Search Results (17)

This article presents the development of a compact, high-precision, and energy-efficient temperature monitoring system designed for tracking applications where continuous and accurate thermal monitoring is essential. Built around the HY0020 System-on-Chip (SoC), the system integrates two bandgap-based temperature sensors—one internal to the SoC and one external (Si7020-A20)—mounted on a custom PCB and powered by a coin cell battery. A distinctive feature of the system is its support for real-time parameterization of the internal sensor, which enables advanced capabilities such as thermal profiling, cross-validation, and onboard diagnostics. The system was evaluated under both room temperature and refrigeration conditions, demonstrating high accuracy with the internal sensor showing an average error of 0.041 °C and −0.36 °C, respectively, and absolute errors below ±0.5 °C. With an average current draw of just 0.01727 mA, the system achieves an estimated autonomy of 6.6 years on a 1000 mAh battery. Data are transmitted via Bluetooth Low Energy (BLE) to a Raspberry Pi 4 gateway and forwarded to an IoT cloud platform for remote access and analysis. With a total cost of approximately EUR 20 and built entirely from commercially available components, this system offers a scalable and cost-effective solution for a wide range of temperature-sensitive applications. Its combination of precision, long-term autonomy, and advanced diagnostic capabilities make it suitable for deployment in diverse fields such as supply chain monitoring, environmental sensing, biomedical storage, and smart infrastructure—where reliable, low-maintenance thermal tracking is essential. Full article

Wireless Sensor Networks (WSNs) are widely used for environmental data collection; however, their reliance on battery power significantly limits network longevity. While energy harvesting technologies provide a sustainable power solution, conventional approaches often fail to efficiently utilize surplus energy, leading to performance constraints. This paper proposes an energy-efficient dual-mode data collection scheme that integrates Long Range Wide Area Network (LoRaWAN) and Bluetooth Low Energy (BLE) in an energy-harvesting WSN environment. The proposed method dynamically adjusts sensing intervals based on harvested energy predictions and reserves energy for urgent data transmissions. Urgent messages are transmitted via BLE using multi-hop routing with redundant paths to ensure reliability, while periodic environmental data is transmitted over LoRaWAN in a single hop to optimize energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances data collection efficiency and improves urgent message delivery reliability compared to existing approaches. Future work will focus on optimizing energy consumption for redundant urgent transmissions and integrating error correction mechanisms to further enhance transmission reliability. Full article

Edge processing is a trend in developing new technologies that leverage Artificial Intelligence (AI) without transmitting large volumes of data to centralized processing services. This technique is particularly relevant for security applications where there is a need to reduce the probability of intrusion or data breaches and to decentralize alert systems. Although drone detection has received great research attention, the ability to identify helicopters expands the spectrum of aerial threats that can be detected. In this work, we present the development of a perimeter alert system that integrates AI and multiple sensors processed at the edge. The proposed system can be integrated into a LoRa or LoRaWAN network powered by solar energy. The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. It is complemented by two PIR motion sensors and a microwave sensor with a range of up to 11 m. Additionally, the DC magnetic field is measured to identify possible sensor movements or changes caused by large bodies, and a configurable RGB light signal visually indicates motion or sound detection. The monitoring system communicates with a second MCU integrated with a LoRa or LoRaWAN communication module, enabling information transmission over distances of up to several kilometers. The system is powered by a LiPo battery, which is recharged using solar energy. The perimeter alert system offers numerous advantages, including edge processing for enhanced data privacy and reduced latency, integrating multiple sensors for increased accuracy, and a decentralized approach to improving security. Its compatibility with LoRa or LoRaWAN networks enables long-range communication, while solar-powered operation reduces environmental impact. These features position the perimeter alert system as a versatile and powerful solution for various applications, including border control, private property protection, and critical infrastructure monitoring. The evaluation results show notable progress in the acoustic detection of helicopters and drones under controlled conditions. Finally, all the original data presented in the study are openly available in an OSF repository. Full article

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the “FloatingBlue” architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE’s low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, “FloatingBlue” demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight “FloatingBlue” as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches. Full article

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications. Full article

Nowadays, smartphones have become indispensable in people’s daily work and life. Since various sensors and communication chips have been integrated into smartphones, it has become feasible to provide indoor positioning using phones. This paper proposes such a solution based on a smartphone, combining Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) in the particle filter framework to realize real-time and reliable indoor positioning. First, the smartphone’s built-in accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible method for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. However, a single positioning technology has limitations, e.g., the cumulative error of PDR and the received signal strength fluctuation of BLE. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experiments are conducted on a floor in a campus building. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is reduced by 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has a vast application prospect in indoor navigation, indoor user tracking, and interactive experience for indoor visitors. Full article

Screen-printed and inkjet-printed conductive fabric antennas have been investigated in this manuscript. The former showed optimal radiation performance after fabrication and measurement, which was the basis for developing a new fabric antenna feeding technique. The aperture-fed technique is achieved with a single coaxial cable overlayed on a cut-out slot on the ground layer of the patch antenna. The cable is connected with conductive silver-based epoxy paste with high resilience to mechanical stress. Two antenna models for Bluetooth low energy (BLE) and long-range (LoRa) wireless applications were designed, fabricated, and measured at 2.44 GHz and 868 MHz, respectively, with good impedance and radiation performance. The measured antennas operated from 2.4 to 2.48 GHz (BLE) and 853 to 886 MHz (LoRa) at −10 dB S11. Measured results also showed a 56% radiation efficiency at BLE and 44.9% at LoRa. The screen-printing procedure and feeding technique have been presented in this manuscript. Full article

The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs). Full article

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores. Full article

Wireless network devices are currently a hot topic in research related to human health, control systems, smart homes, and the Internet of Things (IoT). In the shadow of the coronavirus pandemic, they have gained even more attention. This remote and contactless distributed sensing technology enabled monitoring of vital signs in real-time. Many of the devices are battery powered, so appropriate management of available energy is crucial for lengthening autonomous operation time without affecting weight, size, maintenance requirement, and user acceptance. In this paper, we discuss energy consumption aspects of sensor data transmission using wireless Bluetooth Low Energy Mesh Long Range (BLE-M-LR) technology. Papers in the field of energy savings in wireless networks do not directly address the problem of the dependence of the energy needed for transmission on the type and degree of data preprocessing, which is the novelty and uniqueness of this work. We built and studied a prototype system designed to work as a multimodal sensing node in a compound IoT application targeted to assisted living. To analyze multiple energy-related aspects, we tested it in various operation and data transmission modes: continuous, periodic, and event-based. We also implemented and tested two alternative sensor-side processing procedures: deterministic data stream reduction and neural network-based recognition and labeling of the states. Our results reveal that event-based or periodic operation allows the node for years-long operating, and the sensor-side processing may degrade the power economy more than it benefits from savings made on transmission of concise data. Full article

Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy. Full article

Communication networks are a key element in the development of Smart Cities. This field is a constantly evolving environment, for which new protocols are constantly appearing. Due to the heterogeneous nature of the technologies, the most appropriate candidate must be selected in order to get the best performance to satisfy the application requirements. One of these protocols is Bluetooth Low Energy (BLE), particularly with the upgrades introduced in version 5.x. Its new features are focused on providing increased range, improving robustness, and expanding beaconing capabilities. Connectionless applications such as information broadcasting in Smart Cities could take advantage of this protocol. Furthermore, the wide availability on common devices (mobile phones, car infotainment, etc.), the deployment of these applications can be carried out easily and at low cost. This paper presents an experimental evaluation of the new robust, long-range radio mode of BLE over a set of Smart Cities scenarios, taking into account different conditions such as wireless interference, distances, dynamicity, etc. The results show a promising performance of the protocol even with these constraints. Full article

With the popularization of Internet-of-things (IoT) and wireless communication systems, a diverse set of applications in smart cities are emerging to improve the city-life. These applications usually require a large coverage area and minimal operation and maintenance cost. To this end, the recently emerging backscatter communication (BC) is gaining interest in both industry and academia as a new communication paradigm that provides high energy efficient communications that may even work in a battery-less mode and, thus, it is well suited for smart city applications. However, the coverage of BC in urban area deployments is not available, and the feasibility of its utilization for smart city applications is not known. In this article, we present a comprehensive coverage study of a practical cellular carrier-based BC system for indoor and outdoor scenarios in a downtown area of a Helsinki city. In particular, we evaluate the coverage outage performance of different low-power and wide area technologies, i.e., long range (LoRa) backscatter, arrow band-Internet of Things (NB-IoT), and Bluetooth low energy (BLE) based BC at different frequencies of operation. To do so, we carry out a comprehensive campaign of simulations while using a sophisticated three-dimensional (3D) ray tracing (RT) tool, ITU outdoor model, and 3rd generation partnership project (3GPP) indoor hotspot model. This study also covers the energy harvesting aspects of backscatter device, and it highlights the importance of future backscatter devices with high energy harvesting efficiency. The simulation results and discussion provided in this article will be helpful in understanding the coverage aspects of practical backscatter communication system in a smart city environment. Full article

This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content. Full article

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates. Full article