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A distributed 3D UAV Placement Algorithm for Integrated Ground-Air Cellular Networks

A Distributed 3D UAV Placement Algorithm for Integrated Grou...

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international conference on Big Data computing and Communications (BIGCOM)

作者： Xinwei Wang Ye Liu Shan Chang Computer Science and Technology Donghua University Shanghai China

the problem of deploying unmanned aerial vehicles (UAVs) to form a wireless cellular network has gained much attention. Most of the recent works either need to know the locations of users or to make decision in a centralized way. In this paper, we design a distributed learning-based 3D UAV placement approach, named SDQ-H. Given a set of UAVs, SDQ-H builds an integrated ground-air cellular network to maximize the number of ground users (GUs) covered, without requiring knowledge of geographic locations of GUs. SDQ-H decouples the 3D UAV placement problem into two sub-optimal optimization problems. First, we utilize a Q-learning algorithm to optimize UAV positions in 2D space, i.e., in longitude and latitude. UAVs are modeled as agents, each of which takes action independently according to its position and reward. then, we finetune the position of UAVs in the third dimension, i.e., height. the above two optimization processes are carried out alternately until convergence. We conduct simulations on several different user distributions. Simulation results demonstrate the superiority of SDQ-H.

关键词： Cellular networks Wireless communication Knowledge engineering three-dimensional displays Q-learning Simulation Autonomous aerial vehicles

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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model

Aware: Adaptive Distributed Training with Computation, Commu...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yan Zeng Guangzheng Yi Yuyu Yin Jiyang Wu Meiting Xue Jilin Zhang Jian Wan Yunquan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

the accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. this paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. this paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. the results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence.

关键词： Deep learning Training Adaptation models Runtime Computational modeling Neural networks Reinforcement learning

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A Trust Level Evaluation Method for Intelligent Numerical Control Systems Driven by Edge Intelligence

A Trust Level Evaluation Method for Intelligent Numerical Co...

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international conference on Computer and Communications (ICCC)

作者： Zheng Zhou Dong Yu Yi Hu Lipeng Zhang Wuwei He Haoyu Yu University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Chinese Acadamy of Sciences Shenyang P.R. China Shenyang Institute of Computing Technology Chinese Acadamy of Sciences Shenyang P.R. China Digital Workshop Department Shenyang CASNC Technology Co.Ltd Shenyang P.R. China

With the increase in the number of networking devices in the industrial field, the total amount of data increases, resulting in huge broadband pressure and power consumption, making the traditional centralized processing outdated, and giving rise to the development of edge computing in the industrial field. To ensure the security of the overall network, the trust concept of the access terminal computer numerical control (CNC) system is introduced as an important basis for improving the manufacturing capability of CNC machine tools, so the inclusion of computing power and trust mechanism in the intelligent CNC system is a good solution to the lack of computing and analysis resources and the ability to reduce the security risks of the CNC system. In this paper, we mainly design a trust level evaluation method for the intelligent CNC system based on edge intelligence, which incorporates the trust evaluation in the edge computing environment, and effective plans and coordinates the cloud, edge, and final resources in the intelligent CNC system. We designed the trust degree model evaluation and constructed the security trust level of the CNC intelligent system with the collaboration between the cloud and the edge. Finally, the experimental scheme is validated and tested in a real environment.

关键词： Power demand Computational modeling Collaboration Numerical models Manufacturing Machine tools Computer numerical control

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Weak Network Oriented Mobile distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xinlei Wei Yujuan Tan Duo Liu Daitao Wu Yu Wu Xianzhang Chen Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Node failure is one of the most typical issues in distributed storage systems. the classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. the traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%.

关键词： Data centers Fault tolerant systems Redundancy distributed databases Memory Bandwidth Maintenance engineering

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Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

Scientific and Technological News Recommendation Based on Kn...

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IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Yuyao Zeng Junping Du Zhe Xue Ang Li School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456579

Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. the propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. the contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. the collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation.

关键词： Cloud computing Social networking (online) Collaboration Information filters Encoding Behavioral sciences Intelligent systems

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the Detection of Fake News through the use of a Hierarchical Convolutional Attention Network

The Detection of Fake News through the use of a Hierarchical...

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international conference for Convergence of Technology (I2CT)

作者： Chameera De Silva thilina Halloluwa School of Computing University of Colombo Colombo Sri Lanka

In the current digital era, the popularity of social media platforms has expanded tremendously. Every one of us, no matter what age or gender or class we belong to, are active on social media, sharing so much of our personal and professional life with the world. We are connected with people, countries and borders apart via our phone now. News companies are also recognizing this change in the world and are now making business through this by publishing information online. the rapid spread of an article on the internet by the normal public without checking for its authenticity is resulting in a surge in the amount of incorrect information available online. No end-user finds it simple to confirm the veracity of a given news article. these fake articles are increasing rapidly and making it far more difficult for the real information to reach the public. the purpose of this work is to develop an efficient model for assessing the credibility of a news article. this work addresses the classification problem of Fake News, given just the news article's content and the author's name. the system proposed in this paper works with Hierarchical Convolutional-Attention Network (HCAN) and Convolutional Neural Network to classify the fake news articles based on the words and phrases used in the published piece. Hierarchical Convolutional-Attention Network (HCAN) is comprised of attention-enhanced word-level and sentence-level encoders as well as a CNN to capture sequential correlation. We have also used Recurrent Neural Network (RNN) for word encoder and sentence encoder in our system. We carried out the experiment on our system using a dataset and compared the results with various baseline models like CNN, RCNN, BERT or LR. Extensive trials demonstrate that HCAN outperforms the most advanced baseline models on a collected dataset. Results of our experiment shows that our model is achieving the F1 score of 0.98.

关键词： Recurrent neural networks Correlation Social networking (online) Publishing Bit error rate Companies Convolutional neural networks

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Predictive Modelling for Lung Cancer Detection using Machine Learning Techniques

Predictive Modelling for Lung Cancer Detection using Machine...

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international conference on Communication and Electronics Systems (ICCES)

作者： B. Yamini K. Sudha M. Nalini G. Kavitha R. Siva Subramanian R. Sugumar Dept of Networking and Communications School of Computing College of Engg and Tech SRM Institute of Science and Tech Dept of CSBS R.M.D Engineering College Dept of CSE S.A.Engineering College Dept of CSE Rajalakshmi Insitute of Technology Dept of CSE R.M.K.College of Engg and Tech Institute of CSE Saveetha School of Engineering Saveetha Institute of Medical & Technical Sciences Chennai

Lung cancer (LC), generally called lung carcinoma, is a fatal and debilitating illness that affects the lungs. Lung cancer is caused by the genetic damage that occurs to the DNA of the cell as a result of numerous environmental factors. there are no common signs that can be used to determine if a man or a woman has lung cancer unless the illness has progressed to an advanced stage or a lung screening method has been utilized. Early detection and intervention will help save lives from the lung cancer epidemic. Based on this research problem, lung cancer prediction is investigated and tested. Several ML approaches are considered to the study of lung cancer. ML is an area of AI that seeks to generate predictions based on historical and past data. the experimental technique utilizes lung cancer data collected from the public respiratory system. Lung cancer datasets are analyzed using LR, DT, RF, SVM, XGB classifier, Gradient Boosting, and KNN models. Four distinct measures (accuracy, sensitivity, precision, and F1-score) are used to project and compare the experimental outcomes acquired by ML models: In relation Compared to other ML models such as LR, DT, RF, SVM, Gradient Boosting, & KNN, the experimental findings demonstrate that the XGB classifier provides accurate predictions. Finally from all parameter aspects, it clearly shows XGB classifier good prediction results compare to other ML models like LR, DT, RF, SVM, Gradient Boosting, and KNN.

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. the current centralized energy trading system is less secure and involves large transmission losses and high overheads. the decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. this is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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A Secure and Reliable Construction Scheme for Enterprise Value Chain Networks

A Secure and Reliable Construction Scheme for Enterprise Val...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Chaoxia Qin Bing Guo Yuting Zhao Yan Shen College of Computer Science Sichuan University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

A value chain optimizes the economic benefit of an enterprise alliance by integrating the cooperation relationships among the upstream and downstream companies. Nevertheless, the absence of information security mechanisms within the value chain limits the horizontal cooperation between competing enterprises. the paper proposes a scheme for building an enterprise value chain network based on an alliance blockchain. In the scheme, a distributed authentication technology and cross-chain transaction technology are introduced into the value chain, which enhances the security and reliability of value chain information and facilitates horizontal cooperation among companies. Using Hyperledger Fabric platform, we simulate and extensively evaluate an enterprise value chain network. Experimental results demonstrate the feasibility of the proposed scheme, which can serve as a guide for the construction of enterprise value chain networks in the real world.

关键词： Economics distributed ledger Buildings Authentication Information security Focusing Companies

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Smart street design for managing crowds in Hajj 8

Smart street design for managing crowds in Hajj

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Abi Sen, Adnan Ahmed Alsaawy, Yazed Alkhodre, Ahmad Bahbouh, Nour Mahmoud Alhaboob, Mohamed Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia Granada University Department of Information and Communication Sciences Granada Spain College of Computing and Information Technology Islamic University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544434

Hajj is an annual pilgrimage to Makkah in Saudi Arabia where over two and a half million pilgrims attend. It is one of the most intense crowded events in the world. Unfortunately, Hajj has witnessed several stampedes, fires and other disasters, which have led to the death of thousands of people. In recent years, the hajj management has made several improvements in the infrastructure, which has helped ease congestion in crowded rituals. However, despite the great development and huge contributions in improving the infrastructure of the facilities, the problem of crowd control and congestion still remains a real challenge to the management. Although crowd management during hajj is exemplary, it still remains largely manual. this research proposes an innovative solution by designing digital smart streets based on cheap LED-light screens, and control algorithm in addition to distributed fog nodes, wireless network sensors, and servers for the main computing and management. the proposed system will facilitate the process of controlling crowds, and the way it will function with the help of special signals and colors. these signs and instructions can be quickly and easily followed and adopted by people within the crowd. In this way, crowd management would be able to provide a prompt response to the requests and alerts from the central command. We use many techniques to detect the issues of crowds and locate places of interest based on the fog nodes and digital street processing. A prototype of the proposed system has been simulated to demonstrate its feasibility and ease of application to determine the benefits and features that it is capable to achieve, if implemented in crowded areas during the course of the Hajj. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Crowd management Digital street Flow control Hajj Smart street

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