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A Blockchain Sharding Protocol supporting Dynamic Locality in Mobile Edge computing 13

A Blockchain Sharding Protocol supporting Dynamic Locality i...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Jo, Yongrae Park, Chanik Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the proliferation of mobile edge computing (MEC) in 5G networks, there has been growing interest in deploying high performance blockchain sharding protocols over MEC. However, despite numerous blockchain protocols being proposed, most do not consider workload characteristics that exhibit locality and mobility commonly found in MEC environments. We claim that the existing protocols are missing opportunities for performance improvement, e.g., exploiting locality, and have significant performance degradation by not considering dynamically changing locality, e.g., user mobility, resulting in more and more costly cross-shard transactions with remote access in the system. This paper proposes MEC-Chain, a novel blockchain sharding framework that effectively exploits locality and mobility for high performance. MEC-Chain hierarchically organizes sharded local chains, each assigned to state-ownership for locality-aware execution and fine-grained state resharding with the main chain. For exploiting locality, we design a hybrid processing model that allows fast local updates of blockchain state only when its ownership is assigned to the local chain. Otherwise, i.e., the cross-shard transaction is executed and validated against the globally consistent main chain. For handling mobility, we introduce a finegrained state resharding protocol that transfers state ownership dynamically between local chains in a fine-grained way with the help of users. By doing so, our proposal can efficiently handle a common workload that exhibits a high degree of locality that changes gradually over time. © 2022 IEEE.

关键词： Blockchain

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DRQN-based Task Offloading in UAV-assisted Mobile Edge computing Environments with Hidden Channel Conditions

DRQN-based Task Offloading in UAV-assisted Mobile Edge Compu...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Taewon Song Department of Internet of Things Soonchunhyang University Asan Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Mobile edge computing (MEC) with unmanned aerial vehicle (UAV) assistance offers promising solutions for reducing latency in $5\mathbf{G}$ networks. However, uncertainty in task processing requirements poses challenges for efficient task of-floading. This paper introduces a deep recurrent Q-network (DRQN) based algorithm for task offloading in UAV-assisted MEC environments with hidden channel conditions. We for-mulate the problem as a partially observable Markov decision process (POMDP), where the UAV decides whether to process tasks locally or offload them to a cloud server without complete information about the channel state. Our approach leverages temporal dependencies in the environment to make informed decisions under uncertainty. Simulations demonstrate that our DRQN-based algorithm outperforms previous methods, including DQN-based, offload-only, and local-only strategies. This work contributes to the development of robust task offloading strategies for dynamic edge computing environments in 5G and beyond networks.

关键词： Uncertainty Multi-access edge computing 5G mobile communication Markov decision processes Heuristic algorithms Autonomous aerial vehicles Information and communication technology Servers Vehicle dynamics convergence

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3DFed: A Secure Federated Learning-based System for Fault Detection in 3D Printer Industry 13

3DFed: A Secure Federated Learning-based System for Fault De...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Paramartha Putra, Made Adi Verana, Mark Sampedro, Gabriel Avelino Kim, Dong-Seong Lee, Jae-Min Informatic Enginering Stmik Primakara Denpasar Indonesia Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

This paper proposes a secure federated learning (FL) approach for 3D printer fault detection, namely 3DFed. Most current 3D fault detection systems were developed with a centralized learning approach, which is less efficient for large-scale deployment due to limited data for training. The FL-based system could be exploited to further increase fault detection accuracy while maintaining high performance by using the FedAvg algorithm. The 2D convolutional neural network (CNN) was used to extract data features from an image array. To further improve security in the simulation work, a certificate authority (CA) was added to maintain secure communication between the FL server and clients. The suggested 3DFed with the proposed CNN-based model can deliver high classification accuracy while preserving minimum time-cost, according to a thorough performance evaluation. Also covered in depth is how total client variance affects the learning process. © 2022 IEEE.

关键词： 3D printers

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Modified Butterfly Optimization Algorithm based on convergence Factor and Disturbance Strategy 11

Modified Butterfly Optimization Algorithm based on Convergen...

引用

11th international conference on Networks, Communication and computing, ICNCC 2022

作者： Hao, Congwang Chen, Lei Ma, Yunpeng School of Science Tianjin University of Commerce Tianjin300134 China School of Information Engineering Tianjin University of Commerce Tianjin300134 China

ISBN: (纸本)9781450398039

The butterfly optimization algorithm (BOA) is a relatively new optimization technology with strong competitiveness compared with other meta-heuristic algorithms. However, BOA has shortcomings in convergence accuracy, convergence speed, and jumping out of local optimum. This paper proposes an improved Butterfly Optimization Algorithm based on convergence factor and disturbance strategy (LCD-BOA) to solve these shortcomings. Based on the BOA algorithm, the Levy flight strategy and disturbance factor strategy are added to improve the exploration ability of the algorithm. In addition, the convergence factor strategy is added to improve the exploitation ability of the algorithm further so that the exploration and exploitation of the algorithm are balanced as far as possible. Finally, the experimental results on 11 benchmark functions prove the effectiveness of the improved algorithm. The experimental results show that the improved algorithm significantly improves the performance of BOA compared with other meta-heuristic algorithms. © 2022 ACM.

关键词： Heuristic algorithms

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Optimization of Power allocation coefficient for two-user NOMA using Nature-inspired optimization algorithms 14

Optimization of Power allocation coefficient for two-user NO...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ahuja, Shivam Chhabra, Hardik Jain, Aarti Netaji Subhas University of Technology Department of Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350335095

Power domain Non-Orthogonal Multiple Access (PD-NOMA) is an emerging name amongst the candidate technologies which are being considered for the deployment of fifth-generation networks. This medium access technique employs superposition of signals at the transmitter side for transmission and successive interference cancellation to retrieve the separate signals at individual receivers. For maximum utilization of PD-NOMA technique, the optimization of the power allocation coefficient values used for the superposition of the signals is crucial. This paper proposes nature inspired (NI) algorithms viz. Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Differential Evolution (DE), and Simulating Annealing (SA) for finding the optimum value of the power allocation coefficient for two user PD-NOMA. Numerical results show that by calculating power allocation coefficient using NI algorithms, the performance of the PD-NOMA has been enhanced in terms of Probability outage with respect to SINR. Moreover, simulations are performed to calculate power allocation coefficient by varying distance between two receiver nodes. Further, a comparison between different NI algorithms based power allocation coefficient calculations is compiled in terms of computational time and convergence and it is observed that the DE algorithm-based power allocation co-efficient calculations outperforms all other algorithms by 42.65%. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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DATASET PRUNING: REDUCING TRAINING DATA BY EXAMINING GENERALIZATION INFLUENCE 11

DATASET PRUNING: REDUCING TRAINING DATA BY EXAMINING GENERAL...

引用

11th international conference on Learning Representations, ICLR 2023

作者： Yang, Shuo Xie, Zeke Peng, Hanyu Xu, Min Sun, Mingming Li, Ping School of Electrical and Data Engineering University of Technology Sydney Australia Cognitive Computing Lab Baidu Research China

The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural *** poses crucial questions that, do all training data contribute to model's performance? How much does each individual training sample or a sub-training-set affect the model's generalization, and how to construct the smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance? To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization *** empirically observed generalization gap of dataset pruning is substantially consistent with our theoretical ***, the proposed method prunes 40% training examples on the CIFAR-10 dataset, halves the convergence time with only 1.3% test accuracy decrease, which is superior to previous score-based sample selection methods. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Deep learning

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Routing Algorithm Based on High Latency Communication Node Awareness in Dynamic Networks 1

Routing Algorithm Based on High Latency Communication Node A...

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1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Meng, Xiaolei Zhao, Zhen Xie, Wenhua Su, Bengan Zhao, Shujian Liu, Yang Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao China Qingdao Zichai Boyang Diesel Engine Co. Ltd Qingdao266700 China

ISBN: (纸本)9798350323795

Aiming at the instability of communication links caused by environmental interference, dynamic change of node location, and long distance between nodes in the dynamic networking process of mobile clusters, this paper proposes an algorithm for high delay communication node perception and routing in dynamic networking, predicts the nodes that may generate high communication delay, and optimizes the path selection between high delay nodes to prevent network congestion. First of all, according to the communication status in the time series, the BiLSTM algorithm is used to predict the attenuation trend of the signal with the increase of distance, and calculate the distance between nodes that can establish efficient communication;Then, based on elbow method and K-means algorithm, group is divided according to the distance that can establish efficient communication, critical nodes are identified, and cost capacity diagram is constructed for critical nodes;Finally, according to the network flow optimization algorithm with maximum cost and maximum flow constraint, the best matching link is calculated and mapped to the communication path between critical nodes. By planning the path selection strategy between nodes with high latency communication in the cluster, the problem of unstable communication in the cluster is solved with less system overhead and faster convergence speed. © 2023 IEEE.

关键词： Network topology

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Leak Detection and Classification of Water Pipeline Data Using LSTM Auto-Encoder with Xavier Initialization 8

Leak Detection and Classification of Water Pipeline Data Usi...

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8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Lee, Daehee Choo, Hyunseung Jeong, Jongpil Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Smart Factory Convergence Suwon Korea Republic of

ISBN: (纸本)9798350373615

Modern cities operate extensive water supply networks. In Korea, the water supply network coverage reaches 98.9%, but more than 10% of water is still leaking every year. Previously, leaks in water pipelines were detected using flow meters and pressure meters, but these methods could only detect leaks after they occurred, necessitating on-site investigations to accurately pinpoint the location of the leak. In the United States, aging water and sewage facilities incur economic losses of hundreds of billions of dollars every year. To address this issue, this paper proposes an LSTM (Long Short-Term Memory) Auto-Encoder model, enhanced with Xavier Initialization, that can detect leaks using water pipeline data and automatically identify the location and extent of the leakage. This model achieved 93.7% performance by applying the Xavier Initialization technique to the LSTM Auto-Encoder model and achieved relatively high accuracy compared to the model using only LSTM Auto-Encoder. The results of this paper can serve as an effective predictive tool for monitoring water pipeline leaks. © 2023 IEEE.

关键词： Leak detection

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Efficient Federated Learning with Cost-Adjustable Generative AI over Heterogeneous Edge Devices 20th

Efficient Federated Learning with Cost-Adjustable Generativ...

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Zhang, Hanwen Li, Peichun Kang, Jiawen Qian, Liping Wu, Yuan Niyato, Dusit State Key Laboratory of Internet of Things for Smart City University of Macau China School of Automation Guangdong University of Technology Guangzhou China College of Information Engineering Zhejiang University of Technology Hangzhou China Zhuhai UM Science and Technology Research Institute Zhuhai China Nanyang Technological University Block N4-02a-32 Nanyang Avenue Singapore Singapore

ISBN: (纸本)9789819628636

Federated learning (FL) on edge devices often faces critical challenges, such as slow convergence rates and suboptimal performance, largely attributed to limited local data availability and data heterogeneity across participating devices. Recent advancements in generative artificial intelligence (AI) have demonstrated exceptional proficiency in synthesizing realistic data, offering potential solutions to these issues. In this paper, we present an innovative approach that enables edge devices to enhance their datasets by leveraging generative AI models. Recognizing varying resource capacities among edge devices, we propose a flexible data synthesis strategy, allowing each device to customize the quantity and quality of generated data according to its specific resource conditions and operational requirements. Building on this strategy, we formulate an optimization problem to maximize training accuracy while meeting the local resource constraints and task completion timelines. Experimental results reveal that our model can effectively enhance global accuracy compared to conventional FL algorithms, with even more pronounced improvements observed under skewed data distributions. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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NHVC: Neural Holographic Video Compression with Scalable Architecture 31

NHVC: Neural Holographic Video Compression with Scalable Arc...

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IEEE conference on Virtual Reality and 3D User Interfaces (VR)

作者： Ban, Hyunmin Choi, Seungmi Cha, Jun Yeong Kim, Yeongwoong Kim, Hui Yong Kyung Hee Univ Seoul South Korea

ISBN: (纸本)9798350374025;9798350374032

Recently, neural network-based approaches for hologram generation and compression have gained popularity as they allow for efficient inference on GPUs without the need for iterative optimization required in traditional methods. In this paper, we introduce Neural Holographic Video Compression (NHVC), an end-to-end trainable and scalable model designed for high-quality phase hologram video generation and compression. NHVC consists of an auto-encoder-based phase hologram generator, a latent coder and- two hyper-prior coders. For each input image, the latent features are extracted through the encoder part of the phase generator and then entropy coded at the shared latent coder based on the hyper-prior information. The two hyper-prior coders employ a spatial and a spatio-temporal entropy model for I-frames and P-frames, respectively. With this architecture, our NHVC can offer task-scalability, allowing a single trained model to serve as a phase hologram generator, phase hologram image compressor, or phase hologram video compressor as required. Experimental results on phase hologram video compression with UVG dataset show that our model outperforms 'HoloNet + VVC' by 75.6% BD-Rate reduction, with modest 2K encoding and decoding speeds (5 fps and 12 fps, respectively). For the phase hologram video generation task, our model showed much higher-quality (almost 42dB PSNR) reconstruction using the UVG dataset, while the previous neural generation model HoloNet provides at most 36dB reconstruction quality. We also provide an extensive experimental study on several important design questions such as the need for quadruple extension (QE) in the neural compression model, the feasibility of motion estimation in the phase domain, and an alternative, the need for increasing receptive field to learn better phase features, and variable rate support with a single trained model. It is noteworthy that our model is the first and best neural phase video compression model providing such high

关键词： computing methodologies Computer graphics Image manipulation Image processing computing methodologies Computer graphics Image compression

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