Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC)
Sociedade Brasileira de Computação - SBC
A Decision-Tree Solution for the Outdated CQI Feedback Problem in 5G Networks
Accurately reporting a Channel Quality Indicator (CQI) value that reflects the current channel condition is crucial for 5G networks. However, the delay between measuring the channel condition and effectively utilizing it by the base station can render the CQI outdated, negatively impacting UE communication. This paper proposes a Decision-Tree solution that considers Signal-to-Interference plus Noise Ratio (SINR) and user context to estimate the updated SINR for translating into a CQI value. While various machine learning (ML) models are proposed in the literature, this study focuses on decision trees, which are explainable artificial intelligence (XAI) models capable of elucidating decision-making processes. The results demonstrate that our solution achieves high accuracy and performance similar to the ideal one, with an absolute difference of only 0.001 in both throughput and spectral efficiency metrics. This underscores the feasibility of our approach in addressing the outdated CQI feedback problem.
A Fuzzy Logic Approach to Adaptive Data Rate for Mobile LoRaWAN Environments
LoRaWAN is a prominent technology designed for wireless communication over long distances, particularly suited for Internet of Things applications. A critical feature of Lo-RaWAN is its Adaptive Data Rate (ADR) mechanism, which optimizes network performance by adjusting transmission parameters. However, the standard ADR scheme often struggles in dynamic environments, particularly with mobile end devices. To overcome this limitation, this work introduces Fuzzy logic based ADR for Mobility (FADR-M), a novel ADR approach incorporating fuzzy logic to adaptively adjust transmission parameters, specifically addressing the challenges of mobility in Lo-RaWAN networks. The proposed solution was evaluated through comprehensive simulations involving various ADR schemes in scenarios considering both static and mobile devices. The results demonstrated the superior performance of FADR-M, with an increase in average PDR of approximately 22% and a reduction in average network energy consumption of around 6J compared to standard ADR in a simulation instance with 1000 mobile devices. In this same scenario, the proposed scheme proved to be energy efficient, achieving an improvement of approximately 120 bits/J compared to other schemes. Therefore, the findings suggest that the proposed FADR-M scheme is highly effective for enhancing network reliability and energy efficiency in LoRaWAN, particularly in environments with high mobility.
A Power Management Approach Resilient to Energy Harvesting Prediction Errors on Battery-Operated Cyber-Physical Systems
Power Management in battery-operated Cyber-Physical Systems is challenging due to the distinct demands of different execution profiles and variable energy harvesting results. Existing approaches often rely on predictors to estimate energy harvesting. Nevertheless, such solutions are prone to errors in scenarios with variable behavior. This work proposes a novel approach inspired by the confidence attribution mechanism introduced by Scheffel et al., which assesses prediction reliability by estimating confidence levels based on the difference between predicted and observed values, statistics extracted from the training of the predictor, and application-specific hyper-parameters that define tolerance and the overall sensibility of the solution to errors. The proposed solution considers a simple Exponentially Weighted Moving Average predictor for energy harvesting, alongside battery charge and critical operation thresholds, to dynamically optimize the selection of execution profile and maximize the system's overall utilization by adjusting energy allocation based on the current confidence of the system about its overall power state. Results demonstrate the ability of the proposed solution under diverse environmental conditions and varying prediction errors. Furthermore, the time complexity analysis corroborates the suitability of the present approach to enhance Cyber-Physical Systems reliability and performance.
An Angular Position-Based Tracking Algorithm for Geolocation in Smart Cities Applications
Tracking a vehicle in real time usually requires the transmission of the geographical coordinates of its route to the Internet. As part of a partnership to develop smart cities applications, a prototype vehicle tracking system has been developed and evaluated. The algorithm records the geolocation of the vehicle every 30 seconds, which can lead to unnecessary coordinates being sent and the communication network being overloaded. The aim of this work is to develop an algorithm that is able to determine the location of a moving object based on its angular position. The proposed algorithm uses an inertial measurement unit sensor to determine the change of direction and is able to correctly determine the route even if a coordinate is lost. We conducted an experimental study comparing the periodic approach with angular position-based approach on a route of a public bus in the city of Toledo/PR. The results show that the angle-based algorithm reduces the number of transmitted coordinates by up to 30%.
An Automatic Framework for Collaborative CPU Thread Throttling and FPGA HLS-Versioning
Cloud environments have widely adopted collaborative CPU-FPGA architectures to speed up applications by executing kernels across both devices. However, optimizing the execution on these architectures is challenging and requires smart utilization of optimization techniques depending on the application and target optimization, such as performance or energy. Additionally, incoming application requests to the Cloud with different priorities result in workloads in the form of directed acyclic graphs, which imposes an additional layer of complexity on the optimization process. To address these challenges, we propose a framework that employs Thread Throttling (i.e., artificially decreasing the number of active threads) on the CPU side and HLS (High-Level Synthesis)-versioning on the FPGA side to improve the performance and energy efficiency of CPU-FPGA Clouds. Our framework uses an offline Design Space Exploration to collect execution time and energy consumption data of kernels across a wide range of CPU and FPGA configurations. This data is used at runtime to properly apply Thread Throttling and HLS-versioning on the kernels to optimize overall execution. Our findings show that by synergistically applying Thread Throttling and HLS-versioning to the incoming kernels, the framework can improve the energy-dealy product by up to 19.5x over the default and non-optimized execution.
AttEAGNN: Attention Based Edge-Aware GNN Applied to Network Load Prediction
Graph Neural Networks (GNNs) are deep machine learning methods designed to solve tasks on complex datasets structured as graphs. Most GNNs, however, usually focus on node or whole graph representation, overlooking edge features and edge structural relationships. This paper presents AttEAGNN, an attention-based edge-aware GNN model to predict node loads in a backbone network. The proposed model can process implicit and explicit edge and node features, contributing to improved data representation. Aiming to optimize a real-world backbone network, we apply AttEAGNN to analyze and predict node loads. Our model outperforms the results of network node load predictions obtained by different state-of-the-art baselines by up to 3%. The framework we developed is publicly available for reproduction and evaluation.
Big Data Architecture for Efficient Energy Management in Multi Microgrid Scenarios
The traditional electric power distribution network is evolving to support scalable and connected services in Smart Cities (SC). The emergence of Smart Grid (SG) architecture connects Smart Meters (SM) to the Electric Power Company (EPC) using various communication technologies like Power Line Communication (PLC) and wireless networks such as 5G, LoRa, and others. However, the vast amount of data generated by this infrastructure can lead to network overload and storage and processing challenges. To address this, this paper proposes implementing a Big Data Analytics framework under the Hybrid Demand Side Management as a Service (HyDSMaaS) architecture in the Multi Microgrid scenario. This approach divides the SG into Microgrids, creating smaller groups of nodes and transforming the centralized infrastructure into a scalable and decentralized one. Additionally, a monitoring system using Grafana was implemented to manage server usage flow and monitor access and services. The framework achieved high speed compared to traditional models and demonstrated accuracy of nearly 99.9% in consumption prediction and power quality classification algorithms when operating with up to 1000 microgrids in under 1000 seconds.
CAN IoT and FreeRTOS for High-Resolution Data Collection in Battery Electric Buses: A Real Case Study in Public Transportation
This paper presents the development and validation of an innovative CAN-based IoT device designed primarily for high-resolution data collection in urban battery electric buses (BEB), facilitating the advancement of precise predictive models in smart transportation systems. Utilizing the ESP-WROOM-32 module with multi-core processing capabilities and the FreeRTOS operating system, the device excels in capturing fine-grained data critical for enhanced predictive analytics. The SAE J1939 protocol enables seamless integration with the electric CAN BUS system, ensuring comprehensive data acquisition of key parameters such as voltage, current, state of charge (SOC), state of health (SOH), battery temperature, and motor RPM. Over four months of testing at UNICAMP, the device demonstrated exceptional reliability, with 96.10% of Power Battery data falling within the expected range and 90 % to 93 % adherence for other data groups. It maintained robust connectivity and data integrity, even during operational interruptions. While offering real-time monitoring as an added benefit, the primary contribution of this system lies in its capacity to support the development of highly accurate pre-dictive maintenance models and operational trend forecasting. This paves the way for more intelligent and sustainable urban transportation solutions.
Comparing TinyML Models and Libraries for On-Device Water Potability Classification
Water pollution, mainly caused by human activities that elevate harmful substance concentrations above ideal levels, threatens both the supply and quality of drinking water and also adversely affects economic development and environmental sustainability. Machine learning, combined with TinyML and the Internet of Things, is being used to predict drinking water classification, providing a promising alternative to traditional water sample monitoring. This study aims to compare various machine learning models and TinyML libraries to solve the classification problem of water potability. The Random Forest algorithm showed the best performance in Accuracy, Precision, Recall, and Fl-Score, with Emlearn and Micromlgen libraries achieving the fastest inference time of 362 milliseconds. The multilayer perceptron model with the EmbML library used the least memory, with 283,113 bytes, and the Random Forest model with Micromlgen had the lowest energy consumption, using only 104.534 millijoules. This work can help researchers and professionals implement water potability classification systems and use TinyML in other classification problems as well.
Comparison of Neural Network Training Approaches That Preserve Physical Properties of Cyber-Physical System
This work introduces methods for validating neu-ral network solutions in the context of governing equations and Hamiltonian structures, crucial for accurately representing physical systems. The first approach focuses on the structural preservation of the Hamiltonian system, emphasizing the re-lationship between coordinates and momentum in the state-space representation. The second method utilizes Monte Carlo techniques, generating distributions of parameters and external, dissipative forces to validate the neural network model. This provides a robust framework for assessing the neural network's compliance with the governing equations of the physical system it aims to model. A case study involving a simple pendulum is presented to test the proposed validation techniques. The results highlight the importance of aligning the neural network training approach with the relevant validation criteria for the application, as different methods exhibit varying performance in terms of mean squared error, Hamiltonian structure preservation, and derivative-based verification. This nuanced approach underscores the necessity of tailoring neural network training strategies to enhance the reliability of predictions in modeling complex dynamical systems.
E-THeTA: An Efficient and Adaptive TDMA Approach for Wireless Body Area Networks
Several research efforts have proposed adaptive solutions for Time Division Multiple Access (TDMA) to satisfy time requirements and save energy on Wireless Body Area Networks (WBANs) affected by radiofrequency interferences and carrying dynamic traffic. Even with the benefits, most of these solutions waste network bandwidth, decreasing network performance and the ability to recover from some unexpected communication conditions. This paper introduces the E-THeTA, an adaptive and efficient approach based on TDMA crafted to enhance performance, save energy, and efficiently use WBAN bandwidth. Compared to IEEE 802.15.4 and IEEE 802.15.6 standards and other adaptive approaches, the proposed solution reduces message losses, consumes less energy, and maintains low communication latencies in most evaluated scenarios.
Efficient Tire Tread Depth Estimation with ESP32CAM and U-Net Based Segmentation
Tire inspection is essential for ensuring the safety and efficiency of land vehicles, such as cars and trucks. Reg-ularly checking tire tread depth helps prevent accidents like aquaplaning and reduced braking performance, and also lowers fuel consumption. Traditional estimation methods, like depth gauges, are time-consuming, especially for vehicles with multiple tires, and often imprecise due to the rounded shape of some tire treads. To address these challenges, a portable device is proposed, consisting of cameras, microcontrollers and lasers, capable of quickly capture the tire tread shape. The proposed device connects to an Android application via WiFi, offering the user a graphical interface to conduct tire tread inspections on vehicles. The device retrieves the images from the cameras and uses an artificial intelligence model to estimate the tire treads shape, which are highlighted by lasers. At the end, the device provides the tire tread depth estimation in millimeters, allowing the user to determine if the tire is still functional or not. The proposed device significantly reduces the inspection time per tire by 69.46% compared to traditional depth gauges, with a tread depth estimation error of 0.112 millimeters. The proposed device can be integrated into tire management and maintenance companies to enhance the efficiency of tread inspection processes. This innovation offers significant benefits in both scientific and economic contexts, improving overall quality control and productivity in tire inspection process.
Enabling Water Management System for Agriculture Using a Low Cost Approach
Agriculture is one of Brazil's primary sources of income and has grown steadily over the years. Despite this continuous growth, the sector faces persistent challenges related to water supply and climate change, which are expected to intensify over time, exacerbating farmers' problems. This paper presents the development of a low-cost system that leverages Internet of Things (IoT) and Machine Learning (ML) technologies to address these challenges. The system forecasts soil moisture for up to 24 hours in advance, enabling the estimation of water required for the irrigation of fruit crops. The experimental results highlight the effectiveness of integrating IoT and ML for precise water management, with the Long Short-Term Memory (LSTM) algorithm demonstrating the best performance, achieving a Mean Squared Error (MSE) of 11.9487, a Root Mean Squared Error (RMSE) of 3.4567, and an R2 Score of 99%. The proposed approach improves irrigation efficiency and provides a scalable and cost-effective solution that empowers farmers to boost agricultural productivity and sustainability.
Energy-Efficient eHealth Monitoring with LPWAN
This paper presents the design and development of a novel tool for collecting and transmitting clinical data over a Low Power Wide Area Network (LPWAN). The tool features custom-designed integration boards and power management mechanisms tailored explicitly for the cyclical operation of the integrated technologies. Prioritizing effective data transmission while minimizing energy consumption-a critical factor in healthcare applications-the tool employs two key strategies: (1) on/off circuitry for GPS and ECG components to conserve power, and (2) an adaptive power protocol that leverages message acknowledgments and the Received Signal Strength Indicator (RSSI) for dynamic transmission power adjustments. Real-world experiments conducted in a relatively dense environment provided valuable insights into the system's performance under realistic conditions, showing this to be a promising approach to saving energy and optimizing eHealth applications within cyber-physical distributed systems. The fully operational tool now serves as a valuable testbed for further developments in the e-health domain.
Exploration Tool for Collaborative Provisioning and DVFS in Heterogeneous Cloud Environments
Cloud Warehouses are evolving with diverse computational resources, including CPUs, GPUs, and accelerators, catering to a multitude of tenant applications. While this hetero-geneity promises improved performance and energy efficiency, harnessing its full potential poses challenges due to dynamic workload characteristics and variable application demands. To address this, scheduling approaches combined with optimization techniques like Dynamic Voltage and Frequency Scaling (DVFS) are crucial. However, integrating these approaches effectively can be complex, potentially leading to conflicts and diminished benefits. This paper proposes an Energy Aware Collaborative Provisioning Framework target for heterogeneous CPU-GPU cloud nodes which offline tailors the best provisioning strategy considering the target hardware to optimize the makespan and dynamically tunes the voltage and frequency scaling to reduce energy consumption. Results show the importance of strategically aligning DVFS policies with provisioning algorithms, showcasing up to a 16.08 x improvement in Energy-Delay Product (EDP), underscoring significant gains in energy efficiency and performance.
Fault Tolerance in Multiprocessor Real-Time Systems Under Partitioned EDF Scheduling
This paper addresses the problem of scheduling a set of hard real-time tasks on identical multiprocessors in the presence of faults. The solution we describe is named Fault-Tolerant partitioned Earliest Deadline First (FTpEDF. Fault tolerance is employed via task replication, for which task backups (active or passive) are assigned to processors. Passive backups are released upon error detection while active backups execute independently of the occurrence of errors. The described approach is assessed via extensive experiments. When compared to a related approach based on fixed-priority scheduling, FTpEDF has similar performance in terms of required number of processors but generates less active bacukps.
Implementation and Evaluation of MemGuard in the Bao Hypervisor
The shared resources found in modern multi-core processors, such as the main memory and cache levels, affect the predictability of real-time systems. Memory access regulation, cache partitioning, and the use of static hypervisors are up-to-date techniques to decrease the contention for shared resources in multi-core processors. Among these techniques, Bao hypervisor appears as a lightweight, static partitioning hypervisor designed to provide isolation and minimal interference, making it an ideal platform for implementing memory control mechanisms. However, Bao still does not have support for memory regulation. This paper brings a memory control mechanism based on MemGuard designed and implemented in Bao hypervisor. To validate our design and implementation, we use FreeRTOS as a guest together with three other guests running a stress benchmark and measuring the interference suffered by the FreeRTOS instance. The results have shown that the implemented memory control mechanism can regulate memory accesses while simultaneously creating as little interference as possible on real-time guests.
Landslide Risk Estimation Based on 5G Network RSSI
Landslides are downward movements of soil, rock, and debris along slopes, and pose significant risks to communities, especially in inhabited areas as they can cause severe damage, including the destruction of infrastructure and loss of life. Solutions for prediction and mitigation strategies are crucial, which often relying on rainfall forecasting and monitoring through sensor and IoT technologies. However, such solutions can be costly and challenging to implement, particularly in developing countries. By taking advantage of 5G networks, this paper proposes an innovative Received Signal Strength Indication (RSSI)-based solution to estimate the landslide risk and assist in mitigation actions in advance. Our solution also incorporates Software-Defined Networks (SDN) to manage the collected real-time data, compute the landslide risk, and notify users in the affected area. Results show that adopting 5G RSSI can significantly improve the accuracy in detecting rains and landslides, as shown by a high Pearson correlation coefficient (0.984), a Mean Squared Error (MSE) of 0.0087, and a coefficient of determination (R 2 ) of 0.6185 for the RSSI-bases solution.
Lightweight AES Algorithm for Internet of Things: An Energy Consumption Analysis
The security of IoT applications remains one of the biggest challenges, given that the vast majority of applications are developed on devices with limited computational resources and interconnected by the Internet. By 2027, the number of active IoT applications is expected to reach approximately 30 billion, with a massive amount of data being transmitted over the Internet. Therefore, this information must be protected, as a vulnerable IoT application can lead to serious issues such as data leaks and manipulations. Thus, classical cryptographic algorithms become highly costly or even unfeasible. Optimized AES algorithms are found in the literature to provide lightweight cryptography, but none evaluate energy consumption. Faced with the challenge of implementing secure IoT applications while consuming the least energy possible, this work extends the evaluation of our previously proposed lightweight AES, assessing the energy consumption of the optimizations made in both the SubBytes and MixColumns stages. With the implemented optimizations, it was possible to achieve a reduction in energy consumption of 63.87% and 70.86% on the ESP32 and Raspberry Pi Pico platforms, respectively, when compared to the original version of the AES algorithm, making this version more viable for protecting IoT applications.
Lightweight and Robust Online Learning for Energy-Aware Multicore Real-Time Schedulers
Energy-aware Multicore Real-Time Schedulers leverage Dynamic Voltage and Frequency Scaling (DVFS) and task migration to optimize energy consumption at runtime. However, mapping every performance fluctuation of task-sets becomes impractical given the complexity of the modern multicore platform. Machine Learning, particularly online learning, is a widespread alternative to make energy optimization robust to such fluctuations. Lightweight models are preferred to avoid large overheads and not disrupt critical task execution. Nevertheless, these models might fail to match performance estimations on scenarios that deviate far from the ones used to train them, potentially locking DVFS due to overestimated performance reduction and, therefore, locking online learning. This paper extends an approach for Online Learning Energy-Aware Multicore Real-Time Scheduling (OL-EAMRTS) introduced in prior work by proposing a novel unlocking procedure for online learning. The proposed unlocking procedure overcame a locking event in a new architecture and promoted an average energy saving of 17.30% for the evaluated benchmarks.
Low Power Tree Network Implementation Using Bluetooth Low Energy Multirole
This paper presents an implementation of a low power tree network solution based on Bluetooth Low Energy (BLE) device that form a tree-link topology using multirole. BLE is typically employed in star topologies, which limits its range to the maximum distance of the radio link. To overcome this limitation and extend the range while maintaining low power consumption, this work utilizes a tree topology with BLE connections. By employing a connection-oriented approach, nodes are synchronized to exchange data, minimizing their radio activity and significantly improving energy efficiency and battery life. The implementation details, including node roles and the tree formation process, are discussed. The experimental results show that the BLE tree network achieves low power consumption across all nodes, making it suitable for various IoT applications requiring extensive coverage and long battery life. We include a comparative analysis with other BLE protocols, to demonstrate the superior energy efficiency of the proposed system.
Machine Learning-Based Intrusion Detection for Automotive CAN Networks on Embedded Platforms
Modern cars are composed of electronic components that communicate using the Controller Area Network (CAN) protocol in most cases. CAN is vulnerable to several attacks, which may be dangerous and cause damage. In this paper, we evaluate five machine learning-based models for intrusion detection for two types of CAN attacks (Denial of Service and impersonation). We train and test the models on a public dataset and on a dataset from a real vehicle, demonstrating the generalization and applicability of the models. The obtained results achieve up to 100% of Fl-Score, better than related works. In addition, we embedded the models in a RISC-V platform and evaluate their execution times, proving the feasibility of applying proposed intrusion detection algorithms in a real-time scenario.
Multi-Node Allocation Heuristics for IoT Applications
The rapid and increasing technological advancement highlights the importance of the Internet of Things (IoT) in the digital transformation era, establishing an intelligent network that efficiently connects devices. In IoT networks, the nodes devices have heterogeneous configurations, covering various characteristics, such as processing and storage capacity, network bandwidth, and energy consumption. This complexity of configurations makes it a significant challenge for users interested in allocating the devices according to the applications' demands. This paper presents a Multi-Node greedy heuristic (MNA-IoT) for allocating node resources to meet application demands in IoT systems. The proposed heuristic considers that the applications have multiple objectives, and it is applied to scenarios in which application requirements may exceed the capabilities of any individual node in the IoT network. This work presents scenarios and comparisons with other heuristics: a single-node allocation heuristic, gamultobj (a genetic-based algorithm), and paretosearch (direct search-based in the solutions space). Our results show that MNA-IoT can achieve the same or even better solutions than the heuristics and attains a speedup of around 1815 and 21461 compared to gamultobj and paretosearch.
Portable Device for Faster Tire Tread Depth Estimation Using Linear CCD Sensors
To improve safety and efficiency of land vehicles, such as cars and trucks, frequent tire inspections are necessary. Regular checks of tire tread depths help prevent accidents and enhance fuel efficiency. However, traditional methods for estimating tread depth, like depth gauges, can be both time-consuming and imprecise, especially for vehicles with multiple tires. In this paper, we propose a novel portable embedded Digital Processing System (DPS) device that utilizes linear image sensors (CCDs) and active illumination to estimate the depths of all tire treads at once. The device features a microcontroller that configures the linear sensor's parameters and collects its readings. The microcontroller communicates with an Android device via a serial connection to transfer the data for processing. Additionally, the Android application offers visual feedback to the user, enabling them to conduct tire inspections on vehicles. Both the Printed Circuit Board (PCB) and the device case are designed in our laboratory, with the case being 3D printed. The proposed device achieves an error of 0.115 millimeters in tread depth estimation and consumes only 22.15% of the time required by the traditional depth gauge to estimate all the tread depths of a tire. The integration of the proposed device into tire manufacturing companies can significantly enhance the efficiency of tread inspection processes. This innovation not only improves overall quality control and productivity in tire inspection but also offers substantial benefits in both scientific and economic contexts.
Screen Scratch Detection on Credit Card Payment Terminals for Logistics Optimization
Manufacturers of credit and debit card terminals lend their products to retailers, retaining responsibility for repairing or replacing devices upon verification of malfunctions or cosmetic defects. The logistic operator, which processes up to ten thousand products daily, must assess each device's condition through tests and manual visual inspections. Depending on the evaluation, a terminal may be returned to the client or sent to the manufacturer's tech center for repair. This inspection process is highly subjective and prone to human error, as different inspectors may assess the same device inconsistently. To address these inconsistencies, we developed a visual inspection system specifically for evaluating the LCD conditions of payment termi-nals. The hardware includes a 3D-printed structure to optimize image acquisition, an active illumination scheme, and a camera to capture images of the terminals. The desktop application features a YOLOv8 object detection model to highlight defects on the terminal's screen, complemented by a user-friendly interface with various functionalities. Our system improved the efficiency and accuracy of inspections, providing operators with a more reliable tool that ensures consistency. The final model achieved a mean Average Precision (mAP) of 77.4% and a processing speed of 88 milliseconds on CPU. Additionally, the real-time application interface offers low-latency usability, further enhancing the inspection process. After thorough model training and testing conducted in collaboration with the terminal manufacturer, the system has demonstrated its reliability for deployment at the production level.
The Age and Updating Stability for a Communication Context in VANET
VANETs make it possible to execute specific vehicular applications, in particular those with the aim of turning traffic safe and avoiding congestion. However, it is difficult to maintain communication between vehicles when these vehicles move dynamically at high speeds. Context-aware systems have been studied as a way to monitor context information from sensors, which can be used by systems to adapt to changes in their environment or in application requirements. This concept is used by VANET protocols and applications in different ways. Quality of Context (QoC) metrics are used to qualify the context information considering different characteristics of sensors and of the information, being a base for systems to decide if it is safe and interesting to use specific information. In this paper, we propose QoC metrics related to the stability of the updating process for a vehicular communication context. In addition, we also analyze the Age QoC metric and show how to calculate it for distinct context information. We simulate scenarios to verify the QoC metrics behavior. The results showed that the proposed QoC metrics can properly qualify the vehicular communication context.
TinyML Applied in Hyperspectral Image Classification on COTS Microcontroller
Commercial off-the-shelf (COTS) microcontrollers used in remote sensing are increasingly being used because of the great versatility of application and development provided by this type of component. There has been a significant increase in missions using CubeSats, mainly due to the greater accessibility and flexibility of the COTS equipment used, as well as lower development costs related to this type of equipment. However, CubeSats have main constraints in power, size, and data transmission capacity. As a solution, numerous works use onboard data processing to compress or classify the captured data to reduce the use of storage memory and the amount of data sent to Earth stations. One of the types of data collected by remote sensing satellites are hyperspectral images, which despite being computationally challenging for classification in microcontrollers. Several algorithms are capable of doing hyperspectral image classification even that constrained by low-power microcontrollers with the advances in deep learning. In this sense, the aim and contribution of this study was to demonstrate and explore the feasibility and performance of implementing a deep learning model on a COTS microcontroller for hyperspectral image classification. We developed a convolutional neural network (CNN) architecture adapted for microcontrollers using the TinyML concept with the TensorFlow Lite library to demonstrate the feasibility and performance of deep learning in COTS microcontroller. The results for the overall accuracy of the embedded model were from 82.57 % to 95.81 % for three different literature datasets. The dissipated power was 105 mW for all dataset samples, with the execution time per sample varying from 12.14 ms to 93.18 ms, and the total energy consumed varying from 4.09 mJ to 7.52 mJ for the entire dataset tested.
TinyPPE: Ensuring Workplace Safety Helmet Compliance with Tiny Machine Learning
Ensuring safety in workplaces such as industry and civil construction requires the use of personal protective equipment (PPE). Enhancing compliance with PPE regulations or employing educational measures through a deployable device, which can also restrict access to hazardous areas without proper safety equipment, is crucial to mitigating workplace accidents and reducing the risk of fatalities or disability. This paper proposes the development of a device equipped with embedded machine learning models that use a camera to monitor workers and enforce the usage of PPE. These models are deployed on an Arduino Nano 33 BLE Sense, integrated with an OV7675 camera shield, to capture images and employ our trained computer vision model to classify the usage of safety helmets. The compact size of the trained and quantized model, weighing only 59 KB, taking about 595 milliseconds total time, 92.5% accuracy, demonstrates its efficiency. This innovation not only promotes the adhesion to safety protocols, but also presents significant benefits in scientific and economic contexts.
Transistor-Level Logic Gates Ranking Strategy for Selective Hardening
Application-Specific Integrated Circuits, the core of many embedded systems, are increasingly vulnerable to transient faults due to their nanometer scale, high-frequency and low-voltage operation. Traditional hardening techniques, while effective, often incur significant manufacturing costs and energy consumption. To address this, this work presents a novel method that identifies and targets the most vulnerable circuit blocks based on their transistor structure. This approach offers a efficient and cost-effective alternative to existing selective hardening methods that rely on structural metrics such as logic depth and gate fanout. The proposed ranking methodologies demonstrate a substantial potential for improving circuit reliability, with an average Mean Time Between Failures increase of 29% compared to traditional approaches. This technique can be seamlessly incorporated into existing reliability optimization workflows, providing a valuable tool for designers seeking to optimize the performance and reliability trade-off in circuit design.
Using TinyML to Classify the Flight Phases of an Unmanned Aerial Vehicle
During the development and study of an unmanned aerial vehicle (UAV) the relation between collected data and the flight phase can be a precious information. This work presents a TinyML solution for a real-time classification of flight phases, achieving an accuracy exceeding 80% with an average power consumption of only 0.33 J/s. The implementation employs a MPU6050 module to capture motion data through accelerometer and gyroscope readings, which are then processed via spectral analysis. Post-training quantization techniques are also utilized to achieve the usage of only 3.3 kB of RAM, 24.9 kB of ROM and 20 ms of latency, enabling operation within an ESP32 microcontroller. The motivation for this work stems from the requirements of the PegAzuls AeroDesign team, participating in the SAE AeroDesign competition. This approach demonstrates significant potential for application in various UAV projects, particularly where rapid and precise correlation of flight test data with specific flight phases is essential.
Wearable Sensors: Improving AI for Walking Activities Through GAN-Based Data Augmentation
Human Activity Recognition (HAR) with artificial intelligence fosters the development of innovative solutions. However, building AI models often requires a substantial amount of data and can be time-consuming. In this context, our work adopted the TimeGAN technique for data augmentation, facilitating the construction of a more efficient model. We developed a classifier that integrates both synthetic and real data. This strategy significantly reduces the time required for data collection and may accelerate the development of new wearable technologies. This approach represents a promising step in optimizing development processes in AI applications for HAR, enhancing the speed and effectiveness of technological innovation.
Wearable Stroke Alert System - New Health of Things Approach Based on Generative AI and Datafusion for Real-Time Stroke Monitoring
The Stroke is a severe neurological condition that significantly impacts the cerebral vascular system and is among the leading causes of hospitalization, permanent disability, and death worldwide. This study proposes an approach based on wearable devices applied to edge computing and data fusion for stroke classification through a predictive machine learning model for predicting stroke occurrences in patients, using monitoring and intelligent alerts based on generative AI for real-time pre-diagnosis generation. The new approach presents different models for stroke prediction using various data collected by wearable devices and databases. The proposed work achieved an accuracy of 99.02% and a precision of 98.38% with the Random Forest classifier combined with edge computing and data fusion, surpassing different state-of-the-art models, demonstrating satisfactory performance surpassing models in the literature with an accuracy of 99.54%, precision of 99.18%, and an AUC of 99.99% for stroke classification in patients. The model demonstrated effective performance for applications based on CAD systems applied to the health of things for different data.
Carlos Mauricio Jaborandy de M. Dourado Junior