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International Journal of Interactive Mobile technologies 2025年第2期19卷 126-142页

作者： Ouaissa, Mariya Ouaissa, Mariyam Computer Systems Engineering Laboratory Cadi Ayyad University Marrakech Morocco Laboratory of Information Technologies Chouaib Doukkali University El Jadida Morocco

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objec-tive is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach. © 2025 by the authors of this article. Published under CC-BY.

关键词： Phishing

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A Secure IoT Network for Healthcare systems Based on Quantum Key Distribution Protocol and AES Encryption 16th

A Secure IoT Network for Healthcare Systems Based on Quantum...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Win, Myat Su Wai, Khaing Khaing Faculty of Computer Systems and Technologies University of Computer Studies Mandalay Myanmar Department of Information Technology Support and Maintenance University of Computer Studies Yangon Myanmar

ISBN: (纸本)9789819615308

The primary function of communication through the Internet is to ensure data security. Internet of Things (IoT) devices have evolved to include embedded systems and sensors capable of connecting, gathering, and sending information to the Internet. The main goal is to secure the entire data path: sensor-to-gateway and gateway-to-server data communication. In conventional cryptosystems, the strength of the encryption key is crucial in preventing unauthorized access to sensitive data. However, its security is limited as discovering a quick way to determine the secret key can lead to a system security breach. Quantum cryptography is a unique type that utilizes a quantum characteristic in transmitting information known as photons to guarantee absolute security for the data being sent. This paper tested three Quantum Key Distribution protocols (BB84, B92, and BBM92) to determine which protocol suits IoT network security using the QuVis simulator. As a result, the B92 protocol is better than the other two protocols. This research proposes a solution to secure an IoT network using Quantum Key Distribution Protocol B92 and the Advanced Encryption Standard (AES). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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MIDDLE: A Mobility-Driven Device-Edge-Cloud Federated Learning Framework

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4589-4606页

作者： Zhang, Songli Zheng, Zhenzhe Wu, Fan Li, Bingshuai Shao, Yunfeng Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems China Huawei Technologies Huawei Noah's Ark Lab China

Federated learning (FL) can be implemented in large-scale wireless networks in a hierarchical way, introducing edge servers as relays between the cloud server and devices. These devices are dispersed within multiple clusters coordinated by edges. However, the devices are typically mobile users with unpredictable trajectories, and the impact of their mobility on the model training process is not well-studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE. MIDDLE addresses unbalanced model updates by capitalizing on model aggregation opportunities on mobile devices due to their mobility across edges. It consists of two components: on-device model aggregation, which aggregates models from different edges carried by mobile devices as they move across edges, and in-edge device selection, adjusting the current edge optimization direction through careful device selection. Theoretical analysis emphasizes that on-device model aggregation can reduce bias in model updating on edges and the cloud, thereby accelerating the FL model convergence. Building on this analysis, we introduce on-device global control averaging, modifying the training process on mobile devices and extending MIDDLE into MIDDLE+. Extensive experimental results validate that MIDDLE and MIDDLE+ can reduce the time steps to reach the target accuracy by 19.44% and 20.37% at least, respectively. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

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IEEE Robotics and Automation Letters 2025年第6期10卷 6207-6214页

作者： Horyna, Jiří Jung, Roland Weiss, Stephan Ferrante, Eliseo Saska, Martin Czech Technical University in Prague Multi-Robot Systems Group Faculty of Electrical Engineering Technicka 2 Prague Czech Republic University of Klagenfurt Control of Networked Systems Institute of Smart Systems Technologies Klagenfurt Lakeside Park Austria Vrije Universiteit Amsterdam Department of Computer Science Amsterdam Netherlands New York University Abu Dhabi Department of Computer Science United Arab Emirates Dubai Future Labs Dubai United Arab Emirates

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization. © 2016 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Modeling and Simulating more efficient and reliable fuel cell vehicles through enhancing the integration of the 6kW PEM fuel cell stack with DC/DC boost converter and BLDC motor 33

Modeling and Simulating more efficient and reliable fuel cel...

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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Mtumka, Olona Krishnamurthy, Senthil Cape Peninsula University of Technology Bellville Center for Intelligent Systems and Emerging Technologies Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

This study focuses on enhancing the integration of the PEM fuel cell, DC/DC boost converter, and BLDC motor, which help to develop more efficient and dependable fuel cell vehicles. The development of renewable energy sources for power generation has received much attention due to the depletion of fossil fuel supplies worldwide and economic difficulties. Fuel cells are one of the most efficient and cleanest power generation methods. Polymer electrolyte membrane (PEM) fuel cell is the preferred type of fuel cell for vehicles because of its high energy density, fast start-up, and lack of pollution. This paper aims to model and simulate a PEM fuel cell stack to power a Brushless DC (BLDC) motor for a fuel cell vehicle to reduce emissions in the transportation sectors. A boost converter is implemented to boost the output voltage of the PEM fuel cell stack to 100 V. The paper utilizes the trapezoidal commutation of the BLDC motor to drive the inverter. The trapezoidal commutation uses the six-switching technique to control the inverter's phase currents. The variation of fuel flow rate and drive cycle are included to observe the effect on the operation of the fuel cell stack. The paper presents the modeling and simulation of the PEM Fuel cell stack to improve power conversion, energy management, and vehicle control, helping to make fuel cell vehicles more viable for widespread adoption in the future. © 2025 IEEE.

关键词： Brushless DC motors

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Estimation of orange tree production by regression from video segments under uncontrolled conditions

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Neural Computing and Applications 2025年第5期37卷 3703-3717页

作者： Mollineda, Ramón A. Sandoval, Javier Rodríguez, Christian D. Heredia, José A. Institute of New Imaging Technologies Universitat Jaume I Castelló de la Plana Spain Department of Computer Languages and Systems Universitat Jaume I Castelló de la Plana Spain Ambimetrics S.L. Castelló de la Plana Spain Department of Industrial Systems Engineering and Design Castelló de la Plana Spain

Precision agriculture covers a wide range of information and communications technologies aimed at supporting current challenges in crop estimation, productivity increase, and food security. In particular, crop yield estimation can provide very valuable information on crop distribution, with the aim of optimising logistics and harvest timing. This paper focuses on deep learning-based regression solutions for estimating the number of visible oranges on trees, from real-world crop row videos captured by a camera placed on a farm vehicle. Count predictions based on individual frames were compared with those based on variable size sequences of frames centred on each tree (video segments). The performance of three deep neural networks designed for regression was evaluated in terms of the regression error and the uncertainty of the estimates, and differences were analysed using nonparametric hypothesis testing. Experiments were conducted on a new dataset composed of annotated video segments of orange tree rows acquired under uncontrolled conditions, which has been made publicly available. Results statistically prove the value of considering multiple frames and the feasibility of yield estimation by regression in the wild. These findings are expected to contribute to optimising decision-making in crop resource management. Unlike most efforts so far, which involve counting fruits by detection in tree images usually captured manually, this work explores counting fruits by regression on trees from real-world video data. © The Author(s) 2024.

关键词： Citrus fruits

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Rain

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Context-aware Machine Learning-based Beam Selection with Multi-Panel Devices in the Presence of Self-Blockage

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IEEE Access 2025年 13卷 79203-79216页

作者： Rezaie, Sajad Morais, Joao Alkhateeb, Ahmed Mogensen, Preben Manchon, Carles Navarro Aalborg University Formerly with Department of Electronic Systems Aalborg Denmark Nokia Aalborg Denmark Arizona State University School of Electrical Computer and Energy Engineering TempeAZ United States Aalborg University Department of Electronic Systems Aalborg Denmark Keysight Technologies Spain

Context-aware beam management in millimeter-wave (mmWave) wireless communication systems has received increasing attention over the past few years. Machine learning (ML) has played a key role in leveraging different types of context information from the device position and orientation to more ambitious scenarios using RADAR, LIDAR, or camera images. However, most studies in this area consider simplified configurations for user terminals without considering the self-blockage effects owing to the user's hand and body. This study is a step towards more realistic configurations and scenarios, where methods for location- and orientation-aware beam alignment are evaluated for multi-panel hand-held devices under mild and severe self-blockage conditions. We propose deterministic and probabilistic hand grip schemes that determine the blockage status of device panels. The probabilistic schemes are introduced to account for the inherent randomness of self-blockage owing to variations in the user's hand grip. Contrary to the blockage models that introduce attenuation in multipath components depending on their angles-of-arrival, we propose two blockage models that introduce blockage losses over all the received paths, which more realistically emulate panels blocked by "hard"hand gripping. Our numerical simulations show that the multi-panel ML-based beam alignment method is capable of leveraging the terminal's location and orientation information even under severe self-blockage conditions, achieving performance close to genie-aided alignment with just a few beam-pair measurements. © 2024 IEEE.

关键词： Hands Millimeter wave communication Array signal processing Millimeter wave radar computer architecture Accuracy Radio frequency Probabilistic logic Distance measurement Azimuth

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Interaction through bionic signals. Overview

Interaction through bionic signals. Overview

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Ralev, Ivan Krastev, Georgi University of Ruse Department of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798331515799

The paper examines the possibilities of bionic technology as a means of communication with a computer system. A classification of the most commonly used signal detection technologies is carried out. A model of a system using this technology is presented. An overview of the possibilities of the application of such systems in the daily life of the mass consumer has been carried out. © 2025 IEEE.

关键词： Human computer interaction

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Interaction through bionic signals. Overview

Interaction through bionic signals. Overview

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Ivan Ralev Georgi Krastev Department of Computer Systems and Technologies University of Ruse Ruse Bulgaria

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

关键词： Human computer interaction Electrooculography Computational modeling Entertainment industry Electrocardiography Hardware Electromyography Electroencephalography Safety Signal detection

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