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IEEE ACCESS SPECIAL SECTION EDITORIAL: BIG data TECHNOLOGY AND APPLICATIONS IN intelligent TRANSPORTATION

IEEE ACCESS 2020年 8卷 201331-201344页

作者： Mohammed, Sabah Arabnia, Hamid R. Qu, Xiaobo Zhang, Dalin Kim, Tai-Hoon Zhao, Jiandong Lakehead Univ Dept Comp Sci Thunder Bay ON P7B 5E1 Canada Univ Georgia Dept Comp Sci Athens GA 30602 USA Chalmers Univ Technol Dept Architecture & Civil Engn S-41296 Gothenburg Sweden Beijing Jiaotong Univ Natl Res Ctr Railway Safety Assessment Beijing 100044 Peoples R China Univ Tasmania Dept Comp Sci Hobart Tas 7005 Australia Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture.

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intelligent Traffic Management System for smart Cities 1st

Intelligent Traffic Management System for Smart Cities

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1st international conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2018

作者： Khanna, Abhirup Goyal, Rohit Verma, Manju Joshi, Deepika The University of Melbourne Melbourne Australia Himgiri ZEE University Dehradun India

ISBN: (纸本)9789811338038

In present-day times, the number of vehicles has increased drastically, but in contrast, the capabilities of our roads and transportation systems still remain underdeveloped and as a result, fail to cope with this upsurge in the number of vehicles. As a consequence, traffic jamming, road accidents, increase in pollution levels are some of the common traits that can be observed in our new age cities. With the emergence of the Internet of Things and its applicability in smart Cities, creates a perfect platform for addressing traffic-related issues, thus leading to the establishment of intelligent Traffic Management Systems (ITMS). The work presented in this paper talks about an intelligent traffic management system that lays its foundation on Cloud computing, Internet of Things and data analytics. Our proposed system helps to resolve the numerous challenges being faced by traffic management authorities, in terms of predicting an optimum route, reducing average waiting time, traffic congestion, travel cost and the extent of air pollution. The system aims at using machine learning algorithms for predicting optimum routes based upon traffic mobilization patterns, vehicle categorization, accident occurrences and levels of precipitation. Finally, the system comes up with the concept of a green corridor, wherein emergency services are allowed to travel without facing any kinds of traffic congestion. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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Cartoon Image Design System Based on Genetic Optimization Algorithm

Cartoon Image Design System Based on Genetic Optimization Al...

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international conference on smart Grid and Electrical Automation (ICSGEA)

作者： Yan Wu Academy of Fine Arts Henan University Kaifeng China

ISBN: (数字)9781728182780

ISBN: (纸本)9781728182797

In this paper, evolutionary computing technology is applied to the innovative design of animation image, and a new method of computer-aided generation of 3D animation character image is proposed. Then an intelligent 3DMAX system of animation character image design based on NURBS is established. Aiming at the problem of quickly generating multiple models from one made model, and ensuring that the appearance of each model is not exactly the same, we study the method of NURBS modeling, and put forward the method of group generation of NURBS model based on genetic algorithm optimization. At last, we use such method to generate a large number of cartoon models in Maya to make clustering animation, and to detect the effect of clustering algorithm and following leader algorithm. The experimental data show that the fitting accuracy of NURBS curve has been greatly improved, and the shortcomings of low efficiency and low simulation degree encountered in group character modeling can be solved.

关键词： Radio frequency Zirconium smart grids Hafnium compounds conferences Automation

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An MQTT-based Resource Management Framework for Edge computing Systems

An MQTT-based Resource Management Framework for Edge Computi...

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IEEE international Symposium on Innovations in intelligent Systems and Applications (INISTA)

作者： Saša Pešić Miloš Radovanović Mirjana Ivanović University of Novi Sad Faculty of Sciences

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

The complexity of IoT systems and tasks that are put before them require shifts in the way resources and service provisioning are managed. The concept of edge computing is introduced to enhance IoT systems' scalability, reactivity, efficiency, and privacy. In this paper, we present an edge computing solution for resource management of context-aware decision-making processes distributed between IoT gateways. The solution performs decision-making process management for smart actuation, based on analysis of sensory data streams, and context-informed edge computing resource and service provisioning management based on topology and operational changes. Our architectural solution showcases the first version of a Resource Management Framework - a generic framework for software resource orchestration best-suited to IoT platforms with event-driven, publish-subscribe communication mechanisms. Proof of concept experiments that are executed in a simulated edge computing testbed validate our solution's performance in improving the resilience and responsiveness of the edge computing system when there are operational and topology changes. Furthermore, the framework addresses the recovery of failed decision-making processes, impacting the overall health of the underlying IoT system.

关键词： Software Resource management Edge computing Computer architecture Sensor systems Cloud computing

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Semantic Descriptor for Intelligence Services

Semantic Descriptor for Intelligence Services

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Edgar Ramos Timon Schneider Marie-J. Montpetit Ben De Meester Ericsson Research Finland Aalto University Finland Concordia University Montreal Canada IDLab - imec Ghent University Ghent Belgium

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

The exposition and discovery of intelligence especially for connected devices and autonomous systems have become an important area of the research towards an all-intelligent world. In this article, it a semantic description of functions is proposed and used to provide intelligence services mainly for networked devices. The semantic descriptors aim to provide interoperability between multiple domains' vocabularies, data models, and ontologies, so that device applications become able to deploy them autonomously once they are onboarded in the device or system platform. The proposed framework supports the discovery, onboarding, and updating of the services by providing descriptions of their execution environment, software dependencies, policies and data inputs required, as well as the outputs produced, to enable application decoupling from the AI functions.

关键词： Artificial intelligence Semantics data models Biological system modeling Interoperability Hardware Couplings

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Precise Identification of Rehabilitation Actions using AI based Strategy

Precise Identification of Rehabilitation Actions using AI ba...

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international conference on Computer Communications and Networks (ICCCN)

作者： Min Lei Peng Liu Qingshan Wang Qi Wang School of Computer Science and Technology Hangzhou Dianzi University China School of Mathematics Hefei University of Technology China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

With the development of microelectronics and sensor technologies, there are more and more researchers applying them to human action recognition, most of which are professional motion and rely on specific-designed sensors and wearable de-vices. Meanwhile, the need of rehabilitation training is increasing due to occupational diseases, bad life-style and incorrect exercise habit. However, it is costly and inconvenient to train in clinics and hospitals. To buy or borrow a set of medical training equipment is also unpractical. In this paper, we propose to use smart phones, which have larger computing power and are equipped with richer sensors ever than before, to run artificial intelligence based models and algorithms for identification of rehabilitation actions. Beyond doubt, it will be more convenient to use smart phones instead of professional equipments. Nevertheless, there are still some challenges which prevent it from being put into practice, such as phone deployment, data collection, and model training. We initially conceptualize and implement a smart phone-based accuracy judgment system for rehabilitation action. According to the characteristics of the system, e.g., sensor difference, position variation, and computing power limitation, a supervised and data-sharing learning algorithm is proposed, the operation framework, loss function and regular expression function are carefully selected. The experiment on a prototype of the system verifies that the proposed method precisely identifies the rehabilitation actions of testees.

关键词： Training Biomedical monitoring smart phones Ellipsoids intelligent sensors

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Analysis of Machine Learning Algorithms in smart Manufacturing

Analysis of Machine Learning Algorithms in Smart Manufacturi...

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international conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Dulini Ratnayake Pooja Lohit Bhopendra Singh Ved P Mishra Amity University Dubai UAE

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170176

Digital twins, block chains, Internet of things (IoT) and artificial intelligence (AI) may re-define our imagination and future vision of globalization. Digital twins will likely affect most of the worldwide enterprises as it duplicates the physical models for remote monitoring, viewing and controlling based on numerous algorithms and its digital format. It is the digitalized living model of the physical system that continuously adapts to operational variations based on real-time data from numerous IoT sensors and devices and provides future forecasts corresponding to the physical counterparts with the help of machine learning and IoT. We have investigated the related research that is progressing in this field. In the context of IoT Cyber Physical Systems (CPSs), the major research areas such as machine learning and big data are examined. Machine learning facilitates the development of new models and technology systems in the domain of intelligent smart manufacturing.

关键词： Cloud computing Machine learning Computational modeling Sensors Industries Optimization Internet of Things

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Simulation of a server of multi-agents’s interactions inside a defined environment

Simulation of a server of multi-agents’s interactions insid...

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international conference on intelligent computing in data Sciences (ICDS)

作者： Ikram SABER Wadie BENDALI Rachid EL BACHTIRI Mohammed BOUSSETTA Karima EL HAMMOUMI Fatima CHEDDADI CED STI FST TSI Laboratory EST-USMBA Fez Morocco TSI Laboratory EST-USMBA Fez Morocco REPEER Group LESSI Laboratory ESTF FSDM USMBA University Fez Morocco TSI Laboratory FSTF USMBA University Fez Morocco

ISBN: (数字)9781728180847

ISBN: (纸本)9781728180854

smart grids are one of the best solutions for the integration of different distributed energy sources (DES), load, and storage elements. However, optimizing and managing these systems is a big challenge because it is only done in a distributed way. One of the best methods of managing a smart grid is the use of artificial intelligence (AI), especially multi-agent systems (MAS) approaches. MASs present a comprise of two or more independent agents with some information to achieve a set target that is capable of achieving multiple real-time objectives easily. Several multiagent approaches have been proposed these last years, This work constitutes a demonstration of one of those methods which is the contract net protocol (CNP). The CNP is a technique invented to solve communication problems in distributed networks. It was defined as a high-level protocol for communication among the nodes in distributed problem solver. We will use the extended version of the contract net protocol(ECNP) to optimize the movement of MASs inside a smart grid network. Our example of a smart grid will be a grid constituted from nodes, and each node is a location of a task or an agent. In the scenario each task it's given a target which is also a node inside the grid, and our main objective is that the agent or agents could contribute for each task, depending on their priority order and may allow it to reach its target, which is another node in the network far from its initial location, using the shortest way as possible. A first scenario is used that includes a certain number of tasks and agents, to demonstrate the efficiency and power of the ECNP (extended contract net protocol), and the role of MAS in distributed management of smart grids.

关键词： Microgrids Task analysis Protocols Contracts smart grids Laboratories Job shop scheduling

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Security Challenges and Solutions for 5G HetNet

Security Challenges and Solutions for 5G HetNet

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Aakanksha Sharma Venki Balasubramanian Alireza Jolfaei School of Engineering Information Technology and Physical Science Federation University Australia Ballarat Australia School of Engineering Information Technology and Physical Science Federation University Australia Ballarat VIC Australia Department of Computing Macquarie University Sydney Australia

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The exponential growth of smartphones and other smart communicating devices has led to the proliferation of the Internet of Things (IoT) applications. Literature shows, one person will have more than six intelligent connected devices in future. The existing network infrastructure and bandwidth will be unable to accommodate the growing number of smart connected devices, therefore, achieving the expected Quality of Service (QoS) and Quality of Experience (QoE) remains a challenge. The advent and deployment of 5G network bring a massive number of innovative network services and exceptional user experience by providing superior data rates. Despite numerous benefits that 5G offers, the security and privacy in 5G is a challenge due to the existing large number of heterogeneous networks (HetNet). To harvest the numerous benefits of 5G, it is imperative to provide adequate protection mechanisms to maintain the user and data privacy in growing HetNet. This article comprehensively addresses the existing security issues in 5G HetNet and solutions for the identified problems in the HetNet edge.

关键词： Privacy 5G mobile communication Quality of service User experience Security Quality of experience Internet of Things

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Tiny Neural Networks for Environmental Predictions: An Integrated Approach with Miosix

Tiny Neural Networks for Environmental Predictions: An Integ...

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international conference on smart computing (smartCOMP)

作者： Francesco Alongi Nicolò Ghielmetti Danilo Pau Federico Terraneo William Fornaciari DEIB Informazione e Bioingegneria Politecnico di Milano Milan Italy System Research and Applications STMicroelectronics Italy

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

Collecting vast amount of data and performing complex calculations to feed modern Numerical Weather Prediction (NWP) algorithms require to centralize intelligence into some of the most powerful energy and resource hungry supercomputers in the world. This is due to the chaotic complex nature of the atmosphere which interpretation require virtually unlimited computing and storage resources. With Machine Learning (ML) techniques, a statistical approach can be designed in order to perform weather forecasting activity. Moreover, the recently growing interest in Edge computing Tiny intelligent architectures is proposing a shift towards the deployment of ML algorithms on Tiny Embedded Systems (ES). This paper describes how Deep but Tiny Neural Networks (DTNN) can be designed to be parsimonious and can be automatically converted into a STM32 microcontroller-optimized C-library through X-CUBE-AI toolchain; we propose the integration of the obtained library with Miosix, a Real Time Operating System (RTOS) tailored for resource constrained and tiny processors, which is an enabling factor for system scalability and multi tasking. With our experiments we demonstrate that it is possible to deploy a DTNN, with a FLASH and RAM occupation of 45,5 KByte and 480 Byte respectively, for atmospheric pressure forecasting in an affordable cost effective system. We deployed the system in a real context, obtaining the same prediction quality as the same DNN model deployed on the cloud but with the advantage of processing all the necessary data to perform the prediction close to environmental sensors, avoiding raw data traffic to the cloud.

关键词： Cloud computing Machine learning algorithms Atmospheric modeling Neural networks Weather forecasting Supercomputers Task analysis

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