BLE phase-based ranging: accuracy and capability under strong Wi-Fi interference

Recently, indoor location-based services (LBS) have become attractive with the rapid development of indoor location estimation and with the spread of smartphones. An estimation technique using Bluetooth low energy (BLE) tags becomes most attractive and promising among other techniques for estimating location because a majority of smartphones supports BLE. The BLE tags transmit advertisement (ADV) packets periodically, which include a unique ID related to each tag. When a smartphone receives the ADV packet, the smartphone sends a query to the server to retrieve the locationbased information for the ID. To provide such a service continuously, operators have to monitor whether BLE tags work well and repair them in the event of a problem. These operations are difficult because they require physical labor that cannot be automated for a large number of BLE tags spread over a wide area. Therefore, we evaluated the operations of BLE tags quantitatively by the metric of operational costs. We established an environment composed of 54 BLE tags in a four-story building and measured the lifetime of the battery, the service availability ratio, and the operational costs. Through the experiments, we obtained various results regarding the battery management of the BLE tags and the effectiveness of redundant deployment.

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag's location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Nowadays, it is becoming increasingly important to understand the multiple configuration factors of BLE anchors in indoor location systems. This task becomes particularly crucial in the context of activity recognition in multi-occupancy smart environments. Knowing the impact of the configuration of BLE anchors in an indoor location system allows us to distinguish the interactions performed by each inhabitant in a smart environment according to their proximity to each sensor. This paper proposes a new methodology, OBLEA, that determines the optimisation of Bluetooth Low Energy (BLE) anchors in indoor location systems, considering multiple BLE variables to increase flexibility and facilitate transferability to other environments. Concretely, we present a model based on a data-driven approach that considers configurations to obtain the best performing configuration with a minimum number of anchors. This methodology includes a flexible framework for the indoor space, the architecture to be deployed, which considers the RSSI value of the BLE anchors, and finally, optimisation and inference for indoor location. As a case study, OBLEA is applied to determine the location of ageing inhabitants in a nursing home in Alcaudete, Jaén (Spain). Results show the extracted knowledge related to the optimisation of BLE anchors involved in the case study.

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

Indoor localization through Bluetooth Low Energy (BLE) signals has attracted a lot of interest due to its simplicity in implementation and low cost in deployment. Many techniques have been developed to predict locations through received BLE signal strength. Among them, signal fingerprinting is arguably the most accurate approach. However, this solution heavily relies on high quality fingerprint mapping constructed through costly site surveying. Moreover, such established fingerprints usually need to be updated frequently when the existing environment changes drastically. In this preliminary work, we propose an efficient fingerprinting surveying system prototype through leveraging the Ultra-Wide Band localization sensors to provide accurate location information as ground truth. Our system automatically collects and uploads locations and signal strengths with minimum human interference, and thus is able to quickly and continuously build fingerprints effortlessly. We demonstrate the accuracy of our fingerprinting approach and its benefits in enabling location estimation algorithms, which achieve high prediction accuracy through extensive experiments.

Indoor Positioning Systems (IPS) are an alternative to Global Positioning System (GPS) in those environments where its signal is attenuated. This is one of the main reasons why IPS has been the subject of much research over the last two decades and where different technologies and methods have been used. Among the technologies used in IPS are those that use radio frequency (RF) signals, such as Bluetooth Low Energy (BLE) or Wi-Fi. BLE is widely used in ubiquitous computing and in many applications of the Internet of Things (IoT) mainly due to its low power consumption and because it can provide advanced services to users. The aim of this paper is to provide an overview of the state of the art of BLE-based IPS including its main methods and algorithms.

IoT or The Internet of Things is the latest modern-day technology that is currently transforming the society in which we live in. Various opportunities in IoT have increased the demand for the need of IoT enabled technologies to a great extent. Bluetooth Low Energy (BLE) is one such technology which helped advance the scope of IoT enabled technologies in smart cities and various other corporate organizations. Asset tracking, person location tracking are some of the key use cases of the BLE technology. Hence integration of BLE technology has helped IoT to reach its technological advancement to a next level. With the advancements in technology, many researches in services like indoor localization had been carried out. Indoor localization is that ability of technology to actually find the exact location of a person or device existing in an indoor setting. So this technology feature have been accepted and put into use in sectors like industries, building management, health sectors and so on. Hence this technology is now integrated into latest smart cities so as to maximize the utilization of the technology to the fullest. Security is one major concern with the emergence of a technology. Many algorithms or protocols help to curb the security threats on a major scale. In this paper we had also discuss on some security issues faced in IoT system to know how effectively and carefully one should implement and design an IoT system to ensure that there are no security flaws. This paper focuses on applications of IoT and BLE technologies with some of its security threats like Denial of Service (DoS) attacks with a proposal for improvement in boarding passes issued to passengers in airports that enable passenger tracking possible in airports around the world. Section I consists of the introduction which describes the brief idea on the paper followed by Section II which is the literature review which consists of applications, security and other various aspects of IoT and BLE systems. Section III is a proposal for improved boarding pass system and in the end is Section IV which concludes the paper with a detailed conclusion.

This study presents a method for analyzing workers’ behavior using indoor positioning technology and field test in the workplace. Recently, various indoor positioning methods, such as Wi-Fi, Bluetooth low energy (BLE), visible light communication, Japan’s indoor messaging system, ultra-wide band (UWB), and pedestrian dead reckoning (PDR), have been investigated. The development of these technologies allows tracking of movement of both people and/or goods in indoor spaces, people and/or goods behavior analysis is expected as one of the key technologies for operation optimization. However, when we use these technologies for human tracking, there are some problem as follows. 1) Many cases need to use dedicated facilities (e.g. UWB). 2) When we use smartphone as sensing device, battery depletion is one of the big problem (especially using PDR). 3) the accuracy is instability for tracking (e.g. Wi-Fi). Based on these matters, in this study we designed and developed an indoor positioning system using BLE positioning. And, we adopted smartphone for business use as sensing device, developed a smartphone application runs on android OS. Moreover, we conducted the field test of developed system at Itoki Corporation’s ITOKI Tokyo Innovation Center, SYNQA, office (Tokyo, Japan). Over 40 workers participated in this field test, and worker tracking log data were collected for 6 weeks. We analyzed the characteristics of the workers’ behavior using this log data as a prototyping.

Recently, Wi-Fi and Bluetooth Low Energy (BLE) provide a lot of convenient to people. However, the Wi-Fi access nodes (WFAN) are fixed and cannot provide the same signal strength to every covered region. The signal strength for some regions are poor. Hence, it is difficult to solve the poor signal strengths in some regions. This paper proposes a manager device that can utilize both positions of the indoor positioning monitor device and the WFAN in our experiment environment. We use BLE beacons to achieve our indoor positioning and implement a movable device to carry the WFAN. On basis of the results, our system can judge current WFAN's position and move to appropriate positon in the indoor environment. In our experiments, the calculated position and actual position of our manager device have average differences of 0.807 m, respectively. Our manager device can order the WFAN to move to appropriate position that is based on the positions of the other environment devices and provide better wireless access quality.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

This paper presents a new asset tracking system that integrates a classical tracking solution with the Bluetooth Low Energy (BLE) technology and the opportunities offered by smartphones. The proposed platform has been explicitly designed and implemented to be employed in construction sites and was tested in a real environment. The solution aims at guaranteeing a two-fold requirement: i) a good level of precision in terms of accuracy of the position of the asset location and completeness of the acquired information (i.e., detection of all the tagged assets) and, at the same time, ii) saving smartphones' resources such as CPU, memory and, in particular, energy.

Bluetooth Low Energy (BLE) is a widely adopted wireless communication technology in the Internet of Things (IoT). BLE offers secure communication through a set of pairing strategies. However, these pairing strategies are obsolete in the context of IoT. The security of BLE based devices relies on physical security, but a BLE enabled IoT device may be deployed in a public environment without physical security. Attackers who can physically access a BLE-based device will be able to pair with it and may control it thereafter. Therefore, manufacturers may implement extra authentication mechanisms at the application layer to address this issue. In this paper, we design and implement a BLE Security Scan (BLESS) framework to identify those BLE apps that do not implement encryption or authentication at the application layer. Taint analysis is used to track if BLE apps use nonces and cryptographic keys, which are critical to cryptographic protocols. We scan 1073 BLE apps and find that 93% of them are not secure. To mitigate this problem, we propose and implement an application-level defense with a low-cost $0.55 crypto co-processor using public key cryptography.