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20th European conference on Computer systems-EuroSys

作者： Tian, Han Liao, Xudong Sun, Decang Zeng, Chaoliang Jin, Yilun Zhang, Junxue Wan, Xinchen Wang, Zilong Wang, Yong Chen, Kai Univ Sci & Technol China Beijing Peoples R China Hong Kong Univ Sci & Technol iSING Lab Hong Kong Peoples R China BitIntelligence West Boylston MA USA

ISBN: (纸本)9798400711961

internet congestion control (CC) has long posed a challenging control problem in networking systems, with recent approaches increasingly incorporating deep reinforcement learning (DRL) to enhance adaptability and performance. Despite promising, DRL-based CC schemes often suffer from poor fairness, particularly when applied to network environments unseen during training. This paper introduces Jury, a novel DRL-based CC scheme designed to achieve fairness generalizability. At its heart, Jury decouples the fairness control from the principal DRL model with two design elements: i) By transforming network signals, it provides a universal view of network environments among competing flows, and ii) It adopts a post-processing phase to dynamically module the sending rate based on flow bandwidth occupancy estimation, ensuring large flows behave more conservatively and smaller flows more aggressively, thus achieving a fair and balanced bandwidth allocation. We have fully implemented Jury, and extensive evaluations demonstrate its robust convergence properties and high performance across a broad spectrum of both emulated and real-world network conditions.

关键词： Congestion control Reinforcement Learning Transport Protocol

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SDN-enabled Quantized LQR for Smart Traffic Light controller to Optimize Congestion

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ACM TRANSACTIONS ON internet TECHNOLOGY 2024年第1期24卷 1-25页

作者： Sachan, Anuj Kumar, Neetesh Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

Existing intersection management systems, in urban cities, lack in meeting the current requirements of selfconfiguration, lightweight computing, and software-defined control, which are necessarily required for congested road-lane networks. To satisfy these requirements, this work proposes effective, scalable, multi-input and multi-output, and congestion prevention-enabled intersection management system utilizing a softwaredefined control interface that not only regularly monitors the traffic to prevent congestion for minimizing queue length and waiting time but also offers a computationally efficient solution in real-time. For effective intersection management, a modified linear-quadratic regulator, i.e., Quantized Linear Quadratic Regulator (QLQR), is designed along with Software-defined networking (SDN)-enabled control interface to maximize throughput and vehicles speed and minimize queue length and waiting time at the intersection. Experimental results prove that the proposed SDN-QLQR improves the comparative performance in the interval of 24.94%-49.07%, 35.78%-68.86%, 36.67%-59.08%, and 29.94%-57.87% for various performance metrics, i.e., average queue length, average waiting time, throughput, and average speed, respectively.

关键词： Intelligent transportation system (ITS) smart city traffic light controller (TLC) urban traffic network

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Application of fuzzy learning in IoT-enabled remote healthcare monitoring and control of anesthetic depth during surgery

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INFORMATION SCIENCES 2023年第1期626卷 262-274页

作者： Farivar, Faezeh Jolfaei, Alireza Manthouri, Mohammad Haghighi, Mohammad Sayad Islamic Azad Univ Dept Comp & Mechatron Engn Sci & Res Branch Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci POB 19395-5746 Tehran Iran Flinders Univ S Australia Coll Sci & Engn Adelaide SA 5042 Australia Shahed Univ Elect & Elect Engn Dept Tehran Iran Univ Tehran Sch Elect & Comp Engn Tehran Iran

Smart remote patient monitoring and early disease diagnosis systems have made huge pro-gresses after the introduction of internet of Things (IoT) and Artificial Intelligence (AI) con-cepts. This paper proposes an AI-enabled IoT system to monitor and adjust the depth of anesthesia via network channels. More precisely, fuzzy learning systems are employed to develop a control system for the depth of anesthesia in surgeries. This scheme is com-posed of variable structure control and adaptive type-ii fuzzy systems. Therefore, the con-troller is adaptive and robust to any perturbations and disturbances that may happen during a patient's surgery. The adaptive type-ii fuzzy system is designed as an intelligent online estimator to approximate patient model uncertainties. This estimation helps in boosting the performance of the variable structure control system. An artificial neuron is also designed to reduce chattering for the proposed control system. The designed control system can efficiently adjust the anesthesia drug infusion rate and regulate the Bispectral index. The networked structure of the proposed system makes remote tuning of drug infusion possible. performance of the designed controller is evaluated on several patient models. Simulation results confirm the validity and effectiveness of the proposed remote drug delivery system.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Remote healthcare Artificial intelligence internet of things Anesthesia networked control system Fuzzy learning system

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Compact AFSIW Antenna With Integrated Digitally controlled Impedance Tuner for Smart Surfaces

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第3期71卷 1111-1115页

作者： Sennesael, Joryan Kapusuz, Kamil Yavuz Lemey, Sam Van Torre, Patrick Rogier, Hendrik Univ Ghent Dept Informat Technol IDLab Electromagnet Grp IMEC B-9052 Ghent Belgium

For unobtrusive smart-surface integration, an air-filled substrate-integrated waveguide (AFSIW) antenna for the [5.15-5.85]GHz band is co-designed with a tunable matching network and a cavity-integrated biasing circuit. It features a reconfigurable matching network to mitigate impedance mismatch due to objects in its reactive near field. A digitally controlled biasing circuit with switched-mode power supply is seamlessly integrated inside the air-filled antenna cavity to accurately control the matching network through a serial interface. The proposed impedance-tunable antenna covers an impedance bandwidth from 4.64 GHz to 6.09 GHz with a peak gain of 4.51 dBi at its center frequency. Measurements show that the cavity-integrated circuitry has negligible influence on the antenna performance, yielding novel integration potential. The impedance-tunable antenna is tested in a smart surface desktop integration scenario where it corrects detuning by objects placed on top, improving the wireless link for IEEE 802.11ac bands.

关键词： Air-filled substrate integrated waveguide (AFSIW) cavity-backed slot antenna internet of Things (IoT) reconfigurable matching network smart surfaces

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Restriction in Flow Area in Gas network 1

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23rd International conference on Next Generation Wired/Wireless networks and systems (NEW2AN) / 16th conference on internet of Things and Smart Spaces (RuSMART)

作者： Khujaev, Ismatulla Kushayevich Fayziev, Rabim Alikulovich Hamdamov, Muzaffar Muhiddinovich Inst Mech & Seism Stabil Struct Tashkent Uzbekistan Tashkent State Univ Econ Tashkent Uzbekistan

Examples of solving problems of gas networks are considered in the article by Simulink program, which can be used in modeling and designing linear and nonlinear dynamic systems, their control systems with nonlinear co... 详细信息

ISBN: (数字)9783031609978

ISBN: (纸本)9783031609961;9783031609978

关键词： Simulink system simulation block diagrams elementary sections of gas pipelines gas network pipe ideal and real gas

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Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems networks

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Discover internet of Things 2025年第1期5卷 1-23页

作者： Izuazu, Urslla Uchechi Nwakanma, Cosmas Ifeanyi Kim, Dong-Seong Lee, Jae Min Institut fur Datentechnik und Kommunikationsnetze Tchnische Universitat Carolo-Wilhelmina zu Hans-Sommer-Strasse 66 Braunschweig38106 Germany Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown26506 United States Department of IT-Convergence Engineering Kumoh National Institute of Technology Gumi39177 Korea Republic of

Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-iioT2021 and Edge-iioT datasets, respectively. © The Author(s) 2025.

关键词： network intrusion

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MBMD-LoRa Scalable LoRaWAN for internet of Things: A Multi-band Multi-data Rate Approach 16th

MBMD-LoRa Scalable LoRaWAN for Internet of Things: A Multi-b...

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16th International conference on Computational Collective Intelligence (ICCCI)

作者： Almuhaya, Mkrm Al-Hadhrami, Tawfik Kaiwartya, Omparakash Brown, David. J. Nottingham Trent Univ Nottingham England

ISBN: (纸本)9783031702587;9783031702594

internet of things based applications are increasingly adopting low-power wide area network (LPWAN) technologies because they provide extensive coverage to many battery-powered devices. Due to its physical layer architecture and regulatory benefits, long-range wide area network (LoRaWAN) has become the most extensively adopted LPWAN solution which enables a new multiband technology. LoRaWAN unequivocally employs the ALOHA medium access control (MAC) protocol, resulting in a substantial reduction in the packet delivery rate, particularly in high-density networks where end devices (EDs) access the network randomly. This significantly and adversely affects the overall network performance. However, its scalability performance with simultaneous impact of multi-bandwidth approach has not yet been adequately investigated. This paper proposes a Multi-Band Multi-Data Rate (MBMD-LoRa) framework for enabling an scalabe LoRaWAN IoT use cases. Firstly, a system model for LoRaWAN is presented focusing on link, propagation and simulation scenarios of the framework. Secondly, the slim data rate, MBMD algorithm, and its zone-based implementation are presented detailing the framework. The comparative performance evaluation of the proposed framework attests to potential benefits considering several metrics related to the scalability of LoRAWAN for emerging IoT use cases.

关键词： internet of Things LPWAN LoRa LoRaWAN ADR

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Research on the Design and performance Optimization of Intelligent Traffic Signal control System Based on internet of Things 4

Research on the Design and Performance Optimization of Intel...

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4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Cao, Yang Institute of Smart City and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610000 China

ISBN: (纸本)9798350359985

With the rapid development of internet of Things technology, intelligent traffic signal control systems have become an excellent way to improve traffic efficiency and safety. In this paper, we model an intelligent traffic signal control system based on the basic theory of the internet of Things. It introduces a graph neural network and principal component analysis for performance optimization. Numerical examples include the relationship between the traffic flow prediction or signal control results in the sample library and the traffic flows, the relationship with devices in the internet of Things, the relationship with graph neural networks, and the relationship with principal component analysis error. It turns out that an intelligent traffic signal control system based on the internet of Things can more accurately predict traffic flow and optimize signal control using graph neural networks and principal component analysis. It works with the internet of Things to realize intelligent traffic management. In this paper, we use theoretical analysis to elucidate the system's internal mechanisms and provide scientific support for further improving the quality of transportation systems and meeting users' expectations. © 2024 IEEE.

关键词： Graph neural networks

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Communication-control Co-design for Robotic Manipulation in 5G Industrial IoT 21

Communication-Control Co-design for Robotic Manipulation in ...

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IEEE 21st International conference on Industrial Informatics (INDIN)

作者： Merwaday, Arvind Vannithamby, Rath Eisen, Mark Sudhakaran, Susruth Cavalcanti, Dave A. Frascolla, Valerio Intel Corp Intel Labs Santa Clara CA 95051 USA

ISBN: (纸本)9781665493130

Industrial internet of Things (iioT) use cases have stringent reliability and latency requirements to enable real-time wireless control systems, which are supported by the 5G ultra-reliable low-latency communications (URLLC). However, extremely high quality-of-service (QoS) requirements in 5G URLLC causes huge radio resource consumption and low spectral efficiency, thus limiting network capacity in terms of the number of supported devices. Industrial control applications typically incorporate redundancy in their design and may not always require extreme QoS to achieve the expected control performance. Therefore, we propose both communication-control co-design and dynamic QoS to address the capacity issue for robotic manipulation use cases in 5G-based iioT. We have developed an advanced co-simulation framework that includes a network simulator, physics simulator, and compute emulator, for realistic performance evaluation of the proposed methods. Through simulations, we show significant improvements in network capacity (i.e., the number of supported URLLC devices), and about 2x gain for the robotic manipulation use case.

关键词： dynamic QoS 5G URLLC industrial robotics 5G network simulator robotic manipulation iioT

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The Environmental Cost of High performance Computing System Simulation 32

The Environmental Cost of High Performance Computing System ...

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32nd Euromicro International conference on Parallel, Distributed and network-Based Processing (PDP)

作者： Masciari, Elio Napolitano, Enea Vincenzo Univ Naples Federico II DIETI Naples Italy

ISBN: (纸本)9798350363074;9798350363081

In this research paper, we explore the essential role of High performance Computing (HPC) in the current technological era, highlighting its extensive use in various sectors, while also considering growing alarm over its environmental footprint. High performance computing systems are essential for managing large data sets and solving complex challenges. However, their significant contribution to escalating energy consumption and carbon emissions in the information and communication technology (ICT) sector cannot be ignored. Our study identifies the increasing energy demands and environmental challenges associated with HPC activities, including resource use, electrical waste and greenhouse gas emissions. It highlights the importance of understanding these environmental impacts in detail. It also contributes to the ongoing dialogue on sustainable computing, promoting a harmonious future where technological progress and environmental sustainability can coexist in unison.

关键词： High performance Computing Green Environment Environmental Cost

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