enhancement Ne...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Shilin Chen Jianhao Weng Shicong Hong Yanbo He Yongpan Zou Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Information Hub Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wearable devices such as headphones are increasingly popular in people's lives, and there is an increasing focus on how to achieve continuous and reliable information input using these devices. However, due to constraints in computing power, low power consumption, and low operating frequencies, such devices often record and transmit signals at lower sampling rates, the resultant lower-quality signals often have catastrophic implications for system performance. Efficient real-time conversion of low-resolution speech signals to full-resolution high-quality signals using low-cost wearable sensors on edge devices is a challenging research endeavor. To address this, this paper designs TransFiLM, a mobile deep learning network. It allows users to obtain full-resolution high-quality audio signals using low-cost wearable sensors on edge devices. TransFiLM integrates residual learning and super-resolution networks and employs effective signal processing strategies to achieve audio upscaling and noise reduction, significantly improving audio quality. We implement a prototype on commercial devices and conduct a series of experiments to evaluate its performance. Using signal-to-noise ratio (SNR) and log-spectral distance (LSD) as evaluation metrics, TransFiLM exhibits superior performance compared to other time-domain methods in cross-user, cross-corpus, and cross-noise environment testing. Additionally, our TransFiLM network handles 8192 samples with a response time of 181 ms, which meets the requirement to run in real-time on edge devices.

关键词： Performance evaluation Deep learning Noise reduction Real-time systems Time factors Wearable devices Time-domain analysis Wearable sensors intelligent sensors Signal to noise ratio

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Enhancing Human Action Recognition through advanced Motion Descriptors and Machine Learning

Enhancing Human Action Recognition through advanced Motion D...

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Optimization Techniques for Learning (ICOTL), international conference on

作者： Vijay Ramalingam D Saravanan S. Vignesh Rahin Batcha R Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai

Understanding human activities from videos presents a formidable challenge in Computer Vision. intelligent video systems primarily specialize in automatically recognizing human actions within video sequences and annotating them accordingly; human action identification involves recognizing actions associated with one or more objects based on analysis of their activities and context. As technology has proliferated rapidly, huge amounts of human-centric video data have accumulated; it has become an obstacle in contemporary video processing systems due to identifying all these actions efficiently. Complex backgrounds compound these challenges, creating error margins that severely hamper many object detection algorithms and creating error margins that compromise performance - particularly with regard to occlusion and distortion issues. This work addresses these obstacles using Apache Spark with in-memory computing and distributed environment for improved recognition of human actions; specifically, a Local Motion Descriptor is employed for feature extraction that calculates intensities between frames that include both motion and appearance elements. Spark MLlib employs random forest to effectively recognize human behavior. Experimental studies are performed to evaluate its activity recognition capabilities and results are benchmarked against popular supervised classification techniques like Decision Trees; results demonstrate that our proposed model outshone other classification techniques, confirming its efficacy in activity recognition.

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