Search Results (432)

Energy-efficient and cost-effective localization systems are attractive for large-scale tracking and localization of goods. In this paper, we propose a room-level localization system using energy-harvesting BLE tags to track the targets. We introduce the Dempster–Shafer (D–S) evidence theory combined with fingerprinting technology for location estimation. To reduce the estimation complexity, we divide the indoor environment into clear areas and fuzzy areas. The D–S algorithm is employed to locate the target in the clear areas when the targets are only detected by the anchor nodes within a single room. Conversely, fuzzy areas are characterized by RSSI signals detected by anchor nodes across multiple rooms. Then, the system integrates fingerprint matching to ensure superior positioning accuracy across the deployment. Extensive experiments demonstrate that the proposed system maintains a room-level positioning accuracy above 99% under standard test conditions within an area of approximately 2000 m² with lots of rooms. Full article

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission. The device was tested with 13 healthy subjects to validate its potential and determine the optimal number of distinct commands that users can achieve. The HDD’s portability and precision make it a promising alternative to traditional voice recognition systems, particularly for individuals with speech impairments. Full article

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption. Full article

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. Full article

Cannibalism is a behavior that different species of fish exhibit in the early stages of their life, and it has been widely reported. In Tabasco, Mexico, the ancestral species Atractosteus tropicus is farmed, which is a freshwater fish with a high nutritional and economic value. This species exhibits high cannibalistic behavior both in its larval and juvenile stages, which considerably decreases its production. Therefore, strategies have been developed to mitigate the effects of this behavior. One of them is the placement of shelters (rocks and artificial vegetation), which allow the vulnerable population to protect themselves from cannibals. The goal of this work is to study the effect of shelters on the cannibalistic behavior of the A. tropicus population through a mathematical model. The population is divided into two classes, the vulnerable population (prey) and the cannibal population (predator). Moreover, a system of ordinary differential equations is established, which is analyzed, and sufficient conditions for the coexistence of the two species are shown. Numerical simulations show coexistence by varying levels of refuge. The results obtained in this work can be applied to other populations that exhibit cannibalistic behavior. Full article

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. Full article

The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system’s ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents. Full article

The evolution of low-cost embedded systems is growing exponentially; likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art strategies available for Tiny Machine Learning (TinyML) implementation to provide a complete overview using various existing embedded vision and control systems. Our discussion divides the article into four critical aspects that high-cost and low-cost embedded systems must include to execute real-time control and image processing tasks, applying TinyML techniques: Hardware Architecture, Vision System, Power Consumption, and Embedded Software Platform development environment. The advantages and disadvantages of the reviewed systems are presented. Subsequently, the perspectives of them for the next ten years are present. A basic TinyML implementation for embedded vision application using three low-cost embedded systems, Raspberry Pi Pico, ESP32, and Arduino Nano 33 BLE Sense, is presented for performance analysis. Full article

This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user’s mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage. Full article

This article proposes an algorithm that determines the distance between two transceivers based on phase information collected in a specific frequency range. Even though we have focused on BLE technology, we do not necessarily adhere strictly to this standard regarding the procedures used to obtain phased samples. We assume that phase samples are given and propose an algorithm using a Bayesian approach to find delays in a multi-path environment. Analyzing these delays allows for determining the distance between both transceivers. We show several examples confirming the high accuracy and resolution of the proposed algorithm. Finally, we conclude with some pros and cons of the proposed solution, suggesting its use in such applications as, for example, virtual acoustics. Full article

The increasing demand for energy-efficient and interconnected smart buildings, particularly in the tourism sector, has driven the adoption of advanced wireless technologies. IoT technologies are crucial in this evolution, improving modern buildings’ functionality and operational efficiency. This study investigates the utilization of various wireless technologies within Wireless Personal Area Networks (WPANs), including Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave, in smart buildings. A multiple-criteria decision-making (MCDM) approach, specifically the PIPRECIA-S model, was applied to evaluate these technologies based on criteria such as device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability. Simulations using the PIPRECIA-S model were conducted to assess technology performance across various real-world scenarios. The results indicate that ZigBee (0.2942) and Bluetooth BLE 5.0 (0.2602) provide superior performance in terms of energy efficiency and scalability, followed by Z-Wave (0.2550) and Bluetooth BLE 4.2 (0.1906). These findings provide decision-makers with data-driven recommendations for selecting the most suitable wireless technologies for smart buildings. Full article

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service. Full article

This paper presents an innovative methodology for enhancing museum visitor experiences through personalized content delivery using a combination of explicit and implicit visitor profiling. The approach integrates Bluetooth Low Energy (BLE) smart badges to collect telemetry data, enabling precise visitor localization and dynamic group formation based on real-time proximity and shared interests. Initial profiling begins with OAuth registration and brief surveys and is then refined through the continuous tracking of exhibit interactions and the time spent at each exhibit. An AI-driven system delivers content to individual and group profiles, fostering both personalized learning and social interaction. This methodology addresses the limitations of traditional profiling by adapting to visitor behaviors in real time while maintaining a strong focus on data privacy and ethical considerations. The proposed system not only enhances engagement and satisfaction but also sets the stage for future advancements in personalized cultural experiences. Full article

Galangal (Alpinia galanga (L.) Willd) and bitter ginger (Zingiber zerumbet (L.) Roscoe) are aromatic rhizomatous plants that are typically used for culinary purposes. These rhizomatous plants have many biological properties and the potential to be beneficial for pharmaceutics. In this study, we evaluated the antioxidant and antimicrobial activities, with a specific focus on acne-causing bacteria, as well as the phytochemical constituents, of different parts of galangal and bitter ginger. The rhizomes, stems, and leaves of galangal and bitter ginger were separately dried for absolute ethanol and methanol extractions. The extracts were used to evaluate the antioxidant activity using a DPPH radical scavenging assay (0.005–5000 μg/mL), antimicrobial activity against acne-causing bacteria (0.50–31.68 mg/mL), and in vitro cytotoxicity toward human keratinocytes and fibroblasts (62.5–1000 μg/mL), as well as analyses of bioactive phytochemicals via GC-MS and LC-MS/MS (500 ppm). The ethanol and methanol extracts of bitter ginger and galangal’s rhizomes (BRhE, BRhM, GRhE, and GRhM), stems (BStE, BStM, GRhE, and GRhM), and leaves (BLeE, BLeM, GLeE, and GLeM), respectively, showed antioxidant and antimicrobial activities. The extracts of all parts of bitter ginger and galangal were greatly antioxidative with 0.06–1.42 mg/mL for the IC₅₀ values, while most of the extracts were strongly antimicrobial against C. acnes DMST 14916, particularly BRhM, BRhE, GRhM, and GRhE (MICs: 3.96–7.92 mg/mL). These rhizome extracts had also antimicrobial activities against S. aureus TISTR 746 (MICs: 7.92–31.68 mg/mL) and S. epidermidis TISTR 518 (MICs: 7.92–15.84 mg/mL). The extracts of bitter ginger and galangal rhizomes were not toxic to HaCaT and MRC-5 even at the highest concentrations. Through GC-MS and LC-MS/MS analysis, phytochemicals in bitter ginger rhizome extracts, including zerumbone, tectorigenin, piperic acid, demethoxycurcumin, and cirsimaritin, and galangal rhizome extracts, including sweroside and neobavaisoflavone, were expected to provide the antioxidant and anti-microbial activities. Therefore, the results suggest that the bitter ginger and galangal extracts could be natural anti-acne compounds with potential for pharmaceutic, cosmetic, and aesthetic applications. Full article

Sucralose stands as the most common non-nutritive sweetener; however, its metabolic effects have sparked significant controversy over the years. We aim to examine the effects of sucralose daily intake on glycemia, subjective appetite, and gut microbiota (GM) changes in subjects with overweight or obesity. In this randomized, crossover, and controlled trial, 23 participants with a body mass index between 25 kg/m² and 39.9 kg/m² will be assigned to one of two interventions to receive either sucralose (2 mg/kg/day equivalent to 40% of the acceptable daily intake) or glucose (control) for 4 weeks, each phase separated by a 4-week washout period. The glycemic response will be determined during a meal tolerance test, subjective appetite will be evaluated using a visual analog scale, and GM changes will be analyzed by next-generation sequencing of the bacterial rRNA 16S gene from fecal samples. All measures will be performed before and after intervention periods. We hypothesize that sucralose supplementation induces changes in glycemic response, subjective appetite, and gut microbiota in overweight and obese participants. This protocol was approved by the Ethics Committee of the UJAT (No. 0721) and was registered in the Australian New Zealand Clinical Trials Registry (ACTRN12621001531808). Full article