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4th International Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Dlamini, Basil M. Migabo, Emmanuel M. Kurien, Anish M. Tshwane University of Technology Dept. of Electrical Engineering Pretoria South Africa

ISBN: (纸本)9798350387988

The rapid proliferation of internet of Things (IoT) devices poses significant challenges to network performance, particularly in terms of congestion due to limited network resources. Advances in cellular technology, especially 5G, offer promising solutions to these issues. However, as the number of IoT nodes continues to grow, networks are increasingly at risk of overload, leading to delays, packet loss, and instability. To address these challenges, this paper proposes a multi-layer optimization strategy aimed at enhancing network performance and scalability within 5G IoT networks. The approach involves simulating the uplink communication channel between IoT nodes and the base station (BS), followed by the formulation of a multi-objective optimization problem that considers the number of IoT nodes in a cell and the constraints of 5G cellular networks. This problem is solved to ensure scalability while minimizing performance degradation. The optimization strategy identifies the optimal BS downlink waiting time, transmission power, and IoT device data rates, using a discrete network simulator to evaluate these parameters. The results demonstrate that the proposed method effectively reduces network congestion and is more energy-efficient compared to existing static clustering systems, the mesh schemes, the Fair and Scalable Relay control (FSRC) and the Spreading Factor Partition Based Clustering and Relay (SFPCR) schemes. © 2024 IEEE.

关键词： 5G mobile communication systems

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RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic Engineering 24

RedTE: Mitigating Subsecond Traffic Bursts with Real-time an...

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ACM SIGCOMM conference

作者： Gui, Fei Wang, Songtao Li, Dan Chen, Li Gao, Kaihui Min, Congcong Wang, Yi Tsinghua Univ Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China BNRist Beijing Peoples R China Guangdong Commun & Networks Inst Guangzhou Peoples R China Southern Univ Sci & Technol Inst Future Networks Shenzhen Peoples R China

ISBN: (纸本)9798400706141

internet traffic bursts usually happen within a second, thus conventional burst mitigation methods ignore the potential of Traffic Engineering (TE). However, our experiments indicate that a TE system, with a sub-second control loop latency, can effectively alleviate burst-induced congestion. TE-based methods can leverage network-wide tunnel-level information to make globally informed decisions (e.g., balancing traffic bursts among multiple paths). Our insight in reducing control loop latency is to let each router make local TE decisions, but this introduces the key challenge of minimizing performance loss compared to centralized TE systems. In this paper, we present RedTE, a novel distributed TE system with a control loop latency of < 100ms , while achieving performance comparable to centralized TE systems. RedTE's innovation is the modeling of TE as a distributed cooperative multi-agent problem, and we design a novel multi-agent deep reinforcement learning algorithm to solve it, which enables each agent to make globally informed decisions solely based on local information. We implement real RedTE routers and deploy them on a WAN spanning six city datacenters. Evaluation reveals notable improvements compared to existing solutions: < 100ms of control loop latency, a 37.4% reduction in maximum link utilization, and a 78.9% reduction in average queue length.

关键词： Traffic Engineering network Optimization Machine Learning

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Discrete Gain Scheduling Output Feedback control of Input-Saturated Linear Multi-agent systems 20th

Discrete Gain Scheduling Output Feedback Control of Input-Sa...

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20th Chinese Intelligent systems conference

作者： Gao, Xiangyu Zhong, Zefeng Xie, Weiwei Wang, Jianqiao Guangxi Normal Univ Sch Math & Stat Guilin 541004 GuangXi Peoples R China Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China

ISBN: (纸本)9789819786565;9789819786541;9789819786534

This paper considers the state observer-based discrete gain scheduling output feedback control problem for linear multi-agent systems with input saturation. The distributed discrete gain scheduling output feedback control method is proposed to studey bipartite consensus of input saturated linear multi-agent system with the directed network topology. It can be proved that the linear multi-agent system can achieve the bipartite consensus under the observer-based discrete gain scheduling feedback control protocol, which is obtained through solving the parameter Lyapunov equation. With the increases of discrete switching parameters, the observer-based discrete gain scheduling feedback control protocol can effectively improve the dynamic performance of multi-agent systems with input saturation. Finally, a numerical simulation is provided to show the validity of the theoretical results.

关键词： Linear multi-agent systems Input saturation Bipartite consensus Discrete gain scheduling output feedback control Analytical solution of the parametric Lyapunov equation

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Stochastic network Calculus Based Quality of Service Guarantee for Multi-class Traffic

Stochastic Network Calculus Based Quality of Service Guarant...

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2024 IEEE International performance, Computing, and Communications conference, IPCCC 2024

作者： Wang, Yunlan Wang, Qing Zhao, Tianhai Hu, YongKuo Gu, JianHua Hou, ZhengXiong Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350367942

With the rapid development of cloud computing technology, a variety of emerging network traffic types have higher quality of service(QoS) requirements. However, the traditional internet's best-effort service cannot meet the demands of cloud computing applications. Therefore, we propose a stochastic network calculus(SNC) based QoS guarantee mechanism consisting of two parts. The first part is MTACC, a multi-threshold adaptive admission control algorithm based on network calculus. MTACC classifies network traffic, calculates resource requirements, and introduces admission probabilities and demarcation parameters. As the network load reaches various thresholds, MTACC adjusts the demarcation parameter to modify the admission probabilities accordingly. The second part is TSRA, a two-stage resource allocation algorithm. In the first stage, basic resources are allocated to ensure the minimum resource requirements of the network traffic based on SNC. In the second stage, additional idle resources are allocated to improve the QoS of the network traffic based on the resource utility function. Finally, we simulate the QoS guarantee techniques using NS3. By comparing with Simple Sum and SCAC, we verify the effectiveness of our proposed QoS guarantee techniques. They provide robust performance guarantees for multi-class traffic such as delay-sensitive, bandwidth-sensitive, and packet loss-sensitive traffic. © 2024 IEEE.

关键词： Stochastic systems

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DRL-based Latency-Aware network Slicing in O-RAN with Time-Varying SLAs

DRL-based Latency-Aware Network Slicing in O-RAN with Time-V...

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International conference on Computing, networking and Communications (ICNC)

作者： Raftopoulos, Raoul D'Oro, Salvatore Melodia, Tommaso Schembra, Giovanni Univ Catania CNIT Res Unit Catania Italy Northeastern Univ Inst Wireless Internet Things Boston MA USA

ISBN: (纸本)9798350371000;9798350370997

The Open Radio Access network (Open RAN) paradigm, and its reference architecture proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable and truly intelligent cellular networks. Crucial to this evolution is Machine Learning (ML), which will play a pivotal role by providing the necessary tools to realize the vision of self-organizing O-RAN systems. However, to be actionable, ML algorithms need to demonstrate high reliability, effectiveness in delivering high performance, and the ability to adapt to varying network conditions, traffic demands and performance requirements. To address these challenges, in this paper we propose a novel Deep Reinforcement Learning (DRL) agent design for O-RAN applications that can learn control policies under varying Service Level Agreement (SLAs) with heterogeneous minimum performance requirements. We focus on the case of RAN slicing and SLAs specifying maximum tolerable end-to-end latency levels. We use the OpenRAN Gym open-source environment to train a DRL agent that can adapt to varying SLAs and compare it against the state-of-the-art. We show that our agent maintains a low SLA violation rate that is 8.3x and 14.4x lower than approaches based on Deep Q-Learning (DQN) and Q-Learning, while consuming respectively 0.3x and 0.6x less resources without the need for re-training.

关键词： O-RAN network Slicing Deep Reinforcement Learning Latency-aware Service Level Agreement

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Domain Knowledge Driven Unified Architecture for IoT Device Identification using Deep Metric Representation Learning 7

Domain Knowledge Driven Unified Architecture for IoT Device ...

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7th International conference on Intelligent Computing and control systems, ICICCS 2023

作者： Gunavathie, M.A. Saravanan, S. Deepak, B. Sheshanth Kumar, M. Information Technology Panimalar Engineering College Chennai India

ISBN: (纸本)9798350397253

Devices connected to the internet of Things (IoT) are well recognized for being the root of several security issues, and as a result, they would significantly benefit from automated management. For the purpose of implementing the proper network security measures, robustly identifying devices is necessary. The performance and security of IoT devices are monitored and improved in large part thanks to device identification. Thus, methods to properly recognize IoT devices are explored. When detecting the many distinct categories of Smart home devices as indicated to differentiate devices, IoT devices confront us with both inherent difficulties and prospects. Based on important results from preceding research on comprehending the patterns of network traffic in IoT devices, an efficient machine-deep-learning IoT network identifier approach is created in this study. In order to extract 298 distinct features of network traffic, the proposed model integrates three separate parameters such as network servers in remote, port numbers, the time between packets which is the characteristics of traffic in packet level, and the duration of the flow which is the characteristics of traffic in flowlevel. The fact that distinct devices on a network might create traffic at varying rates and this variability are considered, both during the process of training the system and while evaluating its performance. This isolates the proposed methodology from prior literature. © 2023 IEEE.

关键词： internet of things

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Real-time and Scalability of Smart Two Ticket System Based on Edge Computing in Power Grid Management 2

Real-time and Scalability of Smart Two Ticket System Based o...

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2nd International conference on Data Science and Information System, ICDSIS 2024

作者： Huang, Chengwei Zeng, Jia Zhang, Fayan Zhang, Yubo Zhang, Wei Power Supply Bureau Of Guangxi Hezhou Power Grid Co. Ltd. Hezhou China Jia Zeng Guangxi Power Grid Co. Ltd. Nanning China Electric Power Research Institute Of Guangxi Power Grid Co. Ltd. Guangxi Key Laboratory Of Intelligent Control And Operation And Maintenance Of Power Equipment Nanning China

ISBN: (纸本)9798350381665

With the continuous development of internet of Things (IoT) technology, edge processing platforms for power system characteristics have become an important technology. At present, many professional information systems of State Grid Corporation of China are faced with the problem that information cannot be shared and collected. It is urgent to use the idea of the internet of Things, high-performance edge computing and other methods to build an intelligent internet of Things system. In this paper, a new intelligent internet of Things system based on edge computing was studied according to the application requirements of smart grid for dynamic control, strategy management, data preprocessing, etc., aiming to reduce network transmission capacity, reduce operating costs, and further optimize equipment maintenance costs. Simulation experiments have shown that when the number of Centralized Unit of Execution and Heterogeneous Unit of Execution in the network was increased to 70 and 100, respectively, the unloading completion times of the schemes 'HUE Cloud Edge' and 'HUE-Cloud Edge GM' were between 0.25-0.45 and 0.2-0.4, respectively, which were lower than those of cloud and HUECloud Randomness. © 2024 IEEE.

关键词： Edge computing

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LoRa NET Base Station Monitoring System: Real-time insights using Mobile Application 3

LoRa NET Base Station Monitoring System: Real-time insights ...

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3rd International conference for Innovation in Technology, INOCON 2024

作者： Salgar, Akash Mane, Anand Binnar, Nikhil Chavan, Swapnil Sardar Patel Institute of Technology Andheri Dept of Electronics and Telecommunication Engineering Mumbai India

ISBN: (纸本)9798350381931

With the rapid evolution of internet of Things (IoT) technologies, there is an increasing demand for efficient and scalable solutions for remote monitoring and control systems. This paper introduces a LoRa (Long Range) network-based Base Station Monitoring System that leverages LoRa technology for efficient and scalable remote monitoring. Employing LoRa-enabled sensors and gateways, the system establishes a robust wireless network, enabling the real-time transmission of critical data from remote base stations to a centralized server. A dedicated mobile application provides users with intuitive access to real-time insights, allowing monitoring of key performance indicators, environmental conditions, and operational parameters. With demonstrated effectiveness in diverse applications such as agriculture, industrial automation, and smart cities, the LoRa NET Base Station Monitoring System offers a cost-effective and scalable solution for large-scale deployment, contributing to the advancement of IoT-based monitoring solutions. © 2024 IEEE.

关键词： internet of things

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A Survey on Routing Algorithms and Techniques Used to Improve network performance in Software-Defined networking 2

A Survey on Routing Algorithms and Techniques Used to Improv...

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2nd International conference on Computational systems and Communication, ICCSC 2023

作者： Suryanarayan, Menon A. Prakash, Sanjay J Naveen, Vinayak Cherian, Roshan Aji Zacharia, Ron Regi Suryapriya, S. Josna, V.R. Government Engineering College Department of Information Technology Kerala Thiruvananthapuram India

ISBN: (纸本)9781665493932

The growth of network infrastructure over the years, including the increasing number of devices and users connecting to networks, the increasing complexity of network environments, and the need for more flexible and efficient network management, has led to the emergence of Software-Defined networking (SDN) as a feasible means of support. SDN is a networking approach that uses software to abstract, manage, and control the network infrastructure. In SDN, the control plane, which determines how data is routed through the network, is separated from the data plane, which forwards the traffic. This separation allows the control plane to be more flexible and programmable, enabling administrators to easily configure and manage the network using the software. There are several routing algorithms and techniques that can be used to improve network performance in Software-Defined networking. Shortest Path Routing is a common routing algorithm along with Segment Routing and may be capable of speeding up the network's overall performance by decreasing the time it takes for traffic to reach its destination. Load Balancing is a technique that involves distributing traffic across multiple resources, such as servers or network devices, to optimize the use of resources and improve the performance of the network. For this, there exist several techniques such as Round Robin and Ant Colony Optimization. Another such technique used to optimize the use of resources and improve the overall performance of the network is Traffic Engineering-where software is used to route traffic through the network based on various criteria, such as the type of traffic, the source, and destination of the traffic, or the current load on the network. There have been numerous studies in these fields investigating the feasibility of novel systems and solutions that have been proposed for improving network performance. This paper goes into the methodologies followed for these systems and the impacts they have had

关键词： Software defined networking

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Driving behavior analysis and classification by vehicle OBD data using machine learning

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JOURNAL OF SUPERCOMPUTING 2023年第16期79卷 18800-18819页

作者： Kumar, Raman Jain, Anuj Lovely Profess Univ Phagwara India

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-ii diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-ii protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-ii protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performanc

关键词： Society of automotive engineers (SAE) On-board diagnostic (OBD) internet of things (IoT) Engine control unit (ECU) Analysis of driving behavior (ADB) Body control unit (BCU) Keyword protocol (KWP) Data acquisition systems (DAS) controller area network (CAN) Information and communication technology (ICT)

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