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作者： Wang, Fei Zhang, Xi Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A and M University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

We address secure communications over energy-harvesting based orthogonal frequency-division multiple-access (OFDMA) cooperative cognitive radio networks, where one primary user (PU) cooperates with several size-limited secondary users (SUs) in terms of both data transmission and energy harvesting. To improve spectrum utilization and ensure that SU can harvest as much energy as possible, we let the size-limited SUs be equipped with orthogonally dual-polarized antennas (ODPAs). Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply the power splitting technique to harvest energy from radio frequency (RF) signals radiated by the PU, and then use the harvested energy to concurrently transmit their own and the PU's data. Under our proposed framework, we develop three secure resource allocation schemes for scenarios when SUs are untrusted users, which implies that each SU may overhear the PU's and the other SUs' confidential information. For these scenarios, which has hardly been studied, we jointly optimize the allocation of relays, subcarriers, power splitting ratios, and powers when SUs adopt the decode-and-forward (DF) strategy or the amplify-and-forward (AF) strategy to relay the PU's data, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU's secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and secure resource allocation schemes through numerical analyses. © 2002-2012 IEEE.

关键词： Secure communication

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Epistemic Planning for Heterogeneous Robotic systems

arXiv

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arXiv 2023年

作者： Bramblett, Lauren Bezzo, Nicola The Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles. Copyright © 2023, The Authors. All rights reserved.

关键词： information dissemination

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年第16期37卷 9295-9308页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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Supremal and Robust Control against Actuator Jamming Attacks in Cyber-Physical systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-13页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Wisniewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Institute of Systems Engineering Macau University of Science and Technology Macau China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. IEEE

关键词： Discrete event simulation

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Proposed Flight Recorder Recommendations for Unmanned Aircraft systems Integration into the National Airspace System 43

Proposed Flight Recorder Recommendations for Unmanned Aircra...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Ogirimah, Olayemi Raza, Waleed Bawa, Gurvir Renkhoff, Justus Song, Houbing Stansbury, Richard S. Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach United States University of Maryland - Baltimore County Department of Information Systems Baltimore United States

ISBN: (纸本)9798350349610

The increasing integration of Unmanned Aircraft systems (UAS) into the National Airspace System necessitates the development of flight data recorder (FDR) recommendations tailored specifically for these systems. Traditional FDRs used in manned aviation are inadequate for capturing the flight data required for UAS operations. This study involved a detailed review of existing guidelines and regulations for manned aircraft FDRs, adapting and extending them to meet the unique needs of UAS operations. By drawing upon industry best practices and aca-demic research, a holistic framework for UAS FDRrecommendations is proposed. This framework out-lines the essential data parameters and recommended recording intervals required to facilitate detailed in-cident/accident investigations and continuous safety enhancements. Implementing UAS-specific FDR stan-dards is crucial for realizing the full potential of unmanned aviation while upholding stringent safety and operational standards. © 2024 IEEE.

关键词： Aircraft accidents

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Real-Time Services Robot with an Intelligent System Based on a Radio Frequency Identification Unit 17

Real-Time Services Robot with an Intelligent System Based on...

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17th International Conference on Developments in esystems engineering, DeSE 2024

作者： Al Mashhadany, Yousif Al-Askari, Mohanad A. Al-Ani, Abdullah Alani, Taisir Ahmed Yaseen Dawod Algburi, Sameer Department of Electrical Engineering University of Anbar Anbar Iraq Information Systems Department University of Anbar Baghdad Iraq Computer Network Department University of Anbar Anbar Iraq College of Engineering Al-Kitab University Kirkuk Iraq

ISBN: (纸本)9798350368697

Technological developments have come a long way in developing service robots and employing them to serve human needs to the fullest extent. Communication between the robot and its user is the most important aspect, and developing a system to train robots and provide them with the necessary capabilities to accomplish specific tasks is important for creating a suitable service robot. This work provides solutions to most of the challenges in developing such systems using smart systems that can implement multiple and complex commands in a short and appropriate time, such as matching data outputs with what is examined by these systems. For this reason, (RFID) was used to compare data and give commands so that the robot can move in complex environments. Another challenge to consider is keeping the robot on the specified path without deviating from its specified path, so a special sensor was developed to track the robot's deviation and correct the path. Another challenge to consider is if the robot might collide with an object on the path, this problem has been solved by a wireless sensor, all of which is processed and controlled by the microprocessor and motor control unit. Several practical tasks were implemented for the proposed model, and these tasks were in complex and different environments and under difficult conditions. The proposed service robot model was able to implement them with very high accuracy, so it can be relied upon as an advanced service robot model. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning 3

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Almasah, Tasneem S. Ebrahim, Gamal A. Abdelaal, Marwa A. The British University in Egypt Faculty of Engineering Electrical Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Sadat Academy for Management Sciences Faculty of Computers and Information Computer Science Department Cairo Egypt

ISBN: (纸本)9798350306231

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%. © 2023 IEEE.

关键词： Sentiment analysis

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