Comparative analysis of the Bluetooth Low-Energy indoor positioning systems

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.

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.

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.

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.

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.

Indoor positioning systems (IPS), one of the emerging technologies, play an important role in the Internet of Things (IoT) that addresses the challenges of GPS in indoor environments. This technology creates a great impact on real world application. Bluetooth Low Energy (BLE) has emerged as one of the most cost-effective, energy-efficient mechanisms for facilitating indoor localization. A problem with many available BLE fingerprinting techniques is that they do not consider differences in signal characteristics due to the orientation of the device, and limited statistical evidence exists to demonstrate variations in performance. The present study developed a machine learning-based indoor localization framework using BLE 5.1 Received Signal Strength (RSS) and Angle-of-Arrival (AoA) data to evaluate the effect of a device’s orientation on decimeter-level localization accuracy. Real-world static datasets were assembled under four orientation conditions: North, South, East, and West. In addition, temporal windowing and median-based fingerprint generation techniques were used to alleviate the impact of signal instability on accuracy. Random Forest (RF), k-Nearest Neighbour (KNN), and Multilayer Perceptron (MLP) were used to generate estimates of 2D positions based on the generated fingerprint. Performance of models was evaluated using distance-based error metrics, percentiles, reliability analysis, and the Wilcoxon signed-rank test for statistical assessment. The results obtained indicate that device orientation is a significant factor affecting localization accuracy, and Random Forest (RF) produces more robust localization accuracy than KNN and MLP across each of the four cardinal orientations. The results also illustrated the need to use orientation-aware modeling techniques to create a reliable BLE-based IPS.

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) is a promising candidate technology for use in the Internet of Things (IoT) because of its ultra-low-power communication. Although BLE devices are designed to run on a small battery for a few years, several attempts have been made to extend BLE lifetime through various techniques. In particular, emerging approaches such as artificial intelligence (AI) can be utilized to further improve the BLE energy efficiency. For this purpose, this article proposes a Q-learning-based scheduling algorithm for BLE. The proposed scheduling algorithm dynamically adjusts the key parameters that govern the operation of the BLE transmission scheme. These key parameters, namely, the length of connection interval and the number of packets to transmit during the interval, have a profound effect on energy efficiency and the quality of service (QoS) specified in terms of maximum latency. According to the framework of reinforcement learning, our Q-learning-based scheduling algorithm is appropriately constructed to simultaneously provide a longer network lifetime and satisfy the QoS requirement. The numerical results show that the proposed Q-learning-based approach significantly increases the network lifetime compared to alternative methods while meeting QoS requirements.

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.

Nowadays, the Internet of Things (IoT) is spreading rapidly towards creating smart environments.Home automation, intra-vehicular interaction, and wireless sensor networks (WSN) are among the most popular applications discussed in IoT literature.One of the most available and popular wireless technologies for short-range operations is Bluetooth.In late 2010, the Bluetooth Special Interest Group (SIG) launched the Bluetooth 4.0 Specification, which brings Bluetooth Low Energy (BLE) as part of the specification.BLE characterises as being a very low power wireless technology, capable of working on a coin-cell or even by energy scavenging.Nevertheless, the nature of Bluetooth (and BLE) has always been a connection-oriented communication in a Master/Slave configuration.Several studies exist showing how to create mesh networks for Classic Bluetooth, called Scatternets, by utilizing some nodes as slaves to relay data between Masters.However, BLE didn't support role changing until the 4.1 Specification released in 2013.The capability of a wireless technology to create a Mobile Ad-Hoc Network (MANET) is vital for supporting the plethora of sensors, peripherals, and devices that could coexist in any IoT environment.This work focuses on proposing a new autoconfiguring dynamic address allocation scheme for a BLE Ad-Hoc network, and a network map discovery and maintenance mechanism that doesn't require role changing, thus being possible to implement it in 4.0 compliant devices as well as 4.1 or later to develop a MANET.Any ad-hoc routing protocol can utilise the proposed method to discover, keep track, and maintain the mesh network node topology in each of their nodes.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.

Beacons turn the physical world into a new digital channel. They bring digital content and engagement closer to the point of purchase. Beacon technology transmits small packets of data using Bluetooth Low Energy. When a customer’s phone “hears” the beacon it knows how close it is and we can then send a message, a notice, a piece of media or other information. Beacons are low-power transmitters equipped with Bluetooth Low Energy or BLE. Bluetooth Low Energy help mobile devices to detect Proximity and determine micro-location. Advantages of Bluetooth low energy devices are it works up to 100 meters, doesn’t drain the phone battery.In this paper problems with beacon are described and path navigation using Bluetooth Low Keywords: Beacon , Internet of Things – Beacons The New Revolution, Bluetooth Low Energy, Path Navigation, Background Monitoring, Ranging Beacons, Proximity, Tracking

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.