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DLT-Based Cloud File Sharing System 6

DLT-Based Cloud File Sharing System

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6th Parul international conference on Engineering and Technology, PICET 2024

作者： Chouhan, Ruchika thatipamula, Ajay thumati, M.V.S. Rama Krishna Sharma, Sachin Tandra, Vennela Tadiboina, Ramanjaneyulu Department Computer Science Engineering Parul University Vadodara India Department Computer Science Engineering Parul University Hyderabad India Department Computer Science Engineering Parul University Vinukonda India Department Computer Science Engineering Parul University Indore India Department Computer Science Engineering Parul University Vijayawada India

ISBN: (纸本)9798350369748

Cloud storage is crucial for managing large datasets, but dependence on a single cloud space raises security concerns. Conversely, distributed Ledger Technology (DLT) provides a secure cloud-based storage system operating on a peer-to-peer network. In our proposed system, user files are encrypted and distributed across multiple peers using the Interplanetary File System (IPFS) protocol, with hash values stored in the *** project integrates DLT and a cloud platform for storing filesfrom various users. Our project, distributed Cloud File Sharing Technology (DCFST), merges distributed computing principles with cloud technology to overcome traditional file-sharing limitations. It enables secure file sharing, storage, and access across interconnected devices, ensuring data integrity and authentication through blockchain. Smart contracts automate access control,granting precise content control to users. DCFST orchestrates a peer-to-peer network of nodes, with a central coordinator managing node discovery, file indexing, and load balancing. the implementation utilizes Python, JavaScript, and Ethereum for smart contract execution. DCFST introduces a resilient, user-centric file-sharing ecosystem by merging distributed systems with cloud infrastructure. It contributes to decentralized technology evolution, revolutionizing data collaboration and access paradigms. © 2024 ieee.

关键词： distributed ledger

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Energy-efficient Data Aggregation Schemes for Low-power distributed Wireless sensor Networks using Compressed Sensing 5

Energy-efficient Data Aggregation Schemes for Low-power Dist...

引用

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

作者： Manuel, Ebin M. Pankajakshan, Vinod Indian Institute of Technology Department of Electronics and Communication Uttarakhand Roorkee247664 India College of Engineering Dept. of Electronics and Communication Kerala Trivandrum695016 India

ISBN: (纸本)9798350334128

the advancements in distributed computing and technologies such as Industry 4.0 have rapidly deployed wireless sensor networks. In many scenarios, battery-connected wireless sensors gather the surrounding information that is to be aggregated at a base station. the volume of data generated from large-scale wireless networks has significant impacts on storage, network latency, and bandwidth. this demands efficient data aggregation systems for new-generation smart networks. Compressed sensing techniques are proven to deliver efficient compression for sparse signals. this paper evaluates the extension of such compression techniques to distributed low-power wireless sensor networks for efficient data aggregation. © 2023 ieee.

关键词： Compressed sensing

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Daily Routine Recognition from Longitudinal, Real-life Wearable sensor Data for the Elderly

Daily Routine Recognition from Longitudinal, Real-life Weara...

引用

6th international conference on Activity and Behavior computing (ABC)

作者： Alia, Sayeda Shamma Lago, Paula Concordia Univ Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9798350375510;9798350375503

As our population continues to age, there is a growing need for tailored healthcare solutions for the elderly. Despite the promise of current wearable devices, the effectiveness of these devices in addressing complex healthcare needs, especially in identifying cognitive decline still requires extensive refinement. Analyzing Routines can be a potential solution as it can provide insights into cognitive health, potentially serving as an early indicator of cognitive decline or impairment. this paper presents a framework to identify routine and non-routine behaviors using the CASAS smartwatch dataset, a public real-life longitudinal dataset. this is of particular significance in health monitoring and understanding long-term behavior patterns among the elderly. Our work addresses challenges throughout the entire Routine recognition process, including noisy and incomplete data, class imbalance, reliance on accurate activity labeling, and the preprocessing overhead typically encountered in wearable sensor datasets. the findings of our study suggest that labeling the dataset based on cosine similarity and label propagation results in improved activity recognition accuracy compared to kNN for classes with fewer samples. However, kNN demonstrates the best overall performance, particularly for classes such as Work, Other, Hygiene, where it excels due to its abundance in the dataset. Moreover, our findings emphasize the importance of identifying the most occurring activity sequences, which influences routine identification. this underscores the importance of recognizing and modeling recurring activity patterns, highlighting their significance in the routine identification process. Understanding these patterns can have an impact on the healthcare of the elderly, enabling tailored interventions to support healthy aging at home. In the future, this research aims to contribute to improved monitoring, timely interventions, and ultimately, enhanced quality of life for older adults li

关键词： Routine Elderly Healthcare Aging Daily-life Activities

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Enhancement of Network Lifetime in Wireless sensor Networks Using Modified Cluster Head Selection Algorithm 9

Enhancement of Network Lifetime in Wireless Sensor Networks ...

引用

9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Satyanarayana, P. Guptha Maremalla, Gokul Sriram Phanindra, G. Devi, A.Ch.R.R. Anantha Rajya Lakshmi, K. Sriramam, Yadavalli S S V. R. Siddhartha Engineering College Department of ECE Vijayawada India S.R.K.R. Engineering College Department of ECE A.P Bhimavaram India

ISBN: (纸本)9798350397376

A significant number of sensor nodes that have been distributed around the monitoring area make up the wireless sensor network (WSN), which is a smart an autonomous *** creating a wireless sensor network, one of the most challenging aspects is working out how to maintain a consistent energy usage amount while simultaneously extending the life of the network, presuming the nodes in the network can only be powered by batteries in the majority of scenarios. With the objective of lowering the network's overall energy consumption and improving its lifespan, we suggest an augmentation technique. Enhancing the energy distribution in groups across all the sensor nodes has been carried out with the goal of lowering the amount of energy lost during network connections. A cluster head selection technique serves as the basis for the newly developed and enhanced methodology. Additionally, a modified version of the TDMA schedule has been implemented. By using mathematical analysis and the outcomes of a MATLAB 2015a simulation, the feasibility of the proposed technique was demonstrated. © 2023 ieee.

关键词： Energy efficiency

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Optimal sensor Data Resampling for Anomaly Detection in Industrial Control systems 6th

Optimal Sensor Data Resampling for Anomaly Detection in Indu...

引用

6th international conference on Recent Innovations in computing, ICRIC 2023

作者： Birihanu, Ermiyas Lendák, Imre Data Science and Engineering Department FacultyofInformatics Eötvös Loránd University Pázmány Péter Str. 1/A 1117 Budapest Hungary

ISBN: (纸本)9789819734412

the goal of this study is to identify optimal sensor data sampling rates for anomaly detection in industrial control systems (ICS). the identification of optimal sensor data sampling rates allows ICS operators to store less data while training adequate anomaly detection models. In this study, the autoencoder method was utilized to enhance the effectiveness of the detection process and statistical (histogram) anomaly scores were applied to determine anomaly threshold points. the experiment on the proposed method was conducted using the Secure Water Treatment (SWaT) and the HIL-based Augmented ICS Security (HIL-HAI) datasets. the results of the experiment demonstrate that the proposed method outperformed the current anomaly detection techniques. Our code is publicly available at https://***/Ermiyas21/ICSsensor. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Identification (control systems)

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Managing Smart Urban Transportation with the integration of Big Data Analytic Platform 6

Managing Smart Urban Transportation with the integration of ...

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Kunjir, Sanika Nitin Patil, Sakshi Sanjay Hingane, Bhakti Sunil Pagariya, Janvi Anand Rashid, Mamoon Vishwakarma University Pune India

ISBN: (纸本)9798350304480

Urban traffic management is a major challenge for cities worldwide, but with the help of technology, such as big data analytics and intelligent transport systems, cities are working to improve their situations. An integrated big data analytics platform has the potential to change transportation networks for the better, providing real-time insights that can be used for decisions. By collecting and analyzing real-time data from various sources, such as traffic sensors and GPS, we can predict traffic flow and create plans more efficiently. this paper will explore how an integrated big data analytics platform can be used to improve urban transportation management. It discusses some of the challenges that urban transportation systems face and provides insights from government officials and industry experts. the importance of this paper reviews fundamental concepts such as big data analytics and smart cities so that you have a better understanding of what this technology can do. Afterwards, it identifies opportunities for improving urban transportation networks using big data analytics. © 2023 ieee.

关键词： Big data data analytics data quality distributed environment public transport management smart city

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6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer for distributed Intrusion Detection systems in Advanced Networks 6

6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer ...

引用

6th international conference on Networking and Advanced systems, ICNAS 2023

作者： Sakraoui, Sabrina Derdour, Makhlouf Ahmim, Ahmed Badji Mokhtar Annaba University Networks and Systems Laboratory Department of Computer Science Annaba Algeria Oum El Bouaghi University Networks and Systems Laboratory Department of Computer Science Oum El Bouaghi Algeria Mohamed-Cherif Messaadia University Networks and Systems Laboratory Dpt. of Mathematics and Computer Science Souk Ahras Algeria

ISBN: (纸本)9798350319170

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. the system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. the promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem. © 2023 ieee.

关键词： Wireless networks

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distributed collaborative optimal dispatch of distribution system with multi-stakeholders 6

Distributed collaborative optimal dispatch of distribution s...

引用

6th ieee international Electrical and Energy conference, CIEEC 2023

作者： Ning, Nan Chen, Keyang Xie, Min Cheng, Zhou Ye, Jianan Xie, Yuxing Rao, Yun GuiAn Power Supply Bureau of Guizhou Power Grid Co. Ltd Guizhou China School of Electric Power South China University of Technology Guangzhou China

ISBN: (纸本)9798350346671

In recent years, to achieve energy conservation and emission reduction and the clean transformation of energy structure, the user-side distributed energy has developed rapidly, and the coupled operation characteristics of cooling, heating and power have become increasingly prominent. the distribution system with multiple stakeholders has received extensive attention. However, the change of distribution pattern will bring many problems to the optimal operation of power system. this paper introduces the analysis target cascading (ATC) theory and proposes a coordinated and optimized scheduling model with distribution network operators, distributed energy providers., and combined cooling heating and power (CCHP) load integrators as the main stakeholders by exchanging contact variable information. Based on the detailed technical model of the internal components of each stakeholder., the linkage variables between each stakeholder and the distribution network operator are decoupled and solved in parallel to improve the computational efficiency. the model considers the access of wind-light-storage and combined cooling., heating and power generation systems., which is conducive to the realization of multi-energy complementary operation of the distribution system and the transformation of energy structure. © 2023 ieee.

关键词： Computational efficiency

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Quantum Deep Reinforcement Learning for 6G Mobile Edge computing-based IoT systems 19

Quantum Deep Reinforcement Learning for 6G Mobile Edge Compu...

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

作者： Ansere, James Adu Duong, Trung Q. Khosravirad, Saeed R. Sharma, Vishal Masaracchia, Antonino Dobre, Octavia A. Sunyani Tech Univ Sunyani Ghana Queens Univ Belfast Belfast Antrim North Ireland Nokia Bell Labs Murray Hill NJ USA Mem Univ St John NF Canada

ISBN: (纸本)9798350333398

this paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover's algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed.

关键词： Reinforcement learning

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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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： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. the use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 ieee.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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