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Edge computing devices Authentication using Quantum computing

Edge Computing Devices Authentication using Quantum Computin...

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International conference on computing and Networking Technology (ICCNT)

作者： vinayak A. Telsang Mahabaleshwar S. Kakkasageri Anil D. Devangavi Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(v v), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices.

关键词： Privacy Quantum computing Costs Authentication vehicular ad hoc networks Blockchains Registers Servers Industrial Internet of Things Edge computing

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Intelligent Computation Offloading Mechanism of UAv in Edge computing 2

Intelligent Computation Offloading Mechanism of UAV in Edge ...

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2nd International conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Li, Feixiang He, Siyuan Liu, Mingzhe Li, Ning Fang, Chao The 15th Research Institute of China Electronics Technology Group Corporation Beijing China Beijing University of Technology Beijing China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

With the recent technological progress and the reduction of manufacturing costs, UAvs have attracted more and more attention in various applications such as cargo transportation, shooting, rescue and search, as well as wireless platforms. UA v is very important in wireless area because it is able to help improve communication quality. In the wireless network edge computing scenario, a low delay oriented multi-UA v computation offloading service strategy is proposed. Wireless network base stations usually work in multi-channel environment. There are multiple UA v devices to achieve effective access coordination to meet the challenge of computation offloading. Aiming at the problem of resource constraints in computation offloading, a resource optimal allocation model and a discrete bat algorithm for multi-UA v equipment energy consumption, transmission delay and computing delay are proposed. Compared with other swarm intelligence optimization algorithms, this algorithm can effectively shorten the average delay of task offloading. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAv)

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AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization 42

AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constra...

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42nd IEEE International conference on Computer Communications, INFOCOM 2023

作者： Chen, Ying Inaltekin, Hazer Gorlatova, Maria Duke University DurhamNC United States Macquarie University North RydeNSW Australia

ISBN: (纸本)9798350334142

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (v) and visual-inertial (vI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source v-and vI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method. © 2023 IEEE.

关键词： Edge computing

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Deep Learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping 3rd

Deep Learning-Based Automatic Speaker Recognition Using Self...

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3rd International conference on Computer vision, High Performance computing, Smart devices, and Networks, CHSN 2022

作者： Preethi, K. Prasad, C.v.P.R. Telangana State Hyderabad India

ISBN: (纸本)9789819966899

Automatic speaker recognition (ASR) plays the major role in many applications including forensics, dictionary learning, voice verification, biometric systems, and so on. The performance of these application depends on efficiency of ASR system. However, the conventional ASR systems were developed using standard machine learning algorithms, which resulted in low recognition performance. Therefore, this work is focused on development of deep learning-based ASR system. Initially, voice features are extracted using Mel-frequency cepstral coefficients (MFCC), which analyzed the spectral properties of various voice samples. Then, self-organized feature map (SOFM) is applied to reduce the number of available features, which selects the best features using Euclidian similarity between features. Further, deep learning convolutional neural network (DLCNN) model is used to train the features and forms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASRNet system resulted in superior recognition performance as compared to conventional systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Neural Networks Input Techniques to Maintain a Small Skew Angle in Bit-Patterned Magnetic Recording with a v-Shaped Read-Head Array

Neural Networks Input Techniques to Maintain a Small Skew An...

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2023 International Technical conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Fatika, Kirana Alif Koonkarnkhai, Santi Kovintavewat, Piya Warisarn, Chanon Research Unit Bangkok Thailand Nakhon Pathom Thailand

ISBN: (纸本)9798350326413

The demand for enormous storage devices has kept increasing, leading to the development of various advanced technologies with a vast storage capacity. Extensive numbers of related research studies have been aiming at optimizing code design and algorithms analytically;however, enacting them on practical devices has been scarce. Achieving this demand might bring some obstacles called two-dimensional interference and skew angle (SA). To meet the challenge of the obstacle, we propose a SA detection method for bit-patterned magnetic recording systems by computing a specific target by three readback sequences before estimating the SA value and detecting the SA amount happening in the system using an application of neural network namely multilayer perceptron. An error correction code, low-density parity-check, is applied, and its decoder outputs a log-likelihood ratio whose probability density distribution is examined. The simulation results show that the sliding window technique can significantly provide a better bit error rate performance. © 2023 IEEE.

关键词： Bit error rate

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Fusion of LBP and Median LBP for Dominant Region Based Multimodal Recognition Using Imperfect Face and Gait Cues 3rd

Fusion of LBP and Median LBP for Dominant Region Based Multi...

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3rd International conference on Computer vision, High Performance computing, Smart devices, and Networks, CHSN 2022

作者： Annbuselvi, K. Santhi, N. Sivakumar, S. V.V.Vanniaperumal College for Women Madurai Kamaraj University Virudhunagar Tamilnadu Madurai India Cardamom Planters’ Association College Madurai Kamaraj University Bodinayakanur Tamilnadu Madurai India

ISBN: (纸本)9789819966899

The paper provides a novel approach to recognize imperfect face and gait cues by using only the dominant region of such cues which contain more information than that possessed by the other regions. To enhance features in such dominant region, a fusion of Local Binary Pattern (LBP) and Median Local Binary Pattern (Median LBP) procedure is proposed. Initially, the given imperfect face and gait probe images are divided into six overlapped half regions. After partition, the dominant overlapped half regions of face and gait are selected by using content based image retrieval process. Subsequently, the features of dominant overlapped imperfect face and gait regions are enhanced by fusing the feature vectors obtained by using fusion of LBP and Median LBP methods. Next the Eigen feature vectors followed by Fisher’s vectors are constructed using Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) dimensionality reduction algorithms. The decisions from face and gait biometric systems are obtained separately by using Euclidean distance measure. Finally, the decisions of face and gait classifiers are fused at decision level for recognition using AND/OR rule. The method is verified on freely available ORL face and CASIA B gait datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Local binary pattern

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, v. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Processing Power-based Task Scheduling Technique for Cloud-Fog Environment 17

Processing Power-based Task Scheduling Technique for Cloud-F...

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17th conference on Industrial Electronics and Applications (ICIEA)

作者： Lepcha, Norbu Z. Halgamuge, Malka N. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Comp Sci & Engn Chennai Tamil Nadu India RMIT Univ Dept Informat Syst & Business Analyt Melbourne Vic 3000 Australia

ISBN: (纸本)9781665409841

Internet of Things (IoT) applications have emerged as a critical component in improving people's quality of life. The increasing volume of data generated by IoT devices, on the other hand, places a strain on the resources of traditional cloud data centres. This makes cloud data centres unable to meet the demands of IoT applications. Due to resource constraints and the massive connectivity of IoT devices, efficient task (incoming request) allocation is critical for service providers. We create a novel processing power-based task scheduling technique that employs a broker manager to distribute tasks and the process strategy across multiple devices. This is important in cloud technology because it enables organisations to effectively scale their processes. We use multithreading to parallelize processes and evaluate the performance of the proposed model using existing task allocation algorithms (i) round-robin, (ii) weighted round-robin, (iii) random allocation, (iv) genetic algorithm, and (v) dynamic algorithm. Our results show that when there are few tasks and one cluster has more processing capacity than the others, a weighted round-robin algorithm works well. The effectiveness of allocating based on weight shows no improvement in task completion time as task size increases. Using our proposed model, we show that the dynamic algorithm outperforms all other algorithms for task scheduling by 1.37 times for all task sizes.

关键词： Task Scheduling resource allocation IoT applications dynamic algorithm cloud-fog computing

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Testbed and Experiments for Quantum-Conventional Networking

Testbed and Experiments for Quantum-Conventional Networking

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Quantum Communications, Networking, and computing (QCNC), International conference on

作者： Nageswara S. v. Rao Muneer Alshowkan Joseph C. Chapman Hsuan-Hao Lu Nicholas A. Peters Joseph M. Lukens Guoliang Xue Saikat Guha Emily van Milligen Don Towsley Gayane vardoyan Leonardo Bacciottini Linran Fan Oak Ridge National Laboratory Oak Ridge USA Purdue University West Lafayette IN USA Arizona State University Tempe AZ USA University of Maryland College Park MD USA University of Massachusetts Amherst MA USA University of Texas Austin TX USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

The realization of quantum networks requires the development of devices and methods unprecedented in conventional networks, and yet they critically depend on the latter for implementing foundational blocks and essential operations. We describe a testbed to support the development and testing of their functionality and performance by providing quantum and conventional data planes and devices, together with a secure conventional control plane. It incorporates a variety of entangled photon sources, qubit technologies, detector technologies, photonic components, and supporting conventional switches and workstations. It implements a novel fiber telescoping scheme that provides suites of connections using fiber spools and inground-aerial fiber loops. We briefly summarize a variety of experiments conducted over this testbed including: (i) flex-grid quantum connection experiments, (ii) quantum state and channel tomography, (iii) utilization of quantum key distribution keys to secure conventional encryption and firewall devices, (iv) comparative study of analytical capacity estimates and entanglement throughput, (v) deployed squeezing coexisting with conventional communications, and (iv) measurement of polarization time variation.

关键词： Optical fiber polarization Performance evaluation Quantum entanglement Optical fiber networks Tomography Optical fiber devices Workstations Synchronization Optical fiber testing photonics

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MedBot – An NLP based ChatBot for Diabetes Prediction

MedBot – An NLP based ChatBot for Diabetes Prediction

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International conference on computing and Networking Technology (ICCNT)

作者： K Sreekar v Aswin vishnu Narayanan Senthil Kumar Thangavel K Somasundaram Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics and Computer Science Ashland University Ohio United States of America

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In this age of technology, with digital devices being accessible by almost everyone, have made our lives much better. With the advent of Machine Learning and advanced algorithms at our tips, the applications are immensurate. Such an application can be found in the health-care sector. The need for better and accessible health-care is of immense precedence. In this paper, we employed Machine Learning algorithms like Logistic Regression and other state of the art techniques to predict diabetes, given patient data. Diabetes is an incessant condition that is affecting millions worldwide, every year. Early detection of diabetes and prompt diagnosis is crucial. We have addressed that using a chat-bot to predict diabetes, which we believe is a portable and accessible solution for first-level assessment. We have employed PIMA Indian Diabetes Dataset into our chat-bot, providing interactive medical advice, and enhancing patient engagement, using Telegram API, resulting in an accuracy of 77%.

关键词： Logistic regression Machine learning algorithms Accuracy Personal digital devices Machine learning Prediction algorithms Chatbots Diabetes Medical diagnostic imaging

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