Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

A Survey of Indoor Localization Systems and Technologies

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

Currently, Bluetooth low energy (BLE) beacons are widely used for positioning, especially an indoor environment. However, there is still high error due to dense multipath fading that often occurs in the indoor environment. This paper presents the accuracy study of indoor positioning with BLE beacons. The indoor environment is the room of 91.8 m2 with 3 BLE beacons. The user equipment (UE), iPhone XS Max, with Beacon Analyzer application is used to measure the received signal strength (RSS) of each BLE beacon. Fingerprinting technique with least root mean square (RMS) error matching is used to estimate the position of UE. The accuracy that obtained from single measurement and average five measurements is studied. The RSS fingerprint of each BLE beacon is shown. The cumulative distribution function (CDF) of distance error is evaluated and illustrated. From the results, the average five measurements can reduce the average distance error about 0.86 m.

Accuracy Study of Indoor Positioning with Bluetooth Low Energy Beacons

Nowadays, indoor positioning services of mobile phone with Bluetooth low energy (BLE) beacons are increasingly used. However, due to the indoor environment has dense multipath fading, the positioning system has low accuracy. In this paper, the accuracy study of trilateration method for indoor positioning with BLE beacons is presented. The indoor environment room with 9.0 m width, 10.2 m length and 3 BLE beacons is studied. The received signal strength (RSS) of each BLE beacon is measured by using mobile phone. The distance between each BLE beacon and mobile phone is evaluated using channel model. After that, the trilateration method is used to approximate the position of mobile phone. The accuracies, which are obtained from one, average five and average ten measurements, are studied. The distance errors of all positions and the cumulative distribution function (CDF) of the distance error are calculated and shown. The results show that the average multiple measurements tend to decrease the distance error.

Study on Accuracy of Trilateration Method for Indoor Positioning with BLE Beacons

The location information of sensors and devices plays an important role in Internet of Things (IoT). Low cost and energy efficient Bluetooth Low Energy (BLE)-based localization solutions are ideal for extensive use in IoT applications. The main challenge is to combat the signal fluctuation in non-line-of-sight (NLOS) propagation. Most Machine Learning (ML) algorithms available in the literature to address this issue belong to classification and batch learning. This article proposes a novel online learning algorithm for BLE localization based on Gaussian–Bernoulli Restricted Boltzmann Machine (GBRBM) plus Liquid State Machine (LSM), which learns the training data one-by-one. Unsupervised GBRBM is able to extract sound patterns of fluctuated RSS inputs, and LSM manages to map the patterns to real-time position estimation. Extensive experimental results demonstrate the superiority of the proposed method over other state-of-the-art batch learning methods in terms of localization accuracy and complexity.

Bluetooth Indoor Localization With Gaussian–Bernoulli Restricted Boltzmann Machine Plus Liquid State Machine

Indoor localization represents a hot topic nowadays due to the new emerging location-based services, which are expected to have a major impact, especially on retail sector. There are several technologies that can be used for indoor localization, such as WiFi, RFID, and Beacons. Among them, the technology based on Bluetooth Low Energy beacons, seems to be the most promising, due to the higher accuracy and low energy consumption. In this paper, some preliminary results on indoor localization based on radio beacons are presented. A localization solution based on a signal map that was built in advance is analyzed and evaluated. Conclusions about the indoor localization accuracy are presented in the end.

Indoor localization using radio beacon technology

This paper is intended to present the vulnerabilities of the sign-on methods especially those caused by users when trying to access various network services using a single account and the same password for all of them. The sign-on process is more secure by the mean of two-factor authentication and different security standards but still there are some weaknesses that need to be eliminated so different solutions, new or already used, are discussed in order to increase the security level of the access to the network.

Florin Daniel Mocanu

Papers

Indoor localization using radio beacon technology

An Overview of Sign-On Risks and Solutions