Analysis of Single Board Architectures Integrating Sensors Technologies

A colour sensor integrated into a microcontroller platform as a reliable tool for measuring pH changes in biochemistry applications

Colorimetry is a widely used technique for optical detection in point-of-care testing and on-site detection. Although some studies employ a multiplex approach to analyse coloured solutions, many still analyse one sample at a time. We have prepared a simple and affordable colorimetric assay based on a TCS34725 colour sensor (ams-OSRAM) integrated into an M5Stack module and an RGB LED module both inserted into a 3D printed frame. We found that the colorimetric assay can be easily transferred to a colour sensing platform, and the signal range obtained using the prepared colorimeter is more than 200 times larger than that obtained using digital image colorimetry (DIC) for the same samples containing cholinesterase or urease as a model enzyme providing a change in pH of the processed solution. The assay appears to be ready for practical use.

Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT

Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols. We utilized the Internet of Things (IoT) and cost-effective sensors to capture real-time data for three different use cases: real-time monitoring of visitor counts, room and parking occupancy, and the collection of environment and climate data. Our analysis revealed that the implementation of the utilized hardware and software combination significantly improved the implementation of open smart campus systems, providing a usable visitor information system for students. Moreover, our focus on data privacy and technological versatility offers valuable insights into real-world applicability and limitations. This study contributes a novel framework that not only drives technological advancements but is also readily adaptable, improvable, and reusable across diverse settings, thereby showcasing the untapped potential of smart, sustainable systems.

The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause-effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners' practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis.

Affordable Portable Platform for Classic Photometry and Low-Cost Determination of Cholinesterase Activity

Excessive use of pesticides could potentially harm the environment for a long time. The reason for this is that the banned pesticide is still likely to be used incorrectly. Carbofuran and other banned pesticides that remain in the environment may also have a negative effect on human beings. In order to provide a better chance for effective environmental screening, this thesis describes a prototype of a photometer tested with cholinesterase to potentially detect pesticides in the environment. The open-source portable photodetection platform uses a color-programmable red, green and blue light-emitting diode (RGB LED) as a light source and a TSL230R light frequency sensor. Acetylcholinesterase from Electrophorus electricus (AChE) with high similarity to human AChE was used for biorecognition. The Ellman method was selected as a standard method. Two analytical approaches were applied: (1) subtraction of the output values after a certain period of time and (2) comparison of the slope values of the linear trend. The optimal preincubation time for carbofuran with AChE was 7 min. The limits of detection for carbofuran were 6.3 nmol/L for the kinetic assay and 13.5 nmol/L for the endpoint assay. The paper demonstrates that the open alternative for commercial photometry is equivalent. The concept based on the OS3P/OS3P could be used as a large-scale screening system.

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach's applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies.

Fluorometer Control and Readout Using an Arduino Nano 33 BLE Sense Board

We describe the control and interfacing of a fluorometer designed for aerial drone-based measurements of chlorophyll-a using an Arduino Nano 33 BLE Sense board. This 64 MHz controller board provided suitable resolution and speed for analog-to-digital (ADC) conversion, processed data, handled communications via the Robot Operating System (ROS) and included a variety of built-in sensors that were used to monitor the fluorometer for vibration, acoustic noise, water leaks and overheating. The fluorometer was integrated into a small Uncrewed Aircraft System (sUAS) for automated water sampling through a Raspberry Pi master computer using the ROS. The average power consumption was 1.1 W. A signal standard deviation of 334 µV was achieved for the fluorescence blank measurement, mainly determined by the input noise equivalent power of the transimpedance amplifier. An ADC precision of 130 µV for 10 Hz chopped measurements was achieved for signals in the input range 0-600 mV.

IoT Clusters for Enhancing Multimedia Applications

In this paper, we present a framework for exploring the spare capacity of IoT devices for clustered execution of multimedia applications. Applications of this type are usually framed with specific quality parameters that enable a desirable level of service. This means that the IoT cluster must guarantee strict quality ranges of service to work as expected. The framework is totally customizable, and QoS dimensions can be easily added or removed given their relevance in the application scenario. The achieved results clearly demonstrate the utility of using the spare capacity of IoT devices, otherwise unused, to cooperatively execute servies within the desired quality of service levels.

A Low-Latency Optimization of a Rust-Based Secure Operating System for Embedded Devices

Critical systems such as drone control or power grid control applications rely on embedded devices capable of a real-time response. While much research and advancements have been made to implement low-latency and real-time characteristics, the security aspect has been left aside. All current real-time operating systems available for industrial embedded devices are implemented in the C programming language, which makes them prone to memory safety issues. As a response to this, Tock, an innovative secure operating system for embedded devices written completely in Rust, has recently appeared. The only downside of Tock is that it lacks the low-latency real-time component. Therefore, the purpose of this research is to leverage the extended Berkeley Packet Filter technology used for efficient network traffic processing and to add the low-latency capability to Tock. The result is a secure low-latency operating system for embedded devices and microcontrollers capable of handling interrupts at latencies as low as 60 µs.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

An Energy-Friendly Scheduler for Edge Computing Systems

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.