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IoT-based remote control for robotic arm

International Journal of Systems, Control and Communications 2024年第2期15卷 146-158页

作者： Do, Tri Nhut Department of Embedded Systems and Robotics Faculty of Computer Engineering University of Information Technology Viet Nam Vietnam National University Ho Chi Minh City 1 Han Thuyen Street Linh Trung Ward Thu Duc Ho Chi Minh City71308 Viet Nam

Robotic arms can perform various tasks with high accuracy, efficiency, and safety, especially in hazardous environments. This paper presents four degrees of freedom robotic arm that can be controlled remotely by two methods: direct control via Wi-Fi and indirect control via internet. We have designed an embedded system that allows the user to manipulate the robotic arm by moving the robot hand in different directions. We have also developed two software applications for remote control: one using MIT inventor 2 tool for direct control, and another using a web-based interface for indirect control. The software applications enable the user to adjust the angles of the four servo motors that drive the robotic arm in both clockwise and counter – clockwise directions. We have conducted several experiments to demonstrate the feasibility and stability of our system under different scenarios. Our results show that our system can perform wireless control of the robotic arm effectively using various mobile devices such as phone, tablet, or computer such as laptop, personal computer. © 2024 Inderscience Enterprises Ltd.

关键词： Robotic arms

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Improving Resources in Internet of Vehicles Transportation Systems Using Markov Transition and TDMA Protocol

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IEEE Transactions on Intelligent Transportation Systems 2023年第11期24卷 13050-13067页

作者： Miri, Farimasadat Javadpour, Amir Ja'Fari, Forough Sangaiah, Arun Kumar Pazzi, Richard Faculty of Business and Information Technology OshawaL1H 7K4 Canada Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrical and Telecommunications Department Viana do Castelo4900-347 Portugal Sharif University of Technology Department of Computer Engineering Tehran14588-89694 Iran National Yunlin University of Science and Technology International Graduate Institute of Ai Yunlin64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 Lebanon

In today's world, interconnected Vehicular ad-hoc networks (VANET) and intelligent transportation systems have become more popular. Although IoV can bring many benefits for the smart cities and provide comforts for the passengers, however, the increasing needs for keeping the QoS and QoE at an acceptable level in time sensitive applications seems crucial and needs to be investigated deeply. Also, allocating the right number of resources to avoid congestions and fill the deficiencies in a distributed manner is a challenging issue. So, with the increase in users, attention must be given to Quality of Service (QoS) and resource allocation. As the vehicle network provides information to provide safety, comfort, and entertainment to drivers and passengers, they are one of the most compelling research topics in intelligent transportation systems. TDMA protocol is used in this study to increase the efficiency of the network and the quality of service it provides. To solve the synchronization problem, the Markov method predicts the size of slots and frames. The scenario field is used in the Markov application section to better predict TDMA gaps on solving the synchronization problem. Accordingly, the higher the quality of service, the lower the latency of the network, and the better the allocation of resources. Optimizing allocation and quality of service is further motivated by reducing collision between packets. In terms of its implementation, this method is divided into two components, the first being the database proposal for constructing the Markov matrix and the second being the simulation on VanetMobisim and implementation of the network in NS2. The proposed method performed better in different scenarios in terms of computational complexity, PDF, latency, and overhead, as shown in the results section. © 2000-2011 IEEE.

关键词： Quality of service

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Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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IoT networks attacks detection using multi-novel features and extra tree random - voting ensemble classifier (ER-VEC)

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16637-16651页

作者： Sarwar, Ayesha Mushtaq, Muhammad Faheem Akram, Urooj Rustam, Furqan Hamza, Ameer Rupapara, Vaibhav Ullah, Saleem Department of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Artificial Intelligence The Islamia University of Bahawalpur Bahawalpur63100 Pakistan School of Computer Science University College Dublin DublinD04 V1W8 Ireland School of Computing and Information Sciences Florida International University MiamiFL United States

Internet usage is increasing day by day all over the world and as a result, technology is also developing to make daily life appliances as smart as possible. Millions of devices are connected using IoT technology, and the vulnerabilities of these devices are still exploitable by attackers. Having access to IoT devices through a Bot Master allows the Bot Master to attack a targeted server with these devices. To detect malicious traffic in IoT networks, there is a need for an intelligent mechanism. Although there have been many studies on the detection of botnet malware, accuracy and efficiency remain a gap. This study focuses on an automatic system that can detect botnet malware with high accuracy. A new ensemble model has been proposed in this study, known as the Extra Tree Random Voting Ensemble Classifier (ER-VEC), which is a combination of two tree-based models called Extra Tree and Random Forest. The proposed model is tested on several malicious traffic in the IoT networks datasets such as IoTID20, MedBIoT, UNSW-NB15, N-BaIoT, and ER-VEC achieving 99.99%, 99.91%, 95.64%, and 100% accuracy scores, respectively. In comparison with the proposed model, other machine learning models were also employed, and ER-VEC significantly outperformed them in terms of accuracy, precision, recall, F1-score, and error rate across all datasets. In addition, we performed K-Fold cross-validation and found that ER-VEC achieved an accuracy score of 98% and a standard deviation of 0.04±. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Botnet

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Beamspace Channel Estimation Via PARAFAC Decomposition for RIS Assisted Millimeter-Wave Multiuser MISO Communications

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IEEE Transactions on Vehicular technology 2024年第5期74卷 7594-7609页

作者： Guo, Xinying Xie, Zongyuan Zhang, Jiankang Chen, Sheng Zhu, Chunhua Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing & Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

The reconfigurable intelligent surface (RIS) with massive low-cost passive reflecting elements integrated on a planar surface has the ability to favourably reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. In this work, we consider uplink (UL) channel estimation for the RIS assisted millimeter-wave multiuser multiple-input single-output beamspace system where the base station (BS) is equipped with lens antenna array. This channel state information (CSI) estimation task is extremely challenging for two reasons. First, the BS only has limited number of radio frequency chains but the size of beamspace channel is very large. Second, the number of passive components in the RIS is abundance but they lack signal processing capabilities. By exploiting the parallel factor (PARAFAC) decomposition of the received signals, we derive an iterative estimation algorithm, called unitary approximate message passing (UAMP), to accurately estimate the channels between the BS and the RIS as well as the channels between the RIS and the users. To guide the selection of the system parameters, we provide the uniqueness conditions for our PARAFAC decomposition based channel estimation. To theoretically verify the efficiency of our UAMP algorithm, the Cramér-Rao bound (CRB) of the estimation is also derived. Besides, we investigate the achievable downlink (DL) sum rate for the channel estimation obtained by the proposed algorithm by using the maximum power beam selection, the optimized phase shift matrix and the zero forcing precoding. Extensive simulation results demonstrate the excellent mean squared error (MSE) performance of our UAMP estimation algorithm. In particular, for sufficiently high UL signal-to-noise ratio, the MSE of our channel estimation reaches the CRB. Simulation results also show that the DL sum rate achieved by the estimated CSI is very close to that obtained by the perfect CSI. Theoretica

关键词： Mean square error

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Outage Performance Analysis in Cognitive RIS-NOMA System under Imperfect CSI 3

Outage Performance Analysis in Cognitive RIS-NOMA System und...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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Student modeling considering learning behavior history with deep factorization machines 27

Student modeling considering learning behavior history with ...

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Kakizaki, Toma Oeda, Shinichi Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. These models take only the questions solved by the student and the correct or incorrect answers to those questions as input;they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： DeepLearning Educational data mining Factorization machines Intelligent tutoring system Knowledge tracing Student modeling

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A student modeling method combining Deep Learning and forgetting models with Knowledge Tracing 27

A student modeling method combining Deep Learning and forget...

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Oeda, Shinichi Hasegawa, Shunichi Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

Educational Data Mining (EDM) aims to enhance education by analyzing learners' skills and question difficulty levels using machine learning methods. Knowledge Tracing (KT), a subfield of EDM, utilizes Hidden Markov Models to estimate learners' abilities and predict their performance on unseen questions. While deep learning methods, such as bi-directional RNNs, have improved KT's accuracy, they may lack interpretability from an educational psychology perspective. Item Response Theory (IRT), widely used in educational statistics, offers greater explanatory potential. This study proposes a model that integrates the concept of forgetting into IRT for improved accuracy and explainability in Knowledge Tracing using bi-directional RNNs. The forgetting concept is based on Ebbinghaus' forgetting curve theory. Three experiments were conducted using synthetic data to compare a model from a previous study, a model based on the proposed method, and a model that combines the previous study's model with IRT but excludes the forgetting concept. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Deep Learning Educational Data Mining Intelligent Tutoring System Item Response Theory Knowledge Tracing Student Modeling

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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