Search Results (46)

Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols induces unnecessary CPU utilization. In this study, we repurpose Slowloris as an energy-oriented (E-DoS) attack that exploits pre-authentication statefulness of the most prevalent remote access protocols, the Secure Shell Protocol (SSH) and File Transfer Protocol (FTP). We employ a Raspberry Pi-based experimental setup with different software implementations of the mentioned protocols to validate our hypothesis. Our experiments confirm the susceptibility of SSH and FTP to Slowloris E-DoS attacks, and we quantify the consequential impact on power consumption. We find that the Slowloris E-DoS attack exhibits an asymmetrical nature, causing a disproportionate computational demand on victim systems compared to the resources invested by the attacker. The results of this study indicate that battery-powered single-board computers (SBCs) are critically affected by these attacks due to their limited power availability. This research demonstrates the importance of understanding and mitigating Slowloris E-DoS vulnerabilities in the SSH and FTP protocols, offering valuable insights for enhancing security measures. Our findings show that millions of SBCs worldwide may be at risk and highlight a deeper structural weakness: the stateful design of widely deployed protocols can turn service availability into an energy liability. This systemic risk extends beyond SSH and FTP, with implications for IoT devices and backends that depend on stateful communication protocols. Full article

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control strategies that enhance the reliability and autonomy of PV-powered systems. This review follows a structured methodological protocol including predefined research questions, database selection, screening criteria, and systematic categorization of studies of IoT-enabled solar microgrid applications, relying on peer-reviewed journal articles, reputable conference proceedings, and scholarly works published between 2020 and 2025. The focus centers on microcontroller-based platforms (e.g., Arduino, ESP32, NodeMCU, TTGO LoRa32) and Single-Board Computers (SBCs) (e.g., Raspberry Pi), alongside the integration of optimization algorithms with Machine Learning (ML) and Neural Network (NN) approaches. Results highlight that lightweight microcontrollers offer cost-effective monitoring, ESP32 and NodeMCU balance real-time analytics with energy efficiency, Raspberry Pi supports edge-level AI processing, and LoRa enables scalable long-range communication for remote PV systems. Furthermore, optimization algorithms (PSO, WOA-SA) and neural models (ANN, LSTM, CNN–LSTM) are explored as methods to improve forecasting accuracy, fault detection, and demand-side management. Conclusions indicate that IoT-based architectures significantly improve energy efficiency, support predictive maintenance, and enable scalable deployment of autonomous solar microgrids. The study emphasizes the necessity of hybrid IoT architectures, combining edge and cloud intelligence, to balance computational complexity, power constraints, and cybersecurity requirements. These findings provide practical insights into designing robust, cost-effective, and scalable IoT-enabled PV microgrids that contribute to decentralized and sustainable energy transitions. Full article

The proliferation of Internet of Things (IoT) devices has significantly expanded the threat landscape for malicious software (malware), rendering traditional signature-based detection methods increasingly ineffective in coping with the volume and evolving nature of modern threats. In response, researchers are utilising artificial intelligence (AI) for a more dynamic and robust malware detection solution. An innovative approach utilising AI is focusing on image classification techniques to detect malware on resource-constrained Single-Board Computers (SBCs) such as the Raspberry Pi. In this method the conversion of malware binaries into 2D images is examined, which can be analysed by deep learning models such as convolutional neural networks (CNNs) to classify them as benign or malicious. The results show that the image-based approach demonstrates high efficacy, with many studies reporting detection accuracy rates exceeding 98%. That said, there is a significant challenge in deploying these demanding models on devices with limited processing power and memory, in particular those involving of both calculation and time complexity. Overcoming this issue requires critical model optimisation strategies. Successful approaches include the use of a lightweight CNN architecture and federated learning, which may be used to preserve privacy while training models with decentralised data are processed. This hybrid workflow in which models are trained on powerful servers before the learnt algorithms are deployed on SBCs is an emerging field attacting significant interest in the field of cybersecurity. This paper synthesises the current state of the art, performance compromises, and optimisation techniques contributing to the understanding of how AI and image representation can enable effective low-cost malware detection on resource-constrained systems. Full article

Condition-Based Maintenance Plus (CBM+) has been widely adopted in aerospace and mechanical systems, but its application to single-board computers (SBCs) remains difficult due to scarce failure data and subtle degradation signatures. This study investigates CBM+ for the MVME6100 SBC using accelerated life testing (ALT) to generate degradation trajectories and capture precursor signals. Temperature–humidity cycling and vibration tests were performed, while CPU temperature, memory temperature, and output voltage were continuously monitored. Under stable operation, signals followed ambient variations and showed little statistical drift, making degradation visually indistinguishable. However, precursors emerged before failure: CPU temperature exhibited abnormal behavior during thermal cycling, while vibration stress induced communication noise and irregular thermal behavior. These findings indicate that thermal responses provide reliable precursors for electronic degradation. To evaluate data-driven detection, two neural approaches were applied: an Autoencoder (AE) trained only on normal data and a Long Short-Term Memory (LSTM) network trained on both normal and faulty datasets. The Autoencoder reliably detected anomalies via reconstruction error, while the LSTM accurately classified health states and reproduced lifecycle progression. Together, the results demonstrate that precursor-informed CBM+ is feasible for SBCs and that a hybrid AE–LSTM framework enhances prognostics and health management in mission-critical electronics. Full article

Industrial pump systems require real-time fault diagnosis for predictive maintenance, but conventional Chain-of-Thought (COT) reasoning faces computational bottlenecks when processing high-frequency vibration data. This paper proposes Vibration-Aware COT (VA-COT), a novel framework that integrates multi-domain feature fusion (time, frequency, time–frequency) with adaptive reasoning depth control. Key innovations involve expert prior-guided dynamic feature selection to optimize edge-device inputs, complexity-aware reasoning chains reducing computational steps by 40–65% through confidence-based early termination, and lightweight deployment on industrial ARM-based single-board computers (SBCs). Evaluated on a 12-class pump fault dataset (5400 samples from centrifugal/gear pumps), VA-COT achieves 93.2% accuracy surpassing standard COT (89.3%) and CNN–LSTM (Convolutional Neural Network-Long Short-Term Memory network) (91.2%), while cutting latency to <1.1 s and memory usage by 65%. Six-month validation at pump manufacturing facilities demonstrated 35% maintenance cost reduction and 98% faster diagnostics versus manual methods, proving its viability for IIoT (Industrial Internet of Things) deployment. Full article

This paper presents a novel approach for predicting Particulate Matter (PM) concentrations using mobile camera devices. In response to persistent air pollution challenges across Japan, we developed a system that utilizes cutting-edge transformer-based deep learning architectures to estimate PM values from imagery captured by smartphone cameras. Our approach employs Contrastive Language–Image Pre-Training (CLIP) as a multimodal framework to extract visual features associated with PM concentration from environmental scenes. We first developed a baseline through comparative analysis of time-series models for 1D PM signal prediction, finding that linear models, particularly NLinear, outperformed complex transformer architectures for short-term forecasting tasks. Building on these insights, we implemented a CLIP-based system for 2D image analysis that achieved a Top-1 accuracy of 0.24 and a Top-5 accuracy of 0.52 when tested on diverse smartphone-captured images. The performance evaluations on Graphics Processing Unit (GPU) and Single-Board Computer (SBC) platforms highlight a viable path toward edge deployment. Processing times of 0.29 s per image on the GPU versus 2.68 s on the SBC demonstrate the potential for scalable, real-time environmental monitoring. We consider that this research connects high-performance computing with energy-efficient hardware solutions, creating a practical framework for distributed environmental monitoring that reduces reliance on costly centralized monitoring systems. Our findings indicate that transformer-based multimodal models present a promising approach for mobile sensing applications, with opportunities for further improvement through seasonal data expansion and architectural refinements. Full article

The educational system manages extensive documentation and paperwork, which can lead to human errors and sometimes abuse or fraud, such as the falsification of diplomas, certificates or other credentials. In fact, in recent years, multiple cases of fraud have been detected, representing a significant cost to society, since fraud harms the trustworthiness of certificates and academic institutions. To tackle such an issue, this article proposes a solution aimed at recording and verifying academic records through a decentralized application that is supported by a smart contract deployed in the Ethereum blockchain and by a decentralized storage system based on Inter-Planetary File System (IPFS). The proposed solution is evaluated in terms of performance and energy efficiency, comparing the results obtained with a traditional Proof-of-Work (PoW) consensus protocol and the new Proof-of-Authority (PoA) protocol. The results shown in this paper indicate that the latter is clearly greener and demands less CPU load. Moreover, this article compares the performance of a traditional computer and two Single-Board Computers (SBCs) (a Raspberry Pi 4 and an Orange Pi One), showing that is possible to make use of the latter low-power devices to implement blockchain nodes but at the cost of higher response latency. Furthermore, the impact of Ethereum gas limit is evaluated, demonstrating its significant influence on the blockchain network performance. Thus, this article provides guidelines, useful practical evaluations and key findings that will help the next generation of green blockchain developers and researchers. Full article

The increasing availability of lightweight pre-trained models and AI execution frameworks is causing edge AI to become ubiquitous. Particularly, deep learning (DL) models are being used in computer vision (CV) for performing object recognition and image classification tasks in various application domains requiring prompt inferences. Regarding edge AI task execution platforms, some approaches show a strong dependency on cloud resources to complement the computing power offered by local nodes. Other approaches distribute workload horizontally, i.e., by harnessing the power of nearby edge nodes. Many of these efforts experiment with real settings comprising SBC (Single-Board Computer)-like edge nodes only, but few of these consider nomadic hardware such as smartphones. Given the huge popularity of smartphones worldwide and the unlimited scenarios where smartphone clusters could be exploited for providing computing power, this paper sheds some light in answering the following question: Is smartphone-based edge AI a competitive approach for real-time CV inferences? To empirically answer this, we use three pre-trained DL models and eight heterogeneous edge nodes including five low/mid-end smartphones and three SBCs, and compare the performance achieved using workloads from three image stream processing scenarios. Experiments were run with the help of a toolset designed for reproducing battery-driven edge computing tests. We compared latency and energy efficiency achieved by using either several smartphone clusters testbeds or SBCs only. Additionally, for battery-driven settings, we include metrics to measure how workload execution impacts smartphone battery levels. As per the computing capability shown in our experiments, we conclude that edge AI based on smartphone clusters can help in providing valuable resources to contribute to the expansion of edge AI in application scenarios requiring real-time performance. Full article

The field of unmanned aerial vehicles (UAVs) has experienced substantial growth, with applications expanding across diverse domains. Missions increasingly demand higher autonomy, reducing human intervention and relying more on advanced onboard systems. However, integrating hybrid power sources, especially micro-turboprop engines, into UAVs poses significant challenges due to their complexity, hindering the development of effective power management control systems. This research aims to design a control algorithm for dynamic power allocation based on UAV operational needs. A fuzzy logic-based control algorithm was implemented on the Single-Board Computer (SBC) of a micro-turbogenerator test bench, which was previously developed in an earlier study. After implementing and testing the algorithm, voltage stabilization was achieved at improved levels by tightening the membership function constraints of the fuzzy logic controller. Automating the throttle control of the Electric Ducted Fan (EDF), the test platform’s primary power consumer, enabled the electric generator’s maximum capacity to be reached. This result indicates the necessity of replacing the current electric motor with one that is capable of higher power outputs to support the system’s enhanced performance. Full article

Skin cancer is one of the most common and life-threatening diseases. In the current era, early detection remains a significant challenge, particularly in remote and underserved regions with limited internet access. Traditional skin cancer detection systems often depend on image classification using deep learning models that require constant connectivity to internet access, creating barriers in areas with poor infrastructure. To address this limitation, CNN provides an innovative solution by enabling on-device machine learning on low-computing Internet of Things (IoT) devices. This study evaluates the performance of a convolutional neural network (CNN) model trained on 10,000 dermoscopic images spanning seven classes from the Harvard Skin Lesion dataset. Unlike previous research, which seldom offers detailed performance evaluations on IoT hardware, this work benchmarks the CNN model on multiple single-board computers (SBCs), including low-computing devices like Raspberry Pi and Jetson Nano. The evaluation focuses on classification accuracy and hardware efficiency, analyzing the impact of varying training dataset sizes to assess the model’s scalability and effectiveness on resource-constrained devices. The simulation results demonstrate the feasibility of deploying accurate and efficient skin cancer detection systems directly on low-power hardware. The simulation results show that our proposed method achieves an accuracy of 98.25%, with the fastest hardware being the Raspberry Pi 5, which achieves a detection time of 0.01 s. Full article

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. Full article

Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. However, developing a monitoring system using CNNs compared to conventional machine learning models can result in higher computation costs, greater energy demand, and longer inference times. This study examines the potential of CNN architectures in developing a monitoring system based on constrained hardware. The experimentation involved testing ten CNN architectures from the PyTorch and Torchvision libraries on single-board computers: an Nvidia Jetson Nano (NJN), a Raspberry Pi 5 (RPi5), and an Orange Pi 5 (OPi5). The CNN architectures were trained using four datasets containing spectrograms of acoustic samples of different durations (30, 10, 5, or 1 s) to analyze their impact on performance. The hyperparameter search was conducted using the Optuna framework, and the CNN models were validated using k-fold cross-validation. The inference time and power consumption were measured to compare the performance of the CNN models and the SBCs. The aim is to provide a basis for developing a monitoring system for precision applications in apiculture based on constrained devices and CNNs. Full article

Nail capillaroscopic examination is an inexpensive and easily applicable method to identify capillary morphological changes in patients with conditions such as systemic sclerosis and Raynaud’s. The detection of changes in capillaries makes an important contribution to diagnosing these diseases. Capillary morphology is important in the symptoms of these diseases, and capillary diameter, visibility, distribution, length, microbleeds, blood flow, and density are important indicators in capillaroscopic evaluation. Manual examination to determine these parameters is subjective, causes inconsistent results, and is labor-intensive and time-consuming. To overcome these problems, a YOLOv8s-based system was proposed in this paper to detect the number, thickness, and density of capillaries in the nail bed. The system’s components include database systems that store the analysis results, artificial intelligence-based software that runs on the SBC (Single-Board Computer), and recorded microscope images. mAP and F1_score parameters were used to evaluate the system’s performance, and values of 0.882 and 0.83 were obtained. The proposed system is promising in improving the diagnosis process of diseases such as systemic sclerosis and Raynaud’s by providing objective measurements and the early diagnosis and monitoring of diseases. Full article

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain. Full article

Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations. Full article