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IEEE Pacific Rim conference on Communications, Computers and Signal Processing

作者： Yosuke Komatsu Dirceu Cavendish Daiki Nobayashi Takeshi Ikenaga Graduate School of Engineering Kyushu Institute of Technology Fukuoka Japan Faculty of Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

Video streaming makes most of internet traffic nowadays, being transported over Hypertext Transfer Protocol/Transmission control Protocol (HTTP/TCP). Being the pre-dominant transport protocol, TCP stack performance in transporting video streams has become paramount, specially with regard to MultiPath Transport control Protocol (MPTCP) innovation and multiple client device interfaces currently available. Recently, Low Orbit Satellite networks have become available as a way to cover remote locations where cellular coverage is spotty at best with internet access. In this paper, we evaluate video streaming hand-off performance via cellular and LEO links. Such scenario is commonplace in geographical areas where cellular communication is unreliable, such as disaster and conflict torn situations. We provide an extensive analysis of Bottleneck Bandwidth and Round-trip propagation time (BBR) TCP variant, as well as CUBIC when handing off video streams between terrestrial cellular network (5G) and LEO (Starlink) access networks. We use network performance level, as well video quality level metrics to characterize quality of video streaming over TCP variants.

关键词： performance evaluation Cellular networks Transport protocols Technological innovation Satellites Streaming media internet MPTCP Streams Video recording

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Reinforced Learning Model with Coati optimization Algorithm for Energy Efficient Routing for WSN Connecting IoT

Reinforced Learning Model with Coati optimization Algorithm ...

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IEEE International conference on System, Computation, Automation and networking (ICSCAN)

作者： SriHasini J Oudaya Coumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The emergence of notable obstacles such heterogeneity, dependability, and scalability has been brought about by the growth of varied wireless devices in real-time internet of Things applications. A new architecture known as Energy-Efficient Routing for IoT (EER-IoT) has been designed to efficiently address these problems. IoT devices in these systems, however, have to deal with unpredictable network dynamics, energy limitations, and strict quality of service requirements. The importance of having a well-designed routing protocol is highlighted by these reasons. Inaction on this part frequently results in frequent network disconnections and poor performance. In this work, we offer the intelligent, energy-efficient routing protocol, called EER-COA-RL, that combines reinforcement learning with the Coati Optimization Algorithm. This protocol evaluates fitness functions based on residual energy (RER) and distance limitations in an effort to improve network performance and lifespan. Optimizing energy usage in internet of Things networks is the main goal of implementing this routing method. This is especially important since wireless internet of Things devices have limited energy resources and you need to be able to quickly adjust to changes in the network to minimize interruptions and maximize network performance.

关键词： performance evaluation Wireless communication Wireless sensor networks Clustering algorithms Reinforcement learning Routing Energy efficiency Routing protocols internet of Things Optimization

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Assessing the quality of Differentially Private Synthetic Data for Intrusion Detection 1

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18th EAI International conference on Security and Privacy in Communication networks, SecureComm 2022

作者： Al Amin, Md Ali Reza Shetty, Sachin Formicola, Valerio Otto, Martin Old Dominion University NorfolkVA23508 United States California Polytechnic State University PomonaCA United States Siemens Technology US PrincetonNJ08540 United States

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Supervised learning is effectively adopted in network Intrusion Detection systems (IDS) to detect malicious activities in Information Technology (IT) and Operation Technology (OT). Sharing high-quality network data among cyber-security practitioners increases the chance of detecting new threat campaigns by analyzing updated traffic features. As data sharing brings privacy concerns, Differential-Privacy (DP) has emerged as a promising approach to performing privacy-preserving analytics. This paper presents an approach to generating high-quality synthetic network features using a differentially private Generative Adversarial network (DP-GAN) based on the DoppleGANger https://***/fjxmlzn/DoppelGANger toolset. We assess the classification performance of several machine learning (ML) models on a privacy-preserved synthetic dataset derived from the NSL-KDD intrusion dataset. Experiments show ML algorithms achieve high classification accuracy on the synthetic data (95.95 % ) with a low privacy budget (Ε= 6.73 ), i.e., low success rates for membership inference attacks. Hence, DP-GAN models offer a promising tool for sharing traffic features with bounded loss of privacy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Budget control

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Research on IOT online monitoring system based on efficient utilization pathway of mine water 4

Research on IOT online monitoring system based on efficient ...

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4th International conference on Advanced Information Science and System, AISS 2022

作者： Bo, Lei Zhang, Zihang Liu, Yang Li, Zecheng China University of Mining and Technology-Beijing China State Grid Ningxia Electric Power Research Institute China

ISBN: (纸本)9781450397933

There are problems such as communication lag and data silo in dynamic processing and reuse of mine water resource utilization. In this paper, the framework of mine water online monitoring IOT system is researched for the real situation of mine water resource utilization, and the collaborative scheduling model and algorithm of mine water multi-subsystem is constructed based on the characteristics of rapid data concentration under this framework. For the mine water treatment process on the processing unit water quality information can not be obtained in a timely manner and scheduling timeliness and other problems, based on the internet of Things to achieve intelligent perception of mine water quality data, equipment status information, the establishment of a full sense database, multi-sensor data fusion of mine water quality monitoring three-dimensional network, data-driven to enhance the underground-ground collaborative scheduling of rapid and accurate response to changes in water supply demand;the introduction of Cloud service platform to strengthen the performance analysis and online prediction of mine water dispatching system, enhance the decision-making capability of mine water dispatching system, improve the comprehensive utilization rate of mine water, and realize the status monitoring, performance prediction and optimal control of mine water dispatching system. © 2022 ACM.

关键词： internet of things

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An Improved Image Steganography Method for Domestic internet of Things

An Improved Image Steganography Method for Domestic Internet...

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Communication, control and Intelligent systems (CCIS)

作者： Himanshu Jharaniya Nitin Choudhary Gourav Shrivastava School of Advanced Computing SAGE University Bhopal India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Domestic internet of Things (DioT) environment of a household requires prompt considerations in order to develop proper protection mechanisms for the transfer of sensitive information between linked devices. This created the need for this research work to put forward an improved image steganography technique for DIoT which employs AES encryption to encrypt the cover image and Least Significant Bit (LSB) for the insertion of the secret information table. This paper explains the design and implementation of the proposed method with clear indication of the improvement in security while maintaining system performance for data transfer. Therefore, the results of the evaluation in this article demonstrate the effectiveness of the proposed hybrid solution for strengthening the confidentiality and integrity of data exchanged within the complex environment of domestic IoT.

关键词： Image quality Steganography System performance Noise Machine learning Real-time systems Encryption internet of Things Protection Intelligent systems

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Enhancing Threat Detection Accuracy in IIoT networks using AI-based Models

Enhancing Threat Detection Accuracy in IIoT Networks using A...

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International conference on ent Computing and control systems (ICICCS)

作者： Omer Waseem Al-Zaidi Mohammed Al-Hubaishi Department of Software Engineering Halic University Istanbul Turkey Department of Computer Engineering Halic University Istanbul Turkey

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Software-Defined networking (SDN) and Artificial Intelligence (AI) have the potential to address challenges in Industrial internet of Things (IIoT) networks, such as security, scalability, and network performance, and this paper proposes a robust framework that leverages SDN and AI to enhance network management, real-time monitoring, and efficient resource utilization. SDN decouples the control and data planes, providing centralized control and simplifying the management because AI techniques, including machine learning and reinforcement learning, enable intelligent threat detection, optimized routing, and data protection. The synergistic fusion of these technologies ensures timely and secure data transmission, reduces latency and prevents data loss. Thus, the proposed framework enhances IIoT deployments through automation, predictive analytics, and proactive threat detection and mitigation. This paper demonstrates the integration of SDN and AI to create secure, scalable, and seamless IIoT networks that meet the requirements of modern industrial operations and information security standards. Therefore, it highlights the potential of SDN and AI to revolutionize IIoT networks, providing a comprehensive framework for future IIoT deployments.

关键词： Computational modeling Scalability Routing Threat assessment Real-time systems Computational efficiency Artificial intelligence Software defined networking Standards Industrial internet of Things

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A Multi-Stage Optimisation Approach to Design Relocation Strategies in One-Way Car-Sharing systems With Stackable Cars

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第10期23卷 17048-17061页

作者： Iacobucci, Riccardo Bruno, Raffaele Boldrini, Chiara Tensor Energy Tokyo 1020093 Japan IIT CNR Ubiquitous Internet Lab I-56124 Pisa Italy

One of the main operational challenges faced by the operators of one-way car-sharing systems is to ensure vehicle availability across the regions of the service areas with uneven patterns of rental requests. Fleet balancing strategies are required to maximise the demand served while minimising the relocation costs. However, the design of optimal relocation policies is a complex problem, and global optimisation solutions are often limited to very small network sizes for computational reasons. In this work, we propose a multi-stage decision support system for vehicle relocation that decomposes the general relocation problem into three independent decision stages to allow scalable solutions. Furthermore, we adopt a rolling horizon control strategy to cope with demand uncertainty. Our approach is highly modular and flexible, and we leverage it to design user-based, operator-based and robotic relocation schemes. Besides, we formulate the relocation problem considering both conventional cars and a new class of compact stackable vehicles that can be driven in a road train. We compare the proposed relocation schemes with two recognised benchmarks using a large data set of taxi trips in New York. Our results show that our approach is scalable and outperforms the benchmark schemes in terms of quality of service, vehicle utilisation and relocation efficiency. Furthermore, we find that stackable vehicles can achieve a relocation performance close to that of autonomous cars, even with a small workforce of relocators.

关键词： Automobiles Optimization Costs Uncertainty Task analysis Roads Computational modeling Car sharing vehicle relocation optimisation

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Particularities of Identification and Tuning of Linear systems with State controllers Using Artificial Neural networks

Particularities of Identification and Tuning of Linear Syste...

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International conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Anatolv A. Anisimov Mark E. Sorokovnin Vladimir V. Tyutikov Department of Electronics and Microprocessor Systems Ivanovo State Power Engineering University Ivanovo Russia Department of Automation of Technological Processes Ivanovo State Power Engineering University Ivanovo Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

At present, state controllers are used to improve the quality of mechatronic systems control. The high potential of systems with such controllers can be implemented only if automatic tuning means are available. Since tuning is performed in real time mode, which places higher demands on performance, to reduce tuning time it is proposed to use an artificial neural network. However, under interference in measuring channels, the quality of parameter identification in the controlled object is significantly lowered. In this regard, the purpose of this study is to find the optimal composition of measuring channels at the network input, making it possible to minimize the impact of interference on estimates of object parameters to improve control quality. In the present study, an algorithm is proposed to optimize the positioning of measuring channels in the task of identifying and tuning control systems. As an optimality criterion, we use the information signal-to-interference power ratio at network inputs.

关键词： Linear systems Mechatronics Uncertainty Atmospheric measurements Artificial neural networks Interference control systems

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Development and Evaluation of Smart Home IoT systems applied to HVAC Monitoring and control

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第34期8卷 1-9页

作者： Figueiredo, Ruben E. Alves, Aníbal A. Monteiro, Vitor Pinto, J.G. Afonso, João L. Afonso, José A. Algoritmi Research Centre University Of Minho Guimarães Portugal CMEMS-UMinho Center University of Minho Guimarães Portugal

This work describes the development and evaluation of two smart home-based internet of Things (IoT) systems applied to HVAC (Heating, Ventilation and Air Conditioning) monitoring and control, including parameters such as temperature, humidity, air quality, smoke detection, and human presence. These systems are based on a flexible hybrid wireless network architecture combining Bluetooth Low Energy (BLE) and IEEE 802.11/Wi-Fi, in order to adapt to the requirements of different types of sensor and actuator devices. The original implemented network is based on Cypress PSoC 4 BLE boards and HyperText Transfer Protocol (HTTP), whereas the new network uses ESP32 boards and includes Message Queue Telemetry Transport (MQTT), a lightweight messaging protocol suitable for IoT devices which provides additional quality of service (QoS) mechanisms to guarantee the delivery of messages. A smart temperature control system was implemented in the BLE/Wi-Fi gateway (Raspberry Pi) to keep the room temperature inside a user-defined range. An online database was also developed using the Amazon Web Services (AWS) cloud platform, allowing the users to access the HVAC data and control the system parameters, through the internet, using a mobile app developed for Android devices. Experimental tests were performed to validate the functionalities and performance of the developed systems. The obtained results demonstrate that the new network provides lower delay values compared with the original implementation. © 2020 Ruben E. Figueiredo et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Wireless sensor networks

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Simulating Distributed Wireless Sensor networks for Edge-AI 5

Simulating Distributed Wireless Sensor Networks for Edge-AI

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5th conference on Cloud and internet of Things (CIoT)

作者： Prajapati, Ambar Banerjee, Bonny Proc Control Automat & Integrat Int Paper Memphis TN 38113 USA Univ Memphis Inst Intelligent Syst Memphis TN USA Univ Memphis Dept Elect & Comp Engn Memphis TN USA

ISBN: (纸本)9781665479646

This paper presents the simulation of distributed wireless sensor networks (WSNs) consisting of autonomous mobile nodes that communicate, with or without a central/root node, as desired for edge artificial intelligence (edge-AI). We harness the high-resolution and multidimensional sensing characteristics of IEEE 802.15.4 standard and Routing Protocol for Low-Power and Lossy networks (RPL) to implement dynamic, asynchronous, event-driven, targeted communication in distributed WSNs. We choose Contiki-NG/Cooja to simulate two WSNs, one with and the other without a root node. The simulations are assessed on the network quality of Service (QoS) parameters such as throughput, network lifetime, power consumption, and packet delivery ratio. The simulation outputs show that the sensor nodes at the edge communicate successfully with the specific targets responding to particular events in an autonomous and asynchronous manner. The performance is slightly degraded when using the RPL WSN with a root node. This work shows how to simulate and evaluate distributed WSNs using the Cooja simulator which would be useful for designing such networks for edge-AI applications, such as visual surveillance, monitoring in assisted living facilities, intelligent transportation with connected vehicles, automated factory floors, immersive social media experience, and so on.

关键词： Edge-AI Contiki OS Cooja Wireless sensor networks simulation Routing Protocol for Low-Power and Lossy networks IEEE 802.15.4 event-driven and targeted communication

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