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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Deng, Longmin Zhang, Xuemin Zheng, Qianrong Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

The security of Industrial control systems (ICS) is a critical issue that has gained increasing attention in recent years. Machine learning based intrusion detection systems (IDS) have shown promise in detecting previously unknown attacks, but their performance depends heavily on the quality of the dataset. However, most existing datasets in the field are either outdated or do not reflect the specific characteristics of ICS. We construct a dataset of ICS network traffic collected from a gas pipeline testbed, which covers a range of normal and abnormal events specific to the domain. We detail our methodology for constructing the dataset based on the gas pipeline testbed. Moreover, the systematic and fundamental comparative experiments are performed, based on the collected dataset. Specifically, we evaluate the performance of five supervised machine learning models on it while comparing their results. Our experimental results show that the dataset can serve as a valuable resource for data-driven IDS research and that Random Forest is an effective model for detecting anomalies in ICS data. © 2023 IEEE.

关键词： Intrusion detection

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Advanced Battery Energy Storage System Employing Cloud-based Platform 8

Advanced Battery Energy Storage System Employing Cloud-based...

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8th Southern Power Electronics conference and the 17th Brazilian Power Electronics conference, SPEC / COBEP 2023

作者： Gonçalves, Diuary Soares Callegari, João Marcus De Barros, Rodrigo Cássio Cupertino, Allan Fagner Pereira, Heverton Augusto CEFET-MG Department of Electrical Engineering Belo Horizonte Brazil Universidde Federal de Minas Gerais Department of Electrical Engineering Belo Horizonte Brazil Universidade Federal Do Recôncavo da Bahia Department of Electrical Engineering Cruz das Almas Brazil Universidade Federal de Juiz de Fora Department of Electrical Energy Juiz de Fora Brazil Universidade Federal de Viçosa Department of Electrical Engineering Viçosa Brazil

ISBN: (纸本)9798350321128

This work proposes an upgrade from a conventional Battery Energy Storage System (BESS) to an advanced BESS. The employed hierarchical three-level control architecture improves the system capabilities, allowing the advanced BESS to execute different ancillary services. The internet of Things (IoT)-based network also allows remote commands by external agents. The connection between the BESS and the cloud ensures operational reliability and enables advanced cloud-based features. Experimental tests showed suitable communication with the broker platform under the BESS charging procedure. Additionally, a second experiment was conducted over long periods of time to demonstrate satisfactory performance of the IoT network. © 2023 IEEE.

关键词： Auxiliary systems Battery Energy Storage System internet of Things Low-Cost Smart systems

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Automated Solar Plant using IoT Technology 4

Automated Solar Plant using IoT Technology

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4th International conference on Electrical, control and Instrumentation Engineering, ICECIE 2022

作者： Gochhait, Saikat Patil, Harish Hasarmani, Totappa Patin, Vasily Maslova, Olga Symbiosis Inst.of Digital & Telecom Mangmt Symbiosis International Deemed University Pune India Bharati Vidyapeeth College of Engineering Savitribai Phule Pune University Pune India Neurosciences Research Institute Samara State Medical University Samara Russia

ISBN: (纸本)9781665480765

The internet of Things is an evolving technology that effectively and efficiently enhances everyday activities. Reduce your living expenses by performing the necessary procedures. For synchronized communication, the tools and materials are integrated via the internet. The price of electricity is continuously increasing, so we need natural sources of electricity that can generate power without cost. In this case, photovoltaic panels produce electricity naturally from the sun. Photovoltaic cells are used in these systems that convert sunlight into electricity. In the power sector, renewable energy sources are currently an exceptional solution for filling the gap in supply. Since solar energy is widely available, unlike some resources that are geographically restricted, it is very beneficial for all renewable energy resources. An advanced plant-based monitoring system coupled with a web-based visual interface is required for this large-scale PV system distribution. In most cases, they are placed in inaccessible places so self-monitoring is impossible in a particular area. internet of Things (IoT) enabled devices can be used to build control systems. By using the internet of Things, objects can be detected and controlled remotely over a fixed network, offering opportunities for virtually integrating physical objects into computer-based systems. The use of IoT appears to be beneficial in monitoring renewable energy production. An Arduino-based system is used in this IoT app for tracking the parameters of solar panels. The system continuously monitors the solar panel, and the output power is transmitted over the internet to the IoT network A solar photovoltaic power generation plant can be significantly enhanced by using internet of Things technology for monitoring, analysing, and maintaining its performance. Technology advancements are making renewable energy equipment more affordable worldwide, encouraging the installation of large-scale solar photovoltaic systems. In t

关键词： Arduino internet of Thing Solar PV System

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Artificial Neural network controller for DC-DC Boost Converter: A Design and performance Analysis

Artificial Neural Network Controller for DC-DC Boost Convert...

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Smart Power & internet Energy systems (SPIES), International conference on

作者： Amulya Viswambharan Rachid Errouissi KSP Kiranmai Mahdi Debouza Dept. of Electrical Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This study introduces an artificial neural network (ANN)-based machine learning controller for the DC-DC boost converter. The primary controller is a Disturbance Observer based Feedback Linearization controller, which serves as an expert to provide training data for the proposed ANN. After fine-tuning the ANN is seamlessly integrated into the feedback loop, directly facilitating boost converter control. The key advantage is in the ANN ability to enhance system identification, reduce model errors, and accommodate uncertain parameters. MATLAB/Simulink simulations validate the high performance of the ANN controller, showcasing its capability to follow dynamic reference commands fast, maintain output stability amidst input voltage variations, and effectively handle constraints on maximum duty-ratio and current.

关键词： Training systems operation Training data Artificial neural networks Mathematical models Stability analysis Feedback linearization System identification Voltage control Adaptive control

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Resource Allocation of Large-Scale Communication network based on Machine Learning

Resource Allocation of Large-Scale Communication Network bas...

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Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Zhuxin Zhang Ruifeng Mo Xiaoqi Hou Xin Wei Suzhen Guo North Automatic Control Technology Research Institute Taiyuan China

With the continuous development of mobile internet technology, its demand for network performance is also increasing. Although the traditional multi-level structure of network resources enhances the scalability and independence of the network, it cannot be used effectively, nor can it intelligently configure network resources. To this end, this paper explores a new network resource allocation method, namely, a machine learning-based large-scale communication network resource allocation method. This paper first explains the concepts of communication network resource allocation and machine learning, and then proposes the principle of communication network resource allocation based on machine learning. Finally, through experiments, this paper analyzes that the bandwidth utilization rate, data delay and packet loss rate of the communication network resource allocation based on the method in this paper are 78%, 5ms and 0.05%, respectively, and the comprehensive performance is better than other communication network resource allocation methods.

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Short-Term Electricity Load Forecasting Based on Ensemble Empirical Mode Decomposition and Long Short-Term Memory Neural network 7

Short-Term Electricity Load Forecasting Based on Ensemble Em...

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7th IEEE International conference on Energy internet, ICEI 2023

作者： Xu, Haiyan Zhang, Yong Zhao, Yong School of Electrical and Control Engineering Shenyang Jianzhu University China School of Resources and Civil Engineering Northeastern University China

ISBN: (纸本)9798350386684

Electricity load forecasting forms a basis for planning and economic operation of power systems, so accurate load forecasting is conducive to improving the safety and stability thereof, however, owing to load data being non-linear and fluctuating, electricity load forecasting is difficult. Therefore, a novel hybrid model based on ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM), namely the EEMD-LSTM model was proposed to forecast short-term electricity load. Firstly, by using EEMD, the original load series was broken down into a residual error component (Re) and a sequence of intrinsic mode functions (IMFs) with varying frequencies. So converting the load series into a series that is comparatively stationary. High similarity components were aggregated using the sample entropy (SE) approach in order to simplify the model. Then, by using an LSTM method suitable for processing time series problem, appropriate forecasting models were established for each group of components and the sum of the forecast values of each sub-model was the initial forecast value. The final forecast value of load was the sum of initial forecast value and forecasting errors. Finally, electricity loads of a city in Liaoning Province, China in spring, summer, autumn, and winter were predicted. The model suggested in this study outperforms the other ten forecasting techniques in terms of performance and has less statistical errors, which raises the accuracy of electricity load forecasting, according to the results. © 2023 IEEE.

关键词： Empirical mode decomposition

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Detection and Characterization of Caviar Using a Neural network Algorithm 3rd

Detection and Characterization of Caviar Using a Neural Netw...

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Agriculture Digitalization and Organic Production - Proceedings of the third International conference on Agriculture Digitalization and Organic Production ADOP 2023

作者： Meshcheryakov, Roman Rusakov, Konstantin Tevyashov, Gleb Myshkin, Aleksej V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya St. Moscow117997 Russia 40a Rybnoe Dmitrovskij District Moscow Region141821 Russia

ISBN: (纸本)9789819941643

Conservation and increase of fish resources is one of the priority tasks of modern society. Due to agricultural activities, the impact of the construction of hydroelectric power stations, as well as the widespread presence of poachers have led to the fact that fish resources need to be replenished in special fisheries. The article points out the importance and conservation of fish resources around the world, as well as the priority of caviar counting in the breeding of sturgeon species. Conducting such activities on the farms is necessary to monitor the impact and effectiveness of the use of various feeds, changing conditions, etc. Traditionally, two methods are used at enterprises to count caviar—by weight and volume, in addition, the von Bayer method with the use of a special tool is used in practice to count red caviar. The paper also considers high-performance digital methods of caviar counting, outstanding and requiring technical solutions to perform a design for automatic counting using neural network algorithms. An original design using a camera with internet access is proposed. Results of experimental systems for sturgeon fish caviar reproduction are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fish

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Age-oriented Access control in GEO/LEO Heterogeneous network for Marine IoRT

Age-oriented Access Control in GEO/LEO Heterogeneous Network...

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IEEE Global Communications conference (GLOBECOM)

作者： Cai, Yi Wu, Shaohua Luo, Jiping Jiao, Jian Zhang, Ning Zhang, Qinyu Harbin Inst Technol Dept Elect Engn Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781665435406

Satellite communication is regarded as a promising technique for providing connectivity in remote areas, which creates opportunities for data collection and transmission in marine internet-of-Remote-Things (IoRT) networks. Most existing investigations in the field of satellite access control focus on communication throughput and transmission delay. However, the freshness of information and the heterogeneous satellite networks are rarely considered. To this end, we first present a satellite-based marine IoRT system, where a GEO/LEO heterogeneous network is considered to harness the full potential of existing satellite systems, and the age-of-information (AoI) is introduced to characterize the freshness of the status update information generated by IoRT devices. Then, an optimal age-oriented access control problem is formulated to maintain the freshness of information in the long term. We transform this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) problem and solve it by leveraging the deep reinforcement learning (DRL) framework. Simulation results show that the proposed strategy significantly outperforms the state-of-the-art ones in terms of long-term AoI performance. Moreover, the proposed strategy could make cooperative access decisions and obtain an excellent trade-off between satellites on different layers.

关键词： internet of Remote Things (IoRT) heterogeneous satellite network age of information (AoI) deep reinforcement learning (DRL)

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On deep reinforcement learning security for Industrial internet of Things

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COMPUTER COMMUNICATIONS 2021年 168卷 20-32页

作者： Liu, Xing Yu, Wei Liang, Fan Griffith, David Golmie, Nada Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA NIST Gaithersburg MD 20899 USA

The Industrial internet of Things (iioT), also known as Industry 4.0, empowers manufacturing and production processes by leveraging automation and internet of Things (IoT) technologies. In iioT, the information communication technologies enabled by IoT could greatly improve the efficiency and timeliness of information exchanges between both vertical and horizontal system integrations. Likewise, machine learning algorithms, particularly Deep Reinforcement Learning (DRL), are viable for assisting in automated control of complex iioT systems, with the support of distributed edge computing infrastructure. Despite noticeable performance improvements, the security threats brought by massive interconnections in IoT and the vulnerabilities of deep neural networks used in DRL must be thoroughly investigated and mitigated before widespread deployment. Thus, in this paper we first design a DRL-based controller that could be deployed at edge computing server to enable automated control in an iioT context. We then investigate malicious behaviors of adversaries with two attacks: (i) function-based attacks that can be launched during training phase and (ii) performance-based attacks that can be launched after training phase, to study the security impacts of vulnerable DRL-based controllers. From the adversary's perspective, maximum entropy Inverse Reinforcement Learning (IRL) is used to approximate a reward function through observation of system trajectories under the control of trained DRL-based controllers. The approximated reward function is then used to launch attacks by the adversary against the Deep Q network (DQN)-based controller. Via simulation, we evaluate the impacts of our two investigated attacks, finding that attacks are increasingly successful with increasing accuracy of the control model. Furthermore, we discuss some tradeoffs between control performance and security performance of DRL-based iioT controllers, and outline several future research directions to secure

关键词： Industrial internet of Things Deep reinforcement learning Inverse reinforcement learning Security risks Edge computing

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Inductive Graph Neural network with Causal Sampling for IoT network Intrusion Detection System

Inductive Graph Neural Network with Causal Sampling for IoT ...

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2022 International conference on Computer Engineering, network and Intelligent Multimedia, CENIM 2022

作者： Purnama, Satriawan Rasyid Istiyanto, Jazi Eko Amrizal, Muhammad Alfian Handika, Vian Rochman, Syafiqur Dharmawan, Andi Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781665476508

The widespread use of internet of Things (IoT) devices causes many vulnerabilities. Today's network intrusion detection techniques require a data-driven approach to deal with many variations of new attacks daily. The machine learning method performs well in anomaly-based intrusion detection systems on network traffic. Recently, traditional machine learning processes network flow data without considering the network topology. Graph-based learning is capable of processing the topology of the data. An inductive Graph Neural network is used to process large graphs. However, sampling is very challenging in aggregating relevant information from the neighborhood, which causes frequent noise. We propose the application of causal sampling to the Inductive Graph Neural network model, E-GraphSAGE, to obtain relevant neighboring edges according to the causal weights. Our method was evaluated on publicly available datasets ToN-IoT with improved performance over random sampling, both with and without perturbation. © 2022 IEEE.

关键词： internet of things

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