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International conference on Unmanned Aircraft systems (ICUAS)

作者： Berra, Andrea Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Mellet, Julien Gamage, Udayanga G. W. K. N. Satpute, Sumeet Gajanan Ruggiero, Fabio Lippiello, Vincenzo Tolu, Silvia Fumagalli, Matteo Nikolakopoulos, George Soto, Miguel Angel Trujillo Heredia, Guillermo CATEC Adv Ctr Aerosp Technol Seville Spain Lulea Univ Technol Dept Comp Elect & Space Engn Robot & AI Team Lulea Sweden Univ Naples Federico II Naples Dept Elect Engn & Informat Technol PRISMA Lab Naples Italy Tech Univ Denmark Dept Elect Engn & Photon Automat & Control Grp Lyngby Denmark Univ Seville Robot Vis & Control Grp Sch Engn Seville Spain

ISBN: (纸本)9798350357899;9798350357882

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Aerial Physical Interaction UAVs control Barrier Function Neural network Edge Computing

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A Deep Learning Approach for Anomaly Detection for Industrial control systems 50

A Deep Learning Approach for Anomaly Detection for Industria...

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50th Latin American Computing conference (CLEI)

作者： Giracca, Damian Martinez Pires, Fabio Lopez Baran, Benjamin Jara, Eustaquio Alcides Martinez Univ Nacl Este Ciudad Del Este Paraguay Univ Tres Fonteras Ciudad Del Este Paraguay Univ Comunera Asuncion Paraguay

ISBN: (纸本)9798331540982;9798331540975

Anomaly Detection in Cyber Physical systems (CPS) like Industrial control systems (ICS) presents research opportunities in different industries considering intelligent systems, mainly for Intrusion Detection systems (IDS). This work surveys the specialized literature focusing on main published testbeds, datasets and methodologies for Anomaly Detection based on industrial process measurement data to develop and evaluate the performance of IDS applied for ICS. In this context, this work proposes a novel Deep Learning approach for an IDS based on a Long Short-Term Memory (LSTM) Neural network. An experimental evaluation of obtained results for the HIL-based Augmented ICS (HAI) testbed demonstrate that the proposed LSTM-based IDS outperforms state-of-the-art alternative IDS based on other algorithms such as K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC) and Random Forest (RF), considering performance metrics such as Accuracy (0.9996), Precision (0.9978), F1-Score (0.9978) and Recall (0.9978).

关键词： Anomaly Detection Intrusion Detection systems Deep Learning Anomaly Detection LSTM

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User Behavior Forensics on Encrypted Traffic in the Industrial internet of Things 20th

User Behavior Forensics on Encrypted Traffic in the Industri...

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20th IFIP WG 11.9 International conference on Digital Forensics

作者： Zhu, Zhishen Gou, Gaopeng Wang, Chonghua Zhou, Hao Lin, Chen Xiong, Gang Chinese Acad Sci Beijing Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing Peoples R China

ISBN: (纸本)9783031710278;9783031710254;9783031710247

The Industrial internet of Things is an emerging technology that has rapidly penetrated diverse applications. MindSphere from Siemens stands out as a leader among Industrial internet of Things solutions. Unlike most industrial control systems, MindSphere utilizes robust encryption protocols such as SSL, TLS and even QUIC for data transmission, presenting significant challenges to traditional traffic forensic approaches. Conducting effective user behavior forensics in this context requires the identification of relevant traffic in extensive IP network flows along with the execution of fine-grained classification tasks. The challenges significantly reduce the effectiveness of mainstream encrypted traffic classification methods on MindSphere. This chapter describes a novel flow-correlation framework that is designed to automatically extract user behavior patterns from MindSphere network traffic. The approach engages a hybrid feature set encompassing statistical and sequential data to create feature vectors for flow nodes. By leveraging traffic correlation, the approach incorporates a burst time-domain mechanism to construct a communications diagram. The constituent traffic graphs are processed using a graph neural network model to enable effective classification. Comprehensive experiments demonstrate that the approach exhibits outstanding performance that surpasses state-of-the-art methods.

关键词： Industrial internet of Things Encrypted Traffic Classification User Behavior Forensics Graph Neural network Model

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Enhancing IoT connectivity through spectrum sharing in 5G networks

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第10期15卷 5018-5029页

作者： Singh, Bablu Kumar Khatri, Narendra Pandit Deendayal Energy Univ Dept Informat & Commun Technol Gandhinagar Gujarat India Manipal Acad Higher Educ Dept Mechatron Manipal Inst Technol Manipal India

The integration of the internet of Things (IoT) into high-performance devices and monitoring systems, spanning domains such as smart building, e-health care, and smart agriculture, necessitates a critical emphasis on advancing mobile communications through efficient spectrum utilization. This research addresses pivotal challenges within agricultural IoT applications, specifically focusing on the substantial decline in spectrum efficiency observed with the increasing escalation of network bandwidth. Acknowledging the absence of comprehensive reviews on 5G resource allocation strategies in the existing literature, our study aims to contribute to a nuanced understanding of their implications for service quality. The identified research gaps underscore an urgent need for heightened efforts to optimize resource allocation in 5G networks. This investigation delves into the intricacies of spectrum sharing and real-time analysis techniques within the 5G and beyond network, with a targeted focus on augmenting agricultural IoT services. Three distinct models, namely (i) Non-Priority Algorithm (NPA), (ii) Reserved Channel Algorithm (RCA) - No Permanent Channels, and (iii) Reserved Channel Algorithm (RCA) - Permanent Channels, were meticulously designed and simulated for Agricultural IoT application scenarios. The methodology encompasses the comprehensive evaluation of performance metrics, including call blocking, termination, and handover, to strategically identify and allocate spectrum resources effectively. The research endeavors to address ongoing challenges pertaining to effective communication, standardization, and data management for diverse 5G IoT devices. In light of these persisting concerns, the study not only seeks to enhance the overall efficiency of 5G IoT networks but also proposes innovative perspectives on intelligent and ingenious spectrum allocation techniques. The anticipated outcomes pledge to optimize the utilization of limited spectrum through novel spect

关键词： internet of Things (IoT) 5G Mobile communication Monitoring and control

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Implementation and Evaluation of networked Model Predictive control System on Universal Robot 8

Implementation and Evaluation of Networked Model Predictive ...

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8th International conference on Robotics and Automation Engineering, ICRAE 2023

作者： Noroozi, Mahsa Wang, Kai Institute of Communications Technology Leibniz Universität Hannover Hanover Germany

ISBN: (纸本)9798350327656

networked control systems are closed-loop feedback control systems containing system components that may be distributed geographically in different locations and interconnected via a communication network such as the internet. The quality of network communication is a crucial factor that significantly affects the performance of remote control. This is due to the fact that network uncertainties can occur in the transmission of packets in the forward and backward channels of the system. The two most significant among these uncertainties are network time delay and packet loss. To overcome these challenges, the networked predictive control system has been proposed to provide improved performance and robustness using predictive controllers and compensation strategies. In particular, the model predictive control method is well-suited as an advanced approach compared to conventional methods. In this paper, a networked model predictive control system consisting of a model predictive control method and compensation strategies is implemented to control and stabilize a robot arm as a physical system. In particular, this work aims to analyze the performance of the system under the influence of network time delay and packet loss. Using appropriate performance and robustness metrics, an in-depth investigation of the impacts of these network uncertainties is performed. Furthermore, the forward and backward channels of the network are examined in detail in this study. © 2023 IEEE.

关键词： Time delay

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Q-Flag: QoS-Aware Flow-Rule Aggregation in Software-Defined IoT networks

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IEEE internet OF THINGS JOURNAL 2022年第7期9卷 4899-4906页

作者： Saha, Niloy Misra, Sudip Bera, Samaresh Indian Inst Technol Kharagpur Comp Sci & Engn Dept Kharagpur 721302 W Bengal India

Software-defined IoT (SDIoT) is a promising approach to address the requirements of the internet of Things (IoT), such as network management, Quality of Service (QoS), and resource utilization. The advantages of SDIoT are facilitated by the separation of the data- and the control-planes using flow-rules, that allow fine-grained control over individual flows. However, the number of flow-rules that can be placed at the switches is limited, leading to scalability issues in SDIoT. Existing approaches to flow-rule management either do not consider the impact on QoS or are applicable only to a particular topology. In this article, we propose a QoS-aware flow-rule aggregation scheme for generic network topologies, which aims to achieve a satisfactory tradeoff among flow-rule compression and its impact on the QoS of IoT traffic flows. Specifically, the proposed scheme adaptively aggregates flow-rules while considering different QoS requirements of IoT applications in the network, and the flow-rule capacity of the switches. The proposed scheme consists of the following components-1) a path selection heuristic to increase the total number of flow-rules that can be accommodated in the network and 2) a multiarm bandit-based flow-rule aggregation scheme capable of reducing the number of flow-rules, while maintaining adequate performance in terms of QoS. Experimental results using IoT traffic show that, on average, the proposed scheme is capable of reducing the average end-to-end delay and QoS-violated flows in the network by 22% and 30%, respectively, compared to the state-of-the-art schemes.

关键词： Quality of service internet of Things Routing control systems Topology network topology Delays internet of Things (IoT) Quality of Service (QoS) rule-aggregation software-defined networking (SDN)

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Distributed sliding mode control strategy for microgrids based on finite time consistency 39

Distributed sliding mode control strategy for microgrids bas...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Jun, Wang Shuang, Niu Lanzhou Univ Technol Lanzhou Gansu Peoples R China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780;9798350379228

Microgrids are essential elements within the burgeoning energy internet infrastructure. The synergy of communication networks and control systems facilitates the development of intricate microgrid management strategies;however, this integration also opens the door to various forms of interference. Moreover, inadequate primary control can lead to discrepancies between the actual frequency and voltage of the microgrid and their desired reference values, as well as imprecise distribution of active and reactive power. To tackle these challenges, an integral sliding mode-based quadratic control strategy is proposed. This tactic utilizes sparse communication with neighboring distributed power sources, thereby streamlining the complex communication network structure. It diminishes deviations caused by individual controls and counteracts the system's disturbance effects. Simultaneously, a finite-time consensus protocol is incorporated with the integral sliding mode control, which not only accelerates the convergence rate but also augments the overall convergence efficacy. The stability of the suggested control mechanism is substantiated through analytical proof, and its practicality is confirmed via simulation experiments.

关键词： Microgrid distributed two-level control finite time consistency communication network sliding-mode control convergence performance

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Evolutionary Reinforcement Learning for Multi-objective SFC Deployment 21

Evolutionary Reinforcement Learning for Multi-objective SFC ...

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21st IEEE International conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Zhao, Jialiang Wang, Ran Wu, Qiang Hao, Jie Xiong, Zehui Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore

ISBN: (纸本)9798350363999;9798350364002

In the 5G era, the diversification of application scenarios and the expansion of service demands necessitate a paradigm shift in mobile communication development to ensure Quality of Service (QoS) for a multitude of network services, thereby enhancing user experience and optimizing network resource management. To address this, we extend the traditional single-objective optimization model of Service Function Chains (SFC) to a multi-objective optimization model, incorporating considerations such as latency, deployment cost, and throughput. However, traditional evolutionary computation struggles to optimize multiple objectives simultaneously without considering initial population quality, while Deep Reinforcement Learning (DRL) faces challenges in determining weights between multiple objectives, requiring repeated training and optimization. In this paper, we propose a two-stage solution multi-objective evolutionary reinforcement learning (MOERL) to deploy SFC. In the first stage, DRL can effectively generate excellent initial population solutions, while in the second stage, using this solution as the initial solution for NSGA-ii can obtain the required placement solution and reduce computational time. Extensive experiments demonstrate that MOERL significantly reduces computational time, with its efficacy increasing as the initial population size grows. Furthermore, MOERL exhibits superior performance across three objective functions under varying request numbers, outperforming DRL by 20.9% and NSGA-ii by 38.2% in computational time reduction. Thus, MOERL adeptly meets the rigorous resource and real-time demands within 5G network environments.

关键词： network Function Virtualization Service Function Chain Multi-Objective Optimization MOERL

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Improving the performance of iioT Intrusion Detection System Using Hybrid Synthetic Data 19

Improving the Performance of IIoT Intrusion Detection System...

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19th Annual Asia Joint conference on Information Security (AsiaJCIS)

作者： Chen, Chia-Mei Hsu, Chi-Hsuen Cai, Zheng-Xun Lai, Gu-Hsin Ou, Ya-Hui Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung Taiwan Taiwan Police Coll Dept Technol Crime Invest Taipei Taiwan Natl Penghu Univ Gen Competency Ctr Magong Penghu Taiwan

ISBN: (纸本)9798350380149;9798350380156

The convergence of Industrial control systems (ICS) and Information Technology networks has enabled manufacturers to embrace modern automation technologies with the Industrial internet of Things (iioTs). However, the existing ICS network protocols lack robust security mechanisms, as they were originally designed for isolated environments. This vulnerability has exposed ICS networks to emerging cyber threats, necessitating the development of robust intrusion detection systems to promptly alert security personnel. The effectiveness of an IDS heavily depends on the quality and quantity of its training data. Insufficient and imbalanced data present challenges that impact detection rates. Generative Adversarial networks (GANs) offer a solution by generating realistic data based on existing information. This study utilizes Conditional Tabular GAN, known as CTGAN, tailored for generating tabular data, along with traditional fuzzing for synthetic data generation. Specific rules are defined to ensure data accuracy. Evaluation results demonstrate that IDS trained with a combination of CTGAN, fuzzing data, and existing data yields the best performance.

关键词： Generative Adversarial networks Synthetic Data Industrial internet of Things

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EdgeP4: In-network Edge Intelligence for a Tactile Cyber-Physical System Testbed Across Cities

EdgeP4: In-Network Edge Intelligence for a Tactile Cyber-Phy...

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IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Gnani, Nithish Krishnabharathi Pal, Joydeep Choudhary, Deepak Verma, Himanshu Rana, Soumya Kanta Mhapsekar, Kaushal S. Prabhakar, T., V Singh, Chandramani Indian Inst Sci Bengaluru India North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9798350384482;9798350384475

Tactile internet based operations, e.g., telesurgery, rely on the human operator for end-to-end closed loop control to achieve accuracy. While feedback and control are subject to network latency and packet loss, critical operations such as pose correction, fiducial marking, tremor reduction, etc. are tasks that should be carried out automatically and thus dispensing with these feedback signals from traversing the network to the other end. We design two edge intelligence algorithms hosted at P4 programmable end switches placed 400 km apart. These algorithms locally compute and command corrective signals. We implement these algorithms entirely on data plane on Netronome Agilio SmartNICs. "pose correction" is placed at the edge switch connected to an industrial robot gripping a tool. The round trip between transmitting force sensor array readings to the edge switch and receiving correct tip coordinates at the robot is shown to be less than 100 mu s. "tremor suppression" is placed at the edge switch connected to the operator. It suppresses physiological tremors, which improves the application's performance and also reduces the network load up to 99.9%. Our EdgeP4 framework allows edge intelligence modules to seamlessly switch between the algorithms based on the tasks being executed by devices connected to their ports.

关键词： Cyber-Physical systems Edge Intelligence P4 SmartNIC Tactile internet Teleoperation

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