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8th Competitive Advantage in the Digital Economy, CADE 2022

作者： Babar, Kainat Shah, Munam Ali Department of Computer Science COMSATS University Islamabad Islamabad Pakistan

ISBN: (数字)9781839537424

ISBN: (纸本)9781839537998

internet-connected wearables are used in smart health paradigms such as the internet of Health Things (IoHT) for telemonitoring, diagnostics, and offering low-cost healthcare solutions. Mist computing brings a significant revolution in computer technologies. Instead of delegating control to gateways during data transmission, control is decentralised to end nodes, which reduces the network's communicational delay and thus increases throughput and helps in conserving bandwidth and battery power. To address latency-related issues and for increasing the efficiency of smart healthcare systems, this paper critically reviews existing techniques and proposes a heterogeneous approach to mist computing along with other cloud and fog computing environments. We use a delay-based KNN algorithm for IoHT. This helps achieve high quality of service and reduces latency and packet drop rate to a great extent, which is necessary for improving existing e-healthcare systems. © 2022 IET conference Proceedings. All rights reserved.

关键词： Fog computing

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Not a Trade-Off: On the Wi-Fi Energy Efficiency of Effective internet Congestion control 17

Not a Trade-Off: On the Wi-Fi Energy Efficiency of Effective...

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17th conference on Wireless On-Demand network systems and Services (WONS)

作者： Welzl, Michael Univ Oslo Oslo Norway

ISBN: (纸本)9783903176461

It is common to regard performance and energy efficiency as a trade-off, and this is a characteristic of many proposed solutions for energy efficiency. This is disadvantageous, as it is tempting for administrators or end users to disable energy conserving mechanisms when they have a performance cost. In contrast, this article makes the point that improvements in internet congestion control can be inherently energy-efficient: for example, minimizing the Flow Completion Time (FCT) of data transfers, one of the most common goals in congestion control, can significantly reduce the energy usage of a Wi-Fi receiver.

关键词： Wi-Fi Energy Efficiency Congestion control TCP

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Intelligent Live Video Streaming for Object Detection 23

Intelligent Live Video Streaming for Object Detection

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23rd IEEE International conference on High performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Mingkang Sun, Jingtao Aida, Kento Figueiredo, Renato J. Ku, Yun-Jung Subratie, Kensworth The Graduate University for Advanced Studies Sokendai Department of Informatics Tokyo Japan National Institute of Informatics Tokyo Japan University of Florida Electrical and Computer Engineering Florida United States

ISBN: (纸本)9781665494571

These days, sensors and cameras are being deployed on an increasingly large scale. Furthermore, the rapid development of machine learning models for computer vision now presents novel opportunities for the use of artificial intelligence (AI) and internet of Things (IoT) combinations in various application scenarios. However, challenges remain in supporting low-latency video streaming from distributed mobile IoT devices under dynamic network environments, and overcoming video data quality degradation that results from weather 'noise', which reduces the accuracy of AI-based data analyses such as object detection. In this paper, we propose a live video stream processing system for supporting intelligent services that integrates the following features. First, to cope with dynamic networks and achieve low latency, our approach employs a peer-to-peer (P2P)-based virtual network at the edge and a multi-tiered architecture composed of IoT cameras, edge, and cloud servers. Second, we construct a flexible messaging system for video analysis built upon SINETStream, which is a messaging system that adopts a topic-based pub/sub model. Third, we implement a framework that can remove weather-related (rain, snow, and fog) noise by applying weather classification and adaptive noise removal models that improve the accuracy of video analysis from data collected outdoors. The latency, throughput, and image quality benchmark experiments conducted to validate the feasibility of our proposed system showed that the process resulted in image quality improvements of approximately 30% (on average). © 2021 IEEE.

关键词： Image quality Adaptation models Image edge detection Object detection Streaming media Throughput internet of Things

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A Secure Integrated Framework for Fog-Assisted internet-of-Things systems

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IEEE internet OF THINGS JOURNAL 2021年第8期8卷 6840-6852页

作者： Junejo, Aisha Kanwal Komninos, Nikos McCann, Julie A. Imperial Coll London Fac Engn Dept Comp Adapt Emergent Syst Engn Grp London SW7 2AZ England City Univ London Sch Math Comp Sci & Engn Dept Comp Sci London EC1V 0HB England

Fog-assisted internet-of-Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely, blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this article proposes a framework featuring attribute-based access control and trust-based behavioral monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities' performance using a trust model based on a set of quality of Service (QoS) and network communication features. Subsequently, trust is embedded as an attribute within SC's access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely, Denial of Service (DoS), Distributed DoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework's design principles. The framework is evaluated on a Raspberry Pi 3 Model B+ to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.

关键词： Security internet of Things Computational modeling Cloud computing Authentication quality of service Protocols Fog computing internet of Things (IoT) security trust and credibility

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An Innovation of Distributed Scheduling and QoS Localized Routing Scheme for Wireless Industrial Sensor network 2

An Innovation of Distributed Scheduling and QoS Localized Ro...

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2nd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Ravishankar, S. Kanniga, Devi Department of Electronics and Communication Engineering Bangalore India Bangalore India

ISBN: (纸本)9798350347456

A novel strategy for ensuring reliable and effective communication between sensor nodes and the industrial control systems is the QoS (quality of Service) Localized Routing scheme for Wireless Industrial Sensor networks. This plan is built on the idea of localized routing, a routing technique based on the network's local topology. This system enables effective network performance monitoring and may be used to give QoS assurances to apps that need them. In order to regulate the network, centralized scheduling methods rely on a centralized controller. This controller is responsible for making decisions about the scheduling of resources, such as frequency allocation and transmission power. These algorithms offer a number of advantages, such as scalability, reliability, and security. However, they can be difficult to implement in large networks and may require significant amounts of communication and computation resources. Distributed scheduling algorithms, on the other hand, do not rely on a centralized controller. Instead, they use distributed algorithms to make scheduling decisions. These algorithms are typically more efficient and require less communication and computation resources. They are also better suited to large networks, since they do not require a centralized controller © 2023 IEEE.

关键词： Sensor nodes

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Towards Reinforcement Learning control of an Electromechanical Pinball Machine 1

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International Congress and Workshop on Industrial Artificial Intelligence (IAI)

作者： Alpen, Mirco Herzig, Sven Horn, Joachim Helmut Schmidt Univ Holstenhofweg 85 D-22043 Hamburg Germany

ISBN: (数字)9783030936396

ISBN: (纸本)9783030936396;9783030936389

In recent years, the application of artificial intelligence in production processes is becoming increasingly important. Initially focused on the quality management process, artificial intelligence has experienced more usage in production control processes and other complex dynamic systems. In addition to learning speed, the robustness of the resulting algorithm is an important quality criterion. This paper describes the steps towards a reinforcement learning trained Q-network that operates an electromechanical pinball machine. The aim is that the algorithm generates inputs for the system such that the resulting playing time and the associated point gain is maximized and shows a superior performance compared to a human player. This paper presents the planned learning concept and contains exemplary measurements to determine transition probabilities. Based on these results a Monte Carlo simulation should be used to train the Q-network and thus be able to get an optimal initialization for the practical realization.

关键词： AI Reinforcement learning Q-learning Discrete systems

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Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 140-153页

作者： Yan, Binghao Liu, Qinrang Shen, JianLiang Liang, Dong Informat Engn Univ Zhengzhou 450001 Peoples R China Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450001 Peoples R China Zhengzhou Univ Zhengzhou 450000 Peoples R China

Software-Defined networking (SDN) revolutionizes the rigid operation and management mode of traditional vertical architecture. A centralized controller enables more granular control of the network based on a global view. However, the traffic forwarding mode in the traditional networks is still inherited, which cannot make full use of the underlying network resources to meet higher quality of service. In addition, the demand for massive data processing has also forced operators to seek the help of intelligent management solutions. In this paper, we propose a flowlet-level multipath routing scheme based on graph neural network to leverage the performance optimization benefits of parallel transport. The flowlet-based solution strikes a balance between flow transmission granularity and reordering, which can effectively improve the end-to-end transmission efficiency. Specifically, first, we design an adaptive flow splitting scheme to achieve multipath transmission when the network state changes dynamically by using the rule timeout mechanism that comes with the OpenFlow protocol. Secondly, we propose to use graph neural network to predict link delay to assist in adaptive flow splitting and forwarding path selection intelligently. Simulation results show that the graph neural network has good convergence and generalization in delay prediction, and the flow splitting mechanism based on rule timeout realizes adaptive flow splitting according to network state changes. The overall scheme outperforms existing typical solutions in terms of time overhead, end-to-end delay, flow completion time, and throughput. Meantime, experiments on different topologies verify the scalability of our scheme. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Software-defined networking Graph neural network Multipath routing Flowlets Adaptive flow splitting

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System Modeling and performance Evaluation of Predictive QoS for Future Tele-Operated Driving 16

System Modeling and Performance Evaluation of Predictive QoS...

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16th Annual IEEE International systems conference (SysCon)

作者： Schippers, Hendrik Schueler, Cedrik Sliwa, Benjamin Wietfeld, Christian TU Dortmund Univ Commun Networks Inst D-44227 Dortmund Germany

ISBN: (纸本)9781665439923

Future Tele-operated Driving (ToD) applications place challenging quality of Service (QoS) demands on existing mobile communication networks that are highly important to comply with for safe operation. New remote control and platooning services will emerge and pose high data rate and latency requirements. One key enabler for these applications is the newly available 5G New Radio (NR), promising higher bandwidth and lower latency than its predecessors. In addition to that, public 5G networks do not consistently deliver and do not guarantee the required data rates and latency of ToD. In this paper, we discuss the communication-related requirements of tele-operated driving. ToD is regarded as a complex system consisting of multiple research areas. One key aspect of ToD is the provision and maintenance of the required data rate for teleoperation by the mobile network. An in-advance prediction method of the end-to-end data rate based on so-called Radio Environmental Maps (REMs) is discussed. Furthermore, a novel approach improving the prediction accuracy is introduced and it features individually optimized REM layers. Finally, we analyze the implementation of tele-operated driving applications on a scaled vehicular platform combined with a cyber-physical test environment consisting of real and virtual objects. This approach enables large-scale testing of remote operation and autonomous applications.

关键词： performance evaluation network slicing quality of service Prediction methods systems modeling New Radio Complex systems

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AMSC: Adaptive Multi-channel Graph Convolutional network-Enhanced Web Services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of Service Oriented Architecture (SOA), the number of Web services on the internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems network topology Smart cities quality of service Feature extraction Topology

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MuSense: Sensor Data Fusion-inspired Intelligent Music Improvisation Framework in 5G-internet of Music Things

MuSense: Sensor Data Fusion-inspired Intelligent Music Impro...

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URSI Regional conference on Radio Science (USRI-RCRS)

作者： Roy, Samarjit Mukherjee, Anwesha De, Debashis DY Patil Int Univ Dept Comp Sci & Engn Pune 411044 Maharashtra India Mahishadal Raj Coll Dept Comp Sci Garh Kamalpur 721628 West Bengal India MAKAUT Dept Comp Sci & Engn Kolkata West Bengal India

ISBN: (纸本)9789463968089

This article focuses on the emerging music composition, reconstruction, and improvisation in the internet of Things (IoT). In the current internet era, musical composition and reconstruction schemas are largely dependent on the IoT-based paradigm where human beings can arrange musical compositions through musical things, such as, remotely arranged singing and musical instruments, smart rhythmic behaviors, and intelligent autotuning systems. We have renamed the music composition, sharing, and recommendation phenomenon in the IoT paradigm as the internet of Music Things (IoMT). We present a fifth-generation (5G) technology-enabled IoMT framework to explore remote music collaboration, multisensory music information fusion, and socially-desirable music composition schema due to its capability of providing ultra-low latency, prominent bandwidth, assured quality of Services, and lively orchestrated network environment. We incorporate the system performance metrics with the proposed 5G-IoMT in terms of the data transmission latency and potential energy dissipation. We moreover illustrate a Generative Adversarial network-inspired intelligent music improvisation strategy as a case study for audience interest.

关键词： Music

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