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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.

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.

Future telecommunications aim to be ubiquitous and efficient, as widely deployed connectivity will allow for a variety of edge/fog based services. Challenges are numerous, e.g., spectrum overuse, energy efficiency, latency and bandwidth, battery life and computing power of edge devices. Addressing these challenges is key to compose the backbone for the future Internet-of-Things (IoT). Among IoT applications are Indoor Positioning System and indoor Real-Time-Location-Systems systems, which are needed where GPS is unviable. The Bluetooth Low Energy (BLE) 5.1 specification introduced Direction Finding to the protocol, allowing for BLE devices with antenna arrays to derive the Angle-of-Arrival (AoA) of transmissions. Well known algorithms for AoA calculation are computationally demanding, so recent works have addressed this, since the low-cost of BLE devices may provide efficient solutions for indoor localization. In this paper, we present a system topology and algorithms for self-localization where a receiver with an antenna array utilizes the AoAs from fixed battery powered beacons to self-localize, without a centralized system or wall-power infrastructure. We conduct two main experiments using a BLE receiver of our own design. Firstly, we validate the expected behaviour in an anechoic chamber, computing the AoA with an RMSE of $\mathrm {10.7~ ^{circ}}$ . Secondly, we conduct a test in an outdoor area of 12 by 12 meters using four beacons, and present pre-processing steps prior to computing the AoAs, followed by position estimations achieving a mean absolute error of $\mathrm {3.6~ \text {m}}$ for 21 map positions, with a minimum as low as $\mathrm {1.1~ \text {m}}$ .

Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decision-making that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wireless-based human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

Estimating the distance between two smartphones plays an important role in a host of applications. For this purpose, smartphones emit and scan for Bluetooth Low Energy (BLE) signals. When a device is detected, the distance is estimated by evaluating the received strength of these signals. The main insight that is exploited for distance estimation is that the attenuation of a signal increases with the distance along which it has traveled. However, besides distance, there are multiple additional factors that impact the attenuation and hence disturb the distance estimation procedure. Among them, frequency-selective hardware and signal propagation belong to the most significant ones. For example, a BLE device transmits packets on three different frequencies (channels), while the transmit power and the receiver sensitivity depend on the frequency. As a result, the received signal strength varies for each channel, even when the distance remains constant. However, the information on which wireless channel a packet has been received is not made available to a smartphone. Hence, this error cannot be compensated, e.g. by calibration. In this paper, we for the first time provide a solution to detect the wireless channel on which a packet has been received by a smartphone application. We experimentally evaluate our proposed technique on multiple different smartphone models. Our results help to make distance estimation on smartphones more robust and accurate.

A wireless self-powered temperature sensor node is presented. This sensor node is powered by a commercially available indoor photovoltaic energy harvester module. The sensor node uses Bluetooth® Low Energy as wireless connection. Both the hardware and firmware have been optimized to obtain a very low power consumption. An auto adaptive energy management routine has been implemented to extend the autonomy of the sensor node. The system consumption and photovoltaic generation have been characterized at different conditions, obtaining a tool to estimate the autonomy of the system. The system has also been tested in a climatic chamber to study the effect of the temperature, a key factor that affects the power consumption, and hence the autonomy of the sensor.

The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.

Due to the popularity of short-range communications and the ultra-dense deployment of the Internet of Things (IoT), the occurrence of severe signal collision in crowded nodes is becoming increasingly critical. We propose a scheme called anti-collision voting based on Bluetooth low energy improvement for the ultra-dense edge, in which one topic can be identified and separated from the others to enable intensive voting. The scheme alleviates the exponential growth of advertising collisions and achieves efficient consensus between the devices and humans by localized the message advertising. The previous proposals focused on enhancing the hardware and optimizing the parameters to establish a connection for interaction. Considering the limitation of connective devices, our scheme adopts a novel advertising model in which the nodes can promptly exit the advertising state by actively responding. In addition, the scheme merges active responses into a group to notify the advertising nodes and randomly chooses a channel to reduce redundancy and save energy. The simulations demonstrate that the scheme can reduce the collisions by more than 90% and channel occupancy by half, while doubling the capacity and efficiently supporting intensive voting within edge networks.

The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.

Bluetooth has solidified its status as one of the most prominent and versatile wireless communication technologies alongside Wi-Fi. In spite of its widespread usage, contemporary Bluetooth testing technology still has many unaddressed shortcomings and issues. Hardware limitations, computational complexity, and the lack of standardization are some of these longstanding problems. Furthermore, very few systems possess a proven and reliable capability to test both Wi-Fi and Bluetooth. This paper presents the development of an adaptive Bluetooth test-bed framework that can not only overcome limitations with existing systems but can accommodate other technologies and protocols as well. The work presented in this paper includes: a packet processing and logging utility, a promiscuous packet capture subsystem built around a production test engine, and a complementary subsystem that handles encryption and other security mechanisms.

The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the simulation stage and then deploy a system that can provide fast responses and decentralized services.

Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial efforts, either in determining the exact location of all beacons to facilitate lateration, or collecting signal strength data from a grid over all locations to facilitate fingerprinting. To reduce this initial setup cost, one may infer the positions using Simultaneous Location and Mapping. In this paper, we use a mobile phone equipped with an Inertial Measurement Unit, a Bluetooth receiver, and an Unscented Kalman Filter to infer beacon positions. Further, we apply adaptive noise modeling in the filter based on the estimated distance of the beacons, in contrast to using a fixed noise estimate which is the common approach. This gives us more granular control of how much impact each signal strength reading has on the position estimates. The adaptive model decreases the beacon positioning errors by 27% and the user positioning errors by 21%. The positioning accuracy is 0.3 m better compared to using known beacon positions with fixed noise, while the effort to setup and maintain the position of each beacon is also substantially reduced. Therefore, adaptive noise modeling of Received Signal Strength is a significant improvement over static noise modeling for indoor positioning.

Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.

Nowadays, research and development of various indoor positioning systems (IPS) have been increasing owing to flourishing social and commercial interest in location-based services (LBSs). Among LBS technologies, we used the Bluetooth low energy beacon in our system, which consumes less energy and is embedded in many current smartphones and tablets. In particular, the fingerprinting method has become a prime choice in the design of IPS owing to its good location estimation and the fact that a line-of-sight from access points is not required. We propose an improved two-step fingerprinting localization using multiple fingerprint features to enhance the localization accuracy. The proposed system uses a propagation model to convert RSS of beacons to distance and estimate the weighted centroid (WC) of nearby beacons. The estimated WCs along with signal strength and rank of the nearby beacons are stored in the server database for localization instead of RSS from all the deployed beacons. First, the proposed system makes use of diverse fingerprinting features to increase localization accuracy that also reduces both the physical size of the database and the amount of data communication with the server in the execution phase; second, affinity propagation clustering minimizes the searching space of RPs and reduces the computational cost; third, exponential averaging is introduced to smooth the noisy RSS. The experimental results obtained by real field deployment show that the proposed method significantly improves the performance of the positioning system in both the positioning accuracy and radio-map database size.

Bluetooth low energy (BLE) is one of most promising technologies to enable the Internet-ofThings (IoT) paradigm. The BLE neighbor discovery process (NDP) based on active scanning may be the core of multiple IoT applications in which a large and varying number of users/devices/tags must be detected in a short period of time. Minimizing the discovery latency and maximizing the number of devices that can be discovered in a limited time are challenging issues due to collisions between frames sent by advertisers and scanners. The mechanism for resolution of collisions between scanners has a great impact on the achieved performance, but backoff in NDP has been poorly studied so far. This paper includes a detailed analysis of backoff in NDP, identifies and studies the factors involved in the process, reveals the limitations and problems presented by the algorithm suggested by the specifications and proposes simple and practical adaptations on scanner functionality. They are easily compatible with the current definitions of the standard, which together with a new proposal for the backoff scheme, may significantly improve the discovery latencies and, thus, the probability of discovering a large number of devices in high density scenarios.

This paper proposes a new cluster-based on-demand routing protocol to support multihop communication in Bluetooth low energy ad hoc networks. The proposed scheme includes the topology configuration procedure, topology recovery scheme, and on-demand routing protocol. The topology configuration procedure consists of node discovery, piconet configuration, and scatternet formation in a randomly distributed environment. The proposed on-demand routing protocol is designed to minimize the number of route request messages by forwarding them to a master and relay nodes in each cluster during the route request procedure. The performance evaluation shows that our proposed scheme substantially reduces energy consumption, which is the most critical issue on energy constrained networks.