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An Asynchronous Data Transmission Policy for Task Offloading in Edge-computing Enabled Ultra-Dense IoT

Computers, Materials & Continua 2024年第12期81卷 4465-4483页

作者： Dayong Wang Kamalrulnizam Bin Abu Bakar Babangida Isyaku Liping Lei Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310JohorMalaysia Department of Computer Science Faculty of Information Communication TechnologySule Lamido UniversityKafin Hausa741103JigawaNigeria School of Political Science and Public Administration East China University of Political Science and LawShanghai201620China

In recent years,task offloading and its scheduling optimization have emerged as widely discussed and signif-icant *** multi-objective optimization problems inherent in this domain,particularly those related to resource allocation,have been extensively ***,existing studies predominantly focus on matching suitable computational resources for task offloading requests,often overlooking the optimization of the task data transmission *** inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network,resulting in increased service times due to elevated network transmission latencies and idle computational *** address this gap,we propose an Asynchronous Data Transmission Policy(ADTP)for optimizing data transmission for task offloading in edge-computing enabled ultra-dense *** dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT *** contrast to existing methods,the Deep Deterministic Policy Gradient(DDPG)based task data transmission scheduling module works asynchronously with the Deep Q-Network(DQN)based Virtual Machine(VM)selection module in *** significantly reduces the computational space required for the scheduling *** continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth *** reduces the task completion time and minimizes the failure rate caused by ***,the VM selection module only performs the next inference step when a new task arrives or when a task finishes its *** a result,the wastage of computational resources is further *** simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11%and 8.09%,***,the tas

关键词： Bandwidth allocation edge computing internet of things task offloading reinforcement learning

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An Effective Analysis of Proficient Two Level Security Contraptions for Loading Data in Cloud 1

An Effective Analysis of Proficient Two Level Security Contr...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Arulkumaran, G. Jayagopalan, Santhosh Thanga Mariappan, L. Balamurugan, P. School of Computing and Information Technology REVA University Bengaluru India British Applied College Department of Computing Umm Al Quwain United Arab Emirates School of Information Technology and Engineering Vellore Institute of Technology Vellore Department of Smart Computing Vellore Dist Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Chengalpattu Dist Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350345957

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time. © 2023 IEEE.

关键词： Cryptography

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Detection of Face Mask and Social Distance Calculation Using Deep Learning 9

Detection of Face Mask and Social Distance Calculation Using...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sudhakaran, Pradeep Sindhu, V. Gupta, Sarthak Preetha, M. School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India Sri Krishna College of Engineering And Technology Department of Information Technology Tamil Nadu Coimbatore India Srm Institute of Science And Technology Career Development Centre Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365092

The COVID 19 epidemic is causing a worldwide health disaster. The epidemic caused governments all over the world to institute lockdowns in order to limit viral spread. According to study data, wearing a face mask and keeping social distance in public places significantly reduces the risk of transmission. The project's ultimate goal is the development of a real-time, very accurate system that can detect any non-masked faces anywhere and compulsively require people to wear masks in the interest of public health. The proposed system combines state-of-the-art deep learning algorithms with geometric methods to provide a reliable modal that is effective at detection, tracking, and validation. Consequently, the proposed method is helpful to society because it reduces the spread of the corona virus while saving time. In light of the current situation, where the lockdown is being removed to permit inspections at public gatherings, retail malls, and other areas, this may prove to be effective. Automated inspection saves resources since it reduces the need for human inspectors. In addition, it is applicable in any circumstance. © 2024 IEEE.

关键词： Learning algorithms

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Loss Analysis of the Electromagnetic Metamaterial Applied to Antenna Size Reductions 5

Loss Analysis of the Electromagnetic Metamaterial Applied to...

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2023 5th International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2023

作者： Pang, Xudong Chen, Qun Cao, Beibei Zhu, Shouzheng Department of Computing Center Shanghai Publishing and Printing College Shanghai China School of Information Science and Technology East China Normal University Shanghai China

The research of transformation electromagnetics uses spatial coordinate transformation method to transform Maxwell's equations for the propagations of electromagnetic waves in space, thereby achieving the goal of designing the path of electromagnetic waves, which eventually leads to a more complex tensor design of its constitutive parameters of metamaterials. This demand for artificial design of material parameters has been combined with the rapid development of micro-scale, molecular-scale, and atomic-scale experimental engineering techniques in recent years, enabling metamaterials to be developed, fabricated, and applied in various frontier fields. In this paper, the metamaterial electromagnetic wave concentrator used to reduce the antenna size is mainly discussed and detailed simulations and researches on the electromagnetic lossy conditions of the metamaterial parameters are given, which can better verify the electromagnetic scattering performance under actual engineering conditions. Solutions and quantitative simulations for the attenuation characteristics are also provided, which can deepen the research on the engineering performance of the metamaterial electromagnetic transformation devices. © Published under licence by IOP Publishing Ltd.

关键词： Metamaterials

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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Enhancing Effective Spectrum Utilization in 6G Cellular Networks via Sharing Algorithms 5th

Enhancing Effective Spectrum Utilization in 6G Cellular Netw...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： kudari, Jayashree M. Garg, Aanchal Sridevi, S. Savita Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The emerging 6G mobile network technology presents an opportunity to noticeably increase spectrum utilization in comparison to previous generations. This paper introduces a singular sharing algorithm to correctly utilize the spectrum in 6G mobile networks. This set of rules leverages device mastering techniques to dynamically calculate a charge primarily based at the average channel usage and bandwidth usage through distinct users. Moreover, it gives a dispensed mechanism to define the resource allocation method used in different communique situations. The proposed set of rules is powerful, reliable, and cozy, and it is able to efficaciously apply spectrum sharing amongst unique users to maximize spectral performance. Simulation results display that the proposed set of rules achieves giant improvements in spectral efficiency as compared to both a traditional unmarried-mobile method, and a present sharing algorithm proposed in the literature. Finally, experimental results are supplied, which demonstrate the effectiveness of the proposed set of rules in real-world check setups. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless networks

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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Computer Modeling in Engineering & sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Game Theoretical Distributed Task Offloading for Tethered Aerial Vehicles Communication Network 4

Game Theoretical Distributed Task Offloading for Tethered Ae...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Yang, Zhaohui Huang, Fengling Su, Xuqi Zhou, Pingfang Guo, Fusen Jing, Yuhao School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Engineering and Information Technology University of New South Wales Canberra Australia

ISBN: (纸本)9798350395914

This paper introduces a game-theoretic framework aimed at enhancing offloading decisions within a network of multiple tethered aerial vehicles (TAVs) employed as communication base stations and relay platforms. Given their advantageous low deployment costs and extensive coverage areas, TAVs represent an innovative solution for dynamic air-ground communication networks. We address the inherent challenges associated with the mobility of ground users and consider across multiple temporal intervals. We initially segment offloading decisions into discrete time slots and model them within a strategic game principle. Subsequently, we prove that this game constitutes an exact potential game and introduce a distributed algorithm based on best response iteration. This algorithm is designed for decentralized deployment across individual tethered aerial vehicles. Through simulation studies, we demonstrate that our proposed methodology enhances network throughput relative to conventional random offloading strategies. It advances the theoretical understanding of offloading in dynamic network environments and offers practical algorithms for real-world applications. © 2024 IEEE.

关键词： Antenna grounds

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