Search Results (674)

This paper presents an object detection model for logistics-centered objects deployed and used by autonomous warehouse robots. Using the Robot Operating System (ROS) infrastructure, our work leverages the set of provided models and a dataset to create a complex system that can meet the guidelines of the Autonomous Mobile Robots (AMRs). We describe an innovative method, and the primary emphasis is placed on the Logistics Objects in Context (LOCO) dataset. The importance is on training the model and determining optimal performance and accuracy for the implemented object detection task. Using neural networks as pattern recognition tools, we took advantage of the one-stage detection architecture YOLO that prioritizes speed and accuracy. Focusing on a lightweight variant of this architecture, YOLOv4 Tiny, we were able to optimize for deployment on resource-constrained edge devices without compromising detection accuracy, resulting in a significant performance boost over previous benchmarks. The YOLOv4 Tiny model was implemented with Darknet, especially for its adaptability to ROS Melodic framework and capability to fit edge devices. Notably, our network achieved a mean average precision (mAP) of 46% and an intersection over union (IoU) of 50%, surpassing the baseline metrics established by the initial LOCO study. These results demonstrate a significant improvement in performance and accuracy for real-world logistics applications of AMRs. Our contribution lies in providing valuable insights into the capabilities of AMRs within the logistics environment, thus paving the way for further advancements in this field. Full article

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development. Full article

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%. Full article

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day. Full article

Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices. Full article

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. Full article

Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on edge devices, mitigating data privacy concerns and reducing latency due to transmitting data to central servers. However, the heterogeneity of computational resources, the variability of network connections, and the mobility of IoT devices pose significant challenges to the efficient implementation of FL. This work explores advanced techniques for dynamic model adaptation and heterogeneous data management in edge computing scenarios, proposing innovative solutions to improve the robustness and efficiency of federated learning. We present an innovative solution based on Kubernetes which enables the fast application of FL models to Heterogeneous Architectures. Experimental results demonstrate that our proposals can improve the performance of FL in IoT and edge environments, offering new perspectives for the practical implementation of decentralized intelligent systems. Full article

During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection. Full article

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. Full article

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off. Full article

Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. Full article

IoT applications revolutionize industries by enhancing operations, enabling data-driven decisions, and fostering innovation. This study explores the growing potential of IoT-based facial recognition for mobile devices, a technology rapidly advancing within the interconnected IoT landscape. The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV’s Haar Cascade, Dlib’s CNN Face Detector, and Mediapipe’s Face. The resulting system demonstrates high accuracy in real-time and operates efficiently on low-powered devices like the Raspberry Pi 400. The evaluation involves the use of the multilayer perceptron (MLP) and support vector machine (SVM) classifiers. The system primarily functions as a closed set identification system within a computer engineering department at the College of Engineering, Mustansiriyah University, Iraq, allowing access exclusively to department staff for the department rapporteur room. The proposed system undergoes successful testing, achieving a maximum accuracy rate of 99.976%. Full article

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches. Full article

In the rapidly evolving domain of mobile edge–cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and user mobility. Current research has partially addressed aspects of service migration and resource allocation, yet it often falls short in thoroughly examining the nuanced interdependencies between migration strategies and resource allocation, the consequential impacts of migration delays, and the intricacies of handling incomplete tasks during migration. This study advances the discourse by introducing a sophisticated framework optimized through a deep reinforcement learning (DRL) strategy, underpinned by a Markov decision process (MDP) that dynamically adapts service migration and resource allocation strategies. This refined approach facilitates continuous system monitoring, adept decision making, and iterative policy refinement, significantly enhancing operational efficiency and reducing response times in MECC environments. By meticulously addressing these previously overlooked complexities, our research not only fills critical gaps in the literature but also enhances the practical deployment of edge computing technologies, contributing profoundly to both theoretical insights and practical implementations in contemporary digital ecosystems. Full article

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. Full article