Search Results (440)

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

Steel surface defect detection is essential for maintaining industrial production quality and operational safety. However, existing deep learning-based methods often encounter high computational costs, hindering their deployment on mobile devices. To effectively address this challenge, we propose a lightweight and efficient steel surface defect detection network based on feature segmentation and partially connected structures, termed LESSDD-Net. In LESSDD-Net, we first introduce a lightweight downsampling module called the cross-stage partial-based dual-branch downsampling module (CSPDDM). This module significantly reduces the number of model parameters and computational costs while facilitating more efficient downsampling operations. Next, we present a lightweight attention mechanism known as coupled channel attention (CCAttention), which enhances the model’s capability to capture essential information in feature maps. Finally, we improve the faster implementation of cross-stage partial bottleneck with two convolutions (C2f) and design a lightweight version called the lightweight and partial faster implementation of cross-stage partial bottleneck with two convolutions (LP-C2f). This module not only enhances detection accuracy but also further diminishes the model’s size. Experimental results on the data-augmented Northeastern University surface defect detection (NEU-DET) dataset indicate that the mean average precision (mAP) of LESSDD-Net improves by 3.19% compared to the baseline model YOLO11n. Additionally, in terms of model complexity, LESSDD-Net reduces the number of parameters and computational costs by 39.92% and 20.63%, respectively, compared to YOLO11n. When compared with other mainstream object detection models, LESSDD-Net achieves top detection accuracy with the highest mAP value and demonstrates significant advantages in model complexity, characterized by the lowest number of parameters and computational costs. Full article

Microservice deployment and request routing can help improve server efficiency and the performance of large-scale mobile edge computing (MEC). However, the joint optimization of microservice deployment and request routing is extremely challenging, as dynamic request routing easily results in asymmetric network structures and imbalanced microservice workloads. This article proposes multi-objective joint optimization for microservice deployment and request routing based on structural symmetry. Firstly, the structural symmetry of microservice deployment and request routing is defined, including spatial symmetry and temporal symmetry. A constrained nonlinear multi-objective optimization model was constructed to jointly optimize microservice deployment and request routing, where the structural symmetric metrics take into account the flow-aware routing distance, workload balancing, and request response delay. Secondly, an improved artificial plant community algorithm is designed to search for the optimal route to achieve structural symmetry, including the environment preparation and dependency installation, service packaging and image orchestration, arrangement configuration and dependency management, deployment execution and status monitoring. Thirdly, a benchmark experiment is designed to compare with baseline algorithms. Experimental results show that the proposed algorithm can effectively optimize structural symmetry and reduce the flow-aware routing distance, workload imbalance, and request response delay, while the computational overhead is small enough to be easily deployed on resource-constrained edge computing devices. Full article

During the process of digital-system development from prototype to live implementation, differences in user interactions, perceived usability, and overall satisfaction can emerge. These differences often arise due to various factors, which may include the fidelity of the software prototype, the limitations of the prototyping tool, and the complexity of the live digital system. Recognizing these potential usability discrepancies between prototypes and live digital systems, assessment of how well user experience (UX) test approaches, such as usability testing and the System Usability Scale (SUS), reflect the UX in using the digital-system prototype and its counterpart deployed live system emerged as an important research gap. To address this gap, this study compares usability testing and SUS results among a Figma web prototype and its counterpart live web digital system, for the telecom service extension process as a representative digital-system case study. The research study involved a testing process with a total of 10 participants across the Figma prototype and live-web-system test environments, in which different sensing devices that included versatile types of mobile phones were utilized. The research study presents usability testing results related to the overlap in perceived usability issues for the same digital-product developments in both testing environments, which are experienced on different types of mobile sensing devices. The usability testing results are presented as reports on the frequency of occurrence of web system usability issues and corresponding severity levels. The obtained results demonstrated that prototype testing is highly effective for detecting a wide range of usability issues early in the digital-product development phase. The paper also evaluates the predictive capabilities of SUS assessment for the case of the Figma web prototype and its counterpart live web system in the phase of digital-product development. The results show that the SUS evaluation, when applied to digital-system prototype testing, can provide early in the development process a reliable indication of the perceived usability of its counterpart digital system, once it is developed and deployed. The findings presented in the paper offer valuable guidance for software designers and developers seeking to make prototypes and their counterpart real digital-product deployments with improved digital-product overall user experience. Full article

Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe class imbalance between rare faults and abundant normal data, and the limited computing resources of edge devices make reliable deployment challenging. In this work, a lightweight Audio Spectrogram Transformer (Tiny-AST) is proposed for robust pump anomaly detection under imbalanced supervision. Building on the Audio Spectrogram Transformer, the internal Transformer encoder is redesigned by jointly reducing the embedding dimension, depth, and number of attention heads, and combined with a class frequency-based balanced sampling strategy and time–frequency masking augmentation. Experiments on the pump subset of the MIMII dataset across three SNR levels (−6 dB, 0 dB, 6 dB) demonstrate that Tiny-AST achieves an effective trade-off between computational efficiency and noise robustness. With 1.01 M parameters and 1.68 GFLOPs, it maintains superior performance under heavy noise (−6 dB) compared to ultra-lightweight CNNs (MobileNetV3) and offers significantly lower computational cost than standard compact baselines (ResNet18, EfficientNet-B0). Furthermore, comparisons highlight the performance gains of this lightweight supervised approach over traditional unsupervised benchmarks (e.g., autoencoders, GANs) by effectively leveraging scarce fault samples. These results indicate that a carefully designed lightweight Transformer, together with appropriate sampling and augmentation, can provide competitive acoustic anomaly detection performance while remaining suitable for deployment on resource-constrained industrial edge devices. Full article

In the field management of maize, phenomena such as missed sowing and empty seedlings directly affect the final yield. By implementing seedling replenishment activities and promptly evaluating seedling growth, maize output can be increased by improving seedling survival rates. To address the challenges posed by complex field environments (including varying light conditions, weeds, and foreign objects), as well as the performance limitations of model deployment on resource-constrained devices, this study proposes a Lightweight Real-Time You Only Look Once (LRT-YOLO) model. This model builds upon the You Only Look Once version 11n (YOLOv11n) framework by designing a lightweight, optimized feature architecture (OF) that enables the model to focus on the characteristics of small to medium-sized maize seedlings. The feature fusion network incorporates two key modules: the Feature Complementary Mapping Module (FCM) and the Multi-Kernel Perception Module (MKP). The FCM captures global features of maize seedlings through multi-scale interactive learning, while the MKP enhances the network’s ability to learn multi-scale features by combining different convolution kernels with pointwise convolution. In the detection head component, the introduction of an NMS-free design philosophy has significantly enhanced the model’s detection performance while simultaneously reducing its inference time. The experiments show that the $mA{P}_{50}$ and $mA{P}_{50:95}$ of the LRT-YOLO model reached 95.9% and 63.6%, respectively. The model has only 0.86M parameters and a size of just 2.35 M, representing reductions of 66.67% and 54.89% in the number of parameters and model size compared to YOLOv11n. To enable mobile deployment in field environments, this study integrates the LRT-YOLO model with the ByteTrack multi-object tracking algorithm and deploys it on the NVIDIA Jetson AGX Orin platform, utilizing OpenCV tools to achieve real-time visualization of maize seedling tracking and counting. Experiments demonstrate that the frame rate (FPS) achieved with TensorRT acceleration reached 23.49, while the inference time decreased by 38.93%. Regarding counting performance, when tested using static image data, the coefficient of determination ($R^2$) and root mean square error (RMSE) were 0.988 and 5.874, respectively. The cross-line counting method was applied to test the video data, resulting in an $R^2$ of 0.971 and an RMSE of 16.912, respectively. Experimental results show that the proposed method demonstrates efficient performance on edge devices, providing robust technical support for the rapid, non-destructive counting of maize seedlings in field environments. Full article

This study addresses the challenges of early recognition of fruit and vegetable diseases and pests in facility horticultural greenhouses and the difficulty of real-time deployment on edge devices, and proposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection. The method is built upon a lightweight Mobile-Transformer backbone and integrates a cross-scale lightweight attention mechanism, a small-object enhancement branch, and an alternative block distillation strategy, thereby effectively improving robustness and stability under complex illumination, high-humidity environments, and small-scale target scenarios. Systematic experimental evaluations were conducted on a greenhouse pest and disease dataset covering crops such as tomato, cucumber, strawberry, and pepper. The results demonstrate significant advantages in detection performance, with mAP@50 reaching $0.872$, mAP@50:95 reaching $0.561$, classification accuracy reaching $0.894$, precision reaching $0.886$, recall reaching $0.879$, and F1-score reaching $0.882$, substantially outperforming mainstream lightweight models such as YOLOv8n, YOLOv11n, MobileNetV3, and Tiny-DETR. In terms of small-object recognition capability, the model achieved an mAP-small of $0.536$ and a recall-small of $0.589$, markedly enhancing detection stability for micro pests such as whiteflies and thrips as well as early-stage disease lesions. In addition, real-time inference performance exceeding 20 FPS was achieved on edge platforms such as Jetson Nano, demonstrating favorable deployment adaptability. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

Sub-Saharan Africa (SSA), with an estimated population of 1.243 billion people as of December 2024, had the lowest mobile Internet penetration in the world at 29%, significantly below the global average of 58%. Moreover, SSA also had the lowest mobile data traffic per active smartphone, averaging 5 GB per month—about a quarter of the global average of 19 GB per month in 2024. This paper analyses the factors responsible for the low Internet penetration in SSA, which include limited Internet service availability, Internet device and service affordability, digital ability, government regulation and policy, and deficit of network-supporting infrastructure. The paper then discusses the popular Internet access networks in SSA and their limitations. It presents low Earth orbit (LEO) satellites as a possible access network for enhancing Internet penetration in SSA, giving examples of LEO network service deployment in some SSA countries. The paper discusses the feasible business models for LEO satellite Internet services in SSA, the challenges to LEO satellite service penetration, and possible solutions. Full article

With the increasing adoption of advanced drone technologies across diverse fields, the Internet of Drones (IoD) has emerged as a novel mobility paradigm, particularly enhancing Intelligent Transportation Systems (ITS) in urban environments. Despite its significant potential, the IoD faces substantial challenges due to inherent resource constraints such as limited computational power and energy capacity, which hinder the implementation of robust cybersecurity solutions. These limitations expose IoD networks to various security vulnerabilities and privacy threats, necessitating an exhaustive analysis and understanding of these risks. In this paper we introduce SafeIoD, a comprehensive security framework designed to establish standardized procedures for proactive risk identification in Internet of Drones (IoD) devices. It involves sequential steps to determine the trustworthiness of devices subjected to these certification. Therefore, SafeIoD seeks to ensure a basic security level before implementation in a real scenario, where the network devices are evaluated in regards to the specific security requirements. Validation through experimental testing with 15 participants across four IoD deployment scenarios and one military certification case demonstrated the framework’s effectiveness: the tool achieved 73% user satisfaction rating, successfully identified an average of 3.0 security requirements per device, and provided specific lightweight cryptographic algorithm recommendations for 62.2% of elicited requirements. In a tactical military scenario simulation, the framework accurately predicted risk propagation patterns, with COOJA network simulations confirming that implementation of framework-recommended protocols reduced successful attack propagation from 60% to below 5% of the network. Full article

Deep learning models, especially for object detection have gained immense popularity in computer vision. These models have demonstrated remarkable accuracy and performance, driving advancements across various applications. However, the high computational complexity and large storage requirements of state-of-the-art object detection models pose significant challenges for deployment on resource-constrained devices like mobile phones and embedded systems. Knowledge Distillation (KD) has emerged as a prominent solution to these challenges, effectively compressing large, complex teacher models into smaller, efficient student models. This technique maintains good accuracy while significantly reducing model size and computational demands, making object detection models more practical for real-world applications. This survey provides a comprehensive review of KD-based object detection models developed in recent years. It offers an in-depth analysis of existing techniques, highlighting their novelty and limitations, and explores future research directions. The survey covers the different distillation algorithms used in object detection. It also examines extended applications of knowledge distillation in object detection, such as improvements for lightweight models, addressing catastrophic forgetting in incremental learning, and enhancing small object detection. Furthermore, the survey also delves into the application of knowledge distillation in other domains such as image classification, semantic segmentation, 3D reconstruction, and document analysis. Full article

The increasing use of artificial intelligence (AI) and deep learning (DL) techniques has driven advances in vehicle classification and detection applications for embedded devices with deployment constraints due to computational cost and response time. In the case of urban environments with high traffic congestion, such as the city of Lima, it is important to determine the trade-off between model accuracy, type of embedded system, and the dataset used. This study was developed using a methodology adapted from the CRISP-DM approach, which included the acquisition of traffic videos in the city of Lima, their segmentation, and manual labeling. Subsequently, three SSD-based detection models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, and VGG16-SSD) were trained on the NVIDIA Jetson Orin NX 16 GB platform. The results show that the VGG16-SSD model achieved the highest average precision (mAP $\approx 90.7\%$), with a longer training time, while the MobileNetV1-SSD ($512\times 512$) model achieved comparable performance (mAP $\approx 90.4\%$) with a shorter time. Additionally, data augmentation through contrast adjustment improved the detection of minority classes such as Tuk-tuk and Motorcycle. The results indicate that, among the evaluated models, MobileNetV1-SSD ($512\times 512$) achieved the best balance between accuracy and computational load for its implementation in ADAS embedded systems in congested urban environments. Full article

This study introduces an efficient real-time lane detection and navigation system for greenhouse environments, leveraging the LaneATT architecture. Designed for deployment on the Jetson Xavier NX edge computing platform, the system utilizes an RGB camera to enable autonomous navigation in greenhouse rows. From real-world agricultural environments, data were collected and annotated to train the model, achieving 90% accuracy, 91% F1 Score, and an inference speed of 48 ms per frame. The LaneATT-based vision system was trained and validated in greenhouse environments under heterogeneous illumination conditions and across multiple phenological stages of crop development. The navigation system was validated using a commercial skid-steering mobile robot operating within an experimental greenhouse environment under actual operating conditions. The proposed solution minimizes computational overhead, making it highly suitable for deployment on edge devices within resource-constrained environments. Furthermore, experimental results demonstrate robust performance, with precise lane detection and rapid response times on embedded systems. Full article

The integration of mobile devices into aviation powering electronic flight bags, maintenance logs, and flight planning tools has created a critical and expanding cyber-attack surface. Security for these systems must be not only effective but also transparent, resource-efficient, and certifiable to meet stringent aviation safety standards. This paper presents AVI-SHIELD, a novel framework for developing high-assurance, on-device threat detection. Our methodology, grounded in the MITRE ATT&CK^® framework, models credible aviation-specific threats to generate the AviMal-TinyX dataset. We then design and optimize a set of compact, interpretable detection algorithms through quantization and pruning for deployment on resource-constrained hardware. Evaluation demonstrates that AVI-SHIELD achieves 97.2% detection accuracy on AviMal-TinyX while operating with strict resource efficiency (<1.5 MB model size, <35 ms inference time and <0.1 Joules per inference) on both Android and iOS. The framework provides crucial decision transparency through integrated, on-device analysis of detection results, adding a manageable overhead (~120 ms) only upon detection. Its successful deployment on both Android and iOS demonstrates that AVI-SHIELD can provide a uniform security posture across heterogeneous device fleets, a critical requirement for airline operations. This work provides a foundational approach for deploying certifiable, edge-based security that delivers the mandatory offline protection required for safety critical mobile aviation applications. Full article

Mental health disorders affect large numbers of people worldwide and are a major cause of long-term disability. Digital health technologies such as mobile apps and wearable devices now generate rich behavioural data that could support earlier detection and more personalised care. However, these data are highly sensitive and distributed across devices and platforms, which makes privacy protection and scalable analysis challenging; federated learning offers a way to train models across devices while keeping raw data local. When combined with edge, fog, or cloud computing, federated learning offers a way to support near-real-time mental health analysis while keeping raw data local. This review screened 1104 records, assessed 31 full-text articles using a five-question quality checklist, and retained 17 empirical studies that achieved a score of at least 7/10 for synthesis. The included studies were compared in terms of their FL and edge/cloud architectures, data sources, privacy and security techniques, and evidence for operation in real-world settings. The synthesis highlights innovative but fragmented progress, with limited work on comorbidity modelling, deployment evaluation, and common benchmarks, and identifies priorities for the development of scalable, practical, and ethically robust FL systems for digital mental health. Full article