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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

共 1067 条记录，以下是71-80 订阅

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Automatic Creation of Ontologies and Rules from ML Models for Access Control in IoT 15

Automatic Creation of Ontologies and Rules from ML Models fo...

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15th International conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Tyagi, Surendra Jinwala, Devesh C. Bhattacharjee, Subhasis Computer Science & Engineering Indian Institute of Technology Jammu India Computer Science & Engineering S. V. National Institute of Technology Surat India

ISBN: (纸本)9798350370249

Ontologies have been increasingly used in designing access control policies in Internet of Things (IoT) systems. Machine Learning (ML) has been extensively used to identify patterns from existing data. Trained ML models can be used on end devices for applying access control for resources and users. However it is unreasonable to deploy ML models into the low-powered and mostly hand-held IoT devices. This paper demonstrates an attempt to exploit the power of ML for generating access control rules. We propose algorithms for generating Ontologies and policy rules from trained ML models. Motivated by their simple design, low or almost no requirement for data preprocessing, no requirement for scaling/normalization of data, simple, intuitive design, and most essentially an inherent ability to optimize, by handling irrelevant features through pruning, we use Decision Trees (DT) for the classification in the proposed approach. For smart home dataset, we show in this paper, automatic creation of Ontologies and policy rules from trained DT models. The resulting Ontologies and Semantic Web Rule Language (SWRL) rules are then deployed into an Ontological framework (Protege) and tested for consistency using a Reasoner. Our approach demonstrates a significant improvement in the efficiency of rule generation and offers a promising direction towards designing automated access control systems. In Protege, we achieve 100% consistency in deployment and testing of the generated access control policies by firing queries. We obtain exactly the same accuracy from Protege queries as we get from ML model. We also propose pruning on DT as a fall-back method for IoT devices with limited computing and memory resources that can handle only limited number of policy rules. The proposed method creates a deployable access control system in a few minutes without any human intervention. Our experiment shows a turnaround time of 5 minutes for the Smart Home dataset. ©2024 IEEE.

关键词： Decision trees

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Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic algorithms 8

Optimizing Energy Efficiency in 5G Networks: Integrating Mul...

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8th IEEE International conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Arun, v. Gowtham, A. Arul, U. Angeljulie, J. School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Madanapalle Andhra Pradesh Annamayya India Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Cse Tamilnadu Coimbatore India

ISBN: (纸本)9798331505462

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station 'talk' more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings;(2) improved network performance;and (3) enhanced sustainability under 5G conditions.... We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve. © 2024 IEEE.

关键词： Base stations

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Preserving the Privacy in IoT Medical data in Edge computing using Modified AES Model with Discrete-Time Alternating Quantum Walk

Preserving the Privacy in IoT Medical data in Edge Computing...

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2023 International conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Dileep, M.R. vineeth, L. Avula, Sashikanth Reddy Navaneeth, A.v. Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350313413

The rapid growth of IoT edge devices has caused in an unprecedented explosion of data volume. Extremely often, the created data is gathered and disseminated via edge-based IoT structures. When hostile IoT devices make requests to steal private data from a cloud storage system via edge nodes, the data-sharing privacy exposure issue becomes even more concerning. We provide evolutionary privacy protection learning algorithms for an IoT data sharing scheme based on edge computing as a solution to the stated problem. Deep learning of medical models in an IoT-based healthcare scheme is described here via a federated learning approach. Using a combination of the alternating quantum walk (AQW) and the Advanced encryption standard (AES), this research suggests a new method of picture encryption. A keystream generator associated with AQW limits is used to build a likelihood distribution matrix, and the study makes use of quantum features to enhance the AES algorithm. To unlock the AES method, we must first extract certain singular values from the matrix. Elements of the probability distribution matrix stand in for Rcon in the AES algorithm. The AES algorithm's S-box and ShiftRow transformation mapping rules are then jumbled based on the increasing size of the clone probability distribution matrix. The preprocessing is done with a likelihood distribution matrix and a plaintext XOR operation, and the encryption is done with a modified version of the AES algorithm. To prevent an adversary from concluding confidential medical data via assaults like a perfect reconstruction attack, etc., this encryption technique may be applied to better safeguard local models. © 2023 IEEE.

关键词： Advanced encryption standard Alternating quantum walk Data security Internet of Things Privacy Preserving

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Advanced Encryption Techniques for Protecting Sensitive Data in Multi Cloud Architecture: A Security First Approach 1

Advanced Encryption Techniques for Protecting Sensitive Data...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Anand, v. Saravanan, N. Mahendiran, A. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Srinivasa Ramanujam Centre SASTRA Deemed University Tamil Nadu Kumbakonam India Thanthai Hans Roever College Tamil Nadu Perambalur India

ISBN: (纸本)9798331518578

'Cloud computing' refers to a broad range of computer ideas that include several PCs linked by a real-time transmission network, such the internet. Cloud computing is a new paradigm that has drawn a lot of attention from researchers recently due to its potential to lower computing expenses. This study compares RSA, AES, and a technique that combines both of them. The effectiveness of these methods is examined in relation to encryption and decryption times for various data set sizes. The experiment was designed to be replicable and was conducted on standardised devices using the Mathematical writing and analysis tool, which is based on the Wolfram Language. In this experiment, encryption and decryption speeds were tested and compared for data sizes ranging from 64 to 176 bytes. As a second indicator of the algorithms' performance, the number of iterations each algorithm completed was also counted. The results demonstrate that AES had the fastest encryption and decryption times, and that the hybrid model had the greatest features of both RSA and AES in terms of speed and security. With encryption times of 0.10 ms and 0.20 ms for 64-byte and 176-byte data, respectively, the hybrid approach provided a balanced performance. AES outperformed RSA in throughput by up to 4 times and the hybrid technique by up to 2 times. According to these results, the hybrid technique successfully strikes a balance between speed and security, even though AES is best for speed. © 2025 IEEE.

关键词： Cloud computing Decryption Encryption Hybrid RSA

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GesturesSpeak: An IoT-Based Gesture to Speech Translator 4

GesturesSpeak: An IoT-Based Gesture to Speech Translator

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4th IEEE International conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakravarthi, Sangapu Sreenivasa Manogna, S.v.S. Reddy, Shenagapally Aashish Sountharrajan, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Cse Chennai India

ISBN: (纸本)9798350387063

Hand gestures serve as a natural and intuitive means for humans to communicate ideas and intents. For people with speaking or hearing disabilities, sign languages based on visual hand gestures are indispensable for their expression. With recent advances in machine perception and proliferation of Internet-of- Things (IoT) devices, automating gesture recognition to generate textual or speech output has transformative potential to augment accessibility. This paper surveys key innovations in gesture- to-text conversion spanning machine learning, deep learning and IoT-driven approaches. The research first motivate the assistive technology applications of an IoT framework leveraging specialized sensors and edge devices for gesture tracking, coupled with intelligent algorithms to interpret gestures and convert them into language. Our study covers convolutional and recurrent neural networks for spatial and temporal modelling, as well as multimodal fusion techniques. Challenges related to occlusion, sensor constraints and model generalization are discussed. The paper highlights the tremendous value of gesture-driven systems in enabling more immersive human-computer interaction and accessible interfaces for diverse communities. © 2024 IEEE.

关键词： Gesture recognition

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Optimizing Traffic Flow Using Quantum Annealing

Optimizing Traffic Flow Using Quantum Annealing

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2024 Intelligent Technologies and Electronic devices in vehicle and Road Transport Complex, TIRvED 2024

作者： Bogatyrev, v.A. Moskvin, v.S. Saint Petersburg State University of Aerospace Instrumentation St. Petersburg Russia ITMO University St. Petersburg Russia

ISBN: (纸本)9798331518707

This paper investigates the use of quantum annealing for optimizing traffic flow, serving as a case study for broader applications in network optimization tasks. We start by modeling the traffic network as a quantum system, where intersections, roads, and traffic parameters are represented as qubits within a quantum annealing framework. The optimization process is outlined, illustrating how the quantum system evolves to identify low-energy states that correspond to optimal traffic flow configurations. By harnessing the unique properties of quantum mechanics, such as superposition and quantum tunneling, quantum annealing has the potential to efficiently explore extensive solution spaces beyond the capabilities of classical algorithms. This research aims to expand the understanding of how quantum computing can be applied to complex network optimization challenges, providing insights that could lead to more efficient, responsive, and sustainable management systems across various domains. © 2024 IEEE.

关键词： Quantum computers

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Human Activity Recognition using EfficientNet for Wearable Sensor Data

Human Activity Recognition using EfficientNet for Wearable S...

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2024 International conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Rahul Chiranjeevi, v. Murugan, B.S. Dhanasekaran, S. Senthil Pandi, S. Rajalakshmi Engineering College Department of CSE Chennai India M Kumarasamy College Of Engneering Department of CSE Karur India Kalasalingam Academy Of Research And Education Department of IT Krishnankoil India

ISBN: (纸本)9798350352689

Human Activity Recognition (HAR) has attracted considerable attention from researchers due to its broad implications in healthcare, smart environments, and entertainment. HAR involves the usage of sensor devices and the Internet of Things (IoT). Human Activity Recognition (HAR) has emerged as a crucial application in health monitoring, necessitating the ongoing utilization of Smart phones, smart watches, and wearable devices to document and track patients' everyday activities. Deep learning (DL)-based algorithms have shown effective in predicting various human actions using time-series data collected from cell phones and wearable sensors. Deep learning-based methods have faced challenges when used to time-series data in activity recognition. The proposed methodology has the potential to effectively address those concerns. In this paper a novel approach is proposed using EfficientNet by adding various layers during the classification phase. Proposed method is evaluated by using UCI-HAR dataset and PAMAPS2 dataset, Class imbalance during training and testing phase is reduced by using various data augmentation methods. Proposed method achieved an accuracy of 99.98 on PAMAPS dataset thereby achieving significant results than state of art methods. © 2024 IEEE.

关键词： Wearable sensors

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Securing Edge computing Environments with a Novel Chaos Theory-Based Lightweight Encryption Algorithm for Real-Time Data Protection 2

Securing Edge Computing Environments with a Novel Chaos Theo...

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2nd IEEE International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： vanitha, v. Saritha, G. Hemalatha, K. Sathish, P. Devi, P. Gayathri Department of of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Information Technology Sona College of Technology Salem India Nandha Engineering College Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

In the era of edge computing, securing data transmission has become paramount, especially given the real-time requirements and limited computational resources of edge devices. This study introduces a novel Chaos Theory-Based Lightweight Encryption Algorithm (CTBLEA) designed to enhance data protection in edge computing environments. The proposed algorithm leverages the inherent unpredictability of chaotic maps to secure data streams without imposing significant computational burdens. By adopting a lightweight encryption mechanism, CTBLEA ensures minimal latency and energy consumption, making it ideal for resource-constrained edge devices. Extensive experiments were conducted to evaluate the algorithm’s performance. CTBLEA achieved an encryption and decryption time of 0.25 ms per kilobyte, showing a 35% reduction in latency compared to traditional AES encryption in similar environments. Furthermore, the algorithm demonstrated an energy consumption reduction of 40% while maintaining high security standards with an average key space of 10^15, making brute-force attacks infeasible. Security tests showed a correlation coefficient close to zero (0.003) between the original and encrypted data, indicating high randomness and minimal data correlation. Additionally, the algorithm’s resistance to statistical and differential attacks was validated, with a 99.7% randomness score from the NIST suite tests. These results confirm the potential of CTBLEA for safeguarding real-time data in edge computing, offering a secure, efficient solution for next-generation IoT and edge environments. © 2024 IEEE.

关键词： Encryption algorithms

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Open Source Compiling for v1Model RMT Switch: Making Data Center Networking Innovation Accessible 15

Open Source Compiling for V1Model RMT Switch: Making Data Ce...

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IEEE/ACM 15th International conference on Utility and Cloud computing (UCC) / 9th International conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Das Robin, Debobroto Khan, Javed I. Kent State Univ Kent OH 44242 USA

ISBN: (纸本)9781665460873

very few of the innovations in deep networking have seen data center scale implementation. Because, the Data Center network's extreme scale performance requires hardware implementation, which is only accessible to a few. However, the emergence of reconfigurable match-action table (RMT) paradigm-based switches have finally opened up the development life cycle of data plane devices. The P4 language is the dominant language choice for programming these devices. Now, Network operators can implement the desired feature over white box RMT switches. The process involves an innovator writing new algorithms in the P4 language and getting them compiled for the target hardware. However, there is still a roadblock. After designing an algorithm, the P4 program's compilation technology is not fully open-source. Thus, it is very difficult for an average researcher to get deep insight into the performance of his/her innovation when executed at the silicon level. There is no open-source compiler backend available for this purpose. Proprietary compiler backends provided by different hardware vendors are available for this purpose. However, they are closed-source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable matchaction table architecture. This paper describes our work toward an open-source compiler backend for compiling P416 targeted for the v1Model architecture-based programmable switches.

关键词： Compiler Forwarding ASIC In network Computation P4 v1Model Programmable Data Plane Programmable Switch SDN

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Accelerators in Embedded Systems for Machine Learning: A RISCv view 38

Accelerators in Embedded Systems for Machine Learning: A RIS...

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38th conference on Design of Circuits and Integrated Systems (DCIS)

作者： Sanchez-Flores, Alejandra Alvarez, Lluc Alorda-Ladaria, Bartomeu Univ Illes Balears Ind & Construct Eng Dept Palma De Mallorca Spain Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain Univ Illes Balears Inst Inv Sanitaria IB IdisBa Inst Agro & Eco Agua INAGEA Ind & Construct Eng Dept Palma De Mallorca Spain

ISBN: (纸本)9798350303858

Embedded systems, which are mobile systems with IoT and constrained processing capability, have mostly functioned as a depiction of ubiquitous computing throughout the previous ten years. On the other hand, innovative applications are expanding, consuming more data, and employing ever-complicated algorithms. To run complex algorithms, accelerators have been added into the normal architecture of embedded systems made for convolutional neural networks (CNN), a machine learning technique. Energy economy, adaptability, versatility, and heterogeneity are some characteristics that the new devices that handle ML in embedded applications must have. However, ML algorithms cannot be executed on embedded systems because to a lack of computational capacity. To compute CNN model inference, accelerators are recommended and integrated into the embedded system utilizing the same energy and resource-saving methods. The appeal of accelerators based on the RISC-v architecture is the possibility to deliberately alter the host processor to improve communication with the accelerator. This work seeks a balanced design between acceleration and flexibility to deploy several CNN model configurations.

关键词： accelerator RISC-v energy efficiency

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