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Narrowband Internet of Things (NB-IoT) networks are growing rapidly due to their low energy consumption and strong signal penetration. However, the technology faces key challenges, including high latency and low data rates. This article presents a cooperative solution aimed at boosting network throughput through a two-tier network architecture. These networks typically integrate cellular communication with Device-to-Device (D2D) interactions, using NB-IoT for cellular communication and Bluetooth Low Energy (BLE) for D2D connectivity. Idle nodes support the target node by assisting in data downloads or uploads. The group formation process is designed to achieve three main objectives: maximizing throughput, minimizing energy consumption, and preserving the total battery life of the group nodes connected to the base station. To optimize these multi-objective goals, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied. The algorithm produces candidate groups, and one is selected by adjusting node participation rates. Simulations show a 7.7-fold increase in throughput with only an 8% rise in energy consumption for downloads, and a 7.6-fold improvement with just a 2% energy increase for uploads, compared to the base case. [ABSTRACT FROM AUTHOR]

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Cycling is now a very popular sport and leisure activity or commuting tool around the world, with its popularity growing especially during the epidemic. The traditional bicycle depends on a chain driving mechanism to move forward (This paper is an extended version of our paper published in The 16th Intelligent Living Technology Conference, Taichung, Taiwan, 2 June 2022). However, its transmission chain is easily dirtied and loosened so that regular maintenance is highly demanded to sustain normal function. To achieve the idea of maintenance-free, self-calibrating, and efficient mechanism operation, a wireless-controlled gear shifter for shaft driven bicycles is proposed, not only to overcome the limitations of the traditional chain driving mechanism, but also to make riding control more convenient. Firstly, an actuated gear shifter module coordinated with the gear positioning system was designed. Secondly, a remote controller module with information organic light-emitting diodes (OLEDs) and shift operation buttons was developed. Both modules use independent batteries and a Bluetooth Low Energy (BLE) interface to communicate with each other for wireless shifting control. The experimental results verify the effectiveness of the proposed system in terms of accuracy, rapidness, and robustness. [ABSTRACT FROM AUTHOR]

With the rapid advancement of technology, precision agriculture, as a modern agricultural production model, has seen significant progress in recent years. Its widespread adoption is gradually transforming traditional farming methods, providing strong support for the modernization of global agriculture. In particular, the application of positioning technology plays a crucial role in precision agriculture. This paper focuses on an automated agricultural machinery positioning system based on Bluetooth technology. The system uses Bluetooth at the 2.4 GHz frequency for transmission, processing Constant Tone Extension (CTE) and Received Signal Strength Indicator (RSSI) signals collected from blind nodes. The Propagator Direct Data Acquisition (PDDA) algorithm is employed to calculate angle information from CTE signals, while the Two-Ray Ground Reflection Model is applied to manage the correlation between RSSI and distance, making it suitable for outdoor environments. These two types of data are fused for positioning, with an optimized objective function converting the positioning task into an optimization problem. An Adaptive Secretary Bird Optimization Algorithm (ASBOA) is introduced to enhance the accuracy and efficiency of the positioning process. In the simulation, anchor and blind nodes are deployed to simulate a real farm environment. Anchor nodes receive CTE and RSSI signals from blind nodes. Considering that the tags mounted on agricultural machinery are set at a fixed height in real scenarios, the simulation also fixes the tags at this height. We then compare the accuracy of five algorithms in both static and dynamic tracking. The final simulation results indicate that ASBOA achieves satisfactory high-precision positioning, both for static points and dynamic tracking, theoretically meeting the needs for continuous positioning and laying a solid foundation for future field trials. [ABSTRACT FROM AUTHOR]

Wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici (Pst) seriously affects wheat yield. We studied the fungal infection process and host response between wheat 'AvS' and its near-isogenic line 'AvS+Yr10', which formed compatible and incompatible interactions, respectively, when challenged with Pst race CYR32. The difference in reactive oxygen species (ROS) accumulation between the compatible and incompatible interactions was analyzed by measuring the hydrogen peroxide (H2O2) concentration and observing H2O2 production. Hydrogen peroxide concentrations, H2O2 accumulation areas, necrotic areas and expression of several resistance-related genes in the AvS+Yr10/CYR32 interaction were higher than those in the AvS/CYR32 interaction during the entire infection process. Pst growth was effectively inhibited in the AvS+Yr10 all-stage resistant wheat line. Thus, ROS accumulation is crucial in wheat defence against pathogens. [ABSTRACT FROM AUTHOR]

The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for real-time monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research.

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. [ABSTRACT FROM AUTHOR]

Traditional wisdom for network management allocates network resources separately for the measurement and communication tasks. Heavy measurement tasks may compete limited resources with communication tasks and significantly degrade overall network performance. It is therefore challenging for the interference graph, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, our core insight is to use power as a new dimension for interference graph estimation (IGE) such that IGE can be done simultaneously with the communication tasks using the same frequency-time resources. We propose to marry power-domain IGE with concurrent flooding to achieve simultaneous measurement and communication in BLE networks, where the power linearity prerequisite for power-domain IGE holds naturally true in concurrent flooding. With extensive experiments, we conclude the necessary conditions for the power linearity to hold and analyze several non-linearity issues of power related to hardware imperfections. We design and implement network protocols and power control algorithms for IGE in multi-hop BLE networks and conduct experiments to show that the marriage is mutually beneficial for both IGE and concurrent flooding. Furthermore, we demonstrate the potential of IGE in improving channel map convergence and convergecast in BLE networks. [ABSTRACT FROM AUTHOR]

Stem rust of barley and wheat is caused by the obligate pathogen Puccinia graminis f. sp. tritici (Pgt) and stem rust of oat is caused by P. graminis f. sp. avenae (Pga). Stem rust can be a very destructive disease of cereal crops and, until the development of resistant host varieties, was responsible for millions of dollars of losses to producers. Genetic host resistance is the most common and effective method to control stem rust on cereals. Knowledge of the virulence genes and races present in the pathogen population is important to identify effective host resistance genes for use by cereal plant breeders. The objectives of this study were to determine the incidence and severity of P. graminis in wheat, barley and oat in Manitoba, Canada, and assess the virulence frequencies and races of isolates collected from Manitoba, Saskatchewan and Ontario, Canada during 2020 to 2022. Stem rust incidence and severity was very light in Manitoba in 2020, 2021 and 2022. Virulence was not observed on resistance genes Sr30, Sr24 and Sr31 from Canadian collections of Pgt isolates made during 2020 to 2022. Race QFCSC was the most common race of Pgt in Canada, and races MCCFC and TPMKC were also common in Manitoba in 2022. Virulence to resistance genes Pg6, Pg10 and Pg16 was not observed among Pga isolates. The most common race of Pga from Manitoba and Saskatchewan was TGN, followed by SGB and TJS. The most frequent Pga races were TDJ, TDD and TGN in Ontario. [ABSTRACT FROM AUTHOR]

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily deployable, flexible, and efficient. This paper focuses on indoor positioning solely based on BLE. Motivated by two observations, namely, that (i) involving more anchor nodes can enhance positioning accuracy, and that (ii) narrowing the area for unknown location determination can also lead to improved accuracy, a new distance-based method, the Weighted Centroid of the Convex Polygon (WC-CP), is proposed. While it is generally acknowledged that incorporating more anchor nodes can enhance indoor positioning performance, the current state of the art lacks a robust methodology for selecting and utilizing these nodes. The WC-CP approach addresses this gap by introducing a systematic and efficient method for identifying and employing the most suitable anchor nodes. By avoiding nodes that could potentially introduce significant errors or lead to incorrect localization, our method ensures more accurate and reliable indoor positioning. The efficacy of WC-CP is demonstrated in an indoor environment, achieving an RMSE of 1.35 m. This result shows significant improvements over three state-of-the-art approaches, about 34.15% better than LSBM, 32.50% better than TWCBM, and 30.05% better than ITWCBM. These findings underscore the potential of WC-CP for enhanced accuracy and reliability in indoor positioning based on BLE. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

One of the significant advantages of technological evolution is the greater ease of collecting and analyzing data. Miniaturization, wireless communication protocols and IoT allow the use of sensors to collect data, with all the potential to support decision making in real time. In this paper, we describe the design and implementation of a digital solution to guide the intensity of training or physical activity, based on heart rate wearable sensors applied to participants in group sessions. Our system, featuring a unified engine that simplifies sensor management and minimizes user disruption, has been proven effective for real-time monitoring. It includes custom alerts during variable-intensity workouts, and ensures data preservation for subsequent analysis by physiologists or clinicians. This solution has been used in sessions of up to six participants and sensors up to 12 m away from the gateway device. We describe some challenges and constraints we face in collecting data from multiple and possibly different sensors simultaneously via Bluetooth Low Energy, and the approaches we follow to overcome them. We conduct an in-depth questionnaire to identify potential obstacles and drivers for system acceptance. We also discuss some possibilities for extension and improvement of our system. [ABSTRACT FROM AUTHOR]

As communication capabilities of mobile devices continue to advance, ensuring reliability and security has become increasingly crucial. The aerial tracking system presented in this paper provides a useful solution for tracking and tracing objects in various scenarios. To ensure reliability and security, the system incorporates appropriate mechanisms, including Lightweight Cryptography, to prioritize confidentiality and integrity. The Android component of the system has two modes of operation: Tracker Mode, running on a smartphone mounted on a drone (RPA), and Client Mode, running on mobile devices on the ground. In Client Mode, users transmit their positioning and trajectory information via Bluetooth Low Energy beacon mode, which is then relayed to the server backend via the 4G/5G network once the RPA enters an area with coverage. The system provides a reliable and secure solution for situations where tracking and tracing are essential, such as the supervision and control of public areas with capacity control or tracking and localizing people in isolated environments. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m. [ABSTRACT FROM AUTHOR]

The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for realtime monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research. [ABSTRACT FROM AUTHOR]

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone's internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. [ABSTRACT FROM AUTHOR]

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Cycling is now a very popular sport and leisure activity or commuting tool around the world, with its popularity growing especially during the epidemic. The traditional bicycle depends on a chain driving mechanism to move forward (This paper is an extended version of our paper published in The 16th Intelligent Living Technology Conference, Taichung, Taiwan, 2 June 2022). However, its transmission chain is easily dirtied and loosened so that regular maintenance is highly demanded to sustain normal function. To achieve the idea of maintenance-free, self-calibrating, and efficient mechanism operation, a wireless-controlled gear shifter for shaft driven bicycles is proposed, not only to overcome the limitations of the traditional chain driving mechanism, but also to make riding control more convenient. Firstly, an actuated gear shifter module coordinated with the gear positioning system was designed. Secondly, a remote controller module with information organic light-emitting diodes (OLEDs) and shift operation buttons was developed. Both modules use independent batteries and a Bluetooth Low Energy (BLE) interface to communicate with each other for wireless shifting control. The experimental results verify the effectiveness of the proposed system in terms of accuracy, rapidness, and robustness.

Most facilities are structured in a repetitive manner. In this paper, we propose an algorithm and its partial implementation for a cellular guide in such facilities without GPS use. The complete system is based on iBeacons-like components, which operate on BLE technology, and their integration into a navigation application. We assume that the user's location is determined with sufficient accuracy. Our main goal revolves around leveraging the repetitive structure of the given facility to optimize navigation in terms of storage requirements, energy efficiency in the cellular device, algorithmic complexity, and other aspects. To the best of our knowledge, there is no prior experience in addressing this specific aim. In order to provide high performance in real time, we rely on optimal saving and the use of pre-calculated and stored navigation sub-routes. Our implementation seamlessly integrates iBeacon communications, a pre-defined indoor map, diverse data structures for efficient information storage, and a user interface, all working cohesively under a single supervision. Each module can be considered, developed, and improved independently. The approach is mainly directed to places, such as passenger ships, hotels, colleges, and so on. Because of the fact that there are "replicated" parts on different floors, stored once and used for multiple routes, we reduce the amount of information that must be stored, thus helping to reduce memory usage and as a result, yielding a better running time and energy consumption. [ABSTRACT FROM AUTHOR]

A position identification system for wandering elderly people uses BLE to transmit ID information. The objective of this study is to make the BLE module batteryless using a piezoelectric element. The piezoelectric element is mounted on the sole of a shoe, and when pressure is applied to the piezoelectric element by walking, a voltage is generated between both electrodes of the piezoelectric element. This voltage is used to store the necessary power as a battery to operate the BLE module. In this paper, we provide a step-by-step design approach using piezoelectric elements attached to a shoe to power an actual BLE module. We derive an equivalent circuit for the piezoelectric element under walking conditions and, through circuit simulation and actual measurements, clarify the amount of time required to charge the voltage to drive the BLE, demonstrating the possibility of a batteryless BLE module for use in locating a wanderer while they are walking. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices. [ABSTRACT FROM AUTHOR]

Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities. Dataset: https://pubfaili.edi.lv/wearsecdataDataset License: CC-BY-SA [ABSTRACT FROM AUTHOR]

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

In outdoor positioning, the global positioning system (GPS) is currently the most commonly used method. Considering the power consumption required to use GPS, it may not be efficient for tracking in smaller areas, such as outdoor grazing areas in Japan, and using wireless sensor networks seems more feasible. There are several methods for BLE-based positioning. Because the angles of arrival (AoA) and time of flight (ToF) require additional equipment, the RSSI-based localization method is the most cost-efficient. Owing to the outdoor environment, the RSSI transmission model follows a two-ray ground-reflection model, this can lead to large errors in the trilateration positioning method. On the other hand, fingerprint positioning requires the creation and maintenance of a large database. This paper proposes an RSSI-based positioning method formulated as an optimization problem. We evaluated the performance of various algorithms in two application scenarios. Our simulation results show that a high localization accuracy can be obtained using this localization method. In contrast to other methods, this approach does not necessitate the use of supplementary equipment, as is the case with AoA and ToF, nor does it require the establishment and upkeep of an RSSI fingerprint database.

The proliferation of mobile devices has fueled the demand for indoor location-based services. Consequently, a plethora of techniques have emerged to facilitate object and device localization in indoor environments. Among these, fingerprint-based indoor localization systems, which leverage machine learning, stand out as a promising solution for providing accurate localization. Nonetheless, their performance is inherently reliant on the accuracy of the underlying database, and any changes in the indoor layout can significantly impact the wireless signals, subsequently affecting the localization accuracy. To circumvent this issue, this work proposes a novel access point (AP) selection framework to enhance the robustness of fingerprint-based indoor positioning systems in dynamic indoor environments. More specifically, a hybrid Wi-Fi and BLE fingerprint database is constructed by collecting received signal strength (RSS) from the pre-defined reference points (RPs). To ensure that the fingerprint database remains relevant over time, some RPs are designated as known points so that the system can periodically collect new RSS at the known points. Subsequently, the proposed scheme computes the differences between the RSS of the database and the updated RSS from the new layout to account for the changes occurred. The building-based, floor-based, and zone-based implementation modes determine which APs are reliable to be utilized during localization based on the RSS discrepancies observed in building, floor, and zone, respectively. Results demonstrate that the proposed building-based, floor-based, and zone-based AP selection schemes could achieve reduction in positioning error up to 28.86%, 33.53%, and 39.66%, respectively, compared to the baseline technique without AP selection schemes.

Recently, narrowband Internet-of-Things (NB-IoT) networks have been on the rise in the IoT field due to their features like low power consumption and high penetration rate. However, NB-IoT’s main drawbacks are its high delays and low data rates. To address these problems, in this paper, we present a two-tier cooperative solution to improve network throughput. Two-tier networks generally consist of cellular and device-to-device (D2D) communications. In this work, we use NB-IoT for cellular networks and Bluetooth low energy (BLE) for D2D communications. By leveraging these communications technologies, we enable idle nodes in a group to assist target nodes download and upload data. In doing this, we aim to maximize throughput, minimize consumed energy, and maximize the total remaining capacity of the node group batteries. To tackle the faced multi-objective optimization problem, we used the non-dominated sorting genetic algorithm (NSGA-II). Only one group gets selected from the candidate groups by adjusting the level of node participation. Simulation results show a 7.7-fold growth of throughput against only an 8 percent increase in energy consumption compared to the baseline download scenario and a 7.6-fold growth of throughput against just a 2 percent increase in energy consumption compared to the baseline upload scenario.

Navigation services and other traffic applications are nowadays widespread, and their use is very common. These applications have an impact on traffic flow, driver awareness, and driver safety; however, a determination of the actual position of the end device is required. Most of such applications use Global Navigation Satellite Systems (GNSS), which cover a significant part of the transport infrastructure. However, in a GNSS-denied environment, these commonly used methods are either unavailable or significantly limited.For example, in road tunnels, signals are blocked by the tunnel structure itself. Nevertheless, tunnels are part of critical infrastructure and, in addition, have higher requirements to ensure safety compared to other transport routes. This article proposes an alternative method to determine position using data from additional Bluetooth Low Energy infrastructure. Furthermore, the localization approaches are presented together with the evaluation and its accuracy and stability assessment. The localization infrastructure was tested at various speeds during tunnel closure and during the real traffic scenario. The median positioning error ranges between 15-20m and decreases with distance, providing a stable, reliable, and cost-effective solution, particularly suited for long tunnels with varying curvatures. Therefore, it offers a practical use with significant impact on traffic safety and efficiency.

One of the key issues in wearable electronics is data transmission over wireless networks. Among the most commonly used solutions are Bluetooth Low Energy (BLE), as well as the ZigBee protocol, which are characterized by low power consumption and low implementation cost. The functionality of such solutions also depends on the energy consumption of the proposed solutions, as well as their size and weight. The purpose of this paper is to analyze selected communication techniques, which were compared in terms of their range and energy efficiency. The paper presents results on wireless communication tests. A comparison is presented of Bluetooth communication range carried out under different conditions, such as in a building where there were no obstacles and in a public facility. In order to compare Bluetooth communication parameters, different phone models were used. [ABSTRACT FROM AUTHOR]