Indoor Localization with Multi-objective Selection of Radiomap Models

Indoor localization systems (ILS) evolved over the last years, mainly due to the fact that Global Positioning Systems (GPS) lack precision or fail entirely to localize smartphone users in indoor environments. Many studies attempted to alleviate this issue by utilizing techniques developed on top of technologies, such as Bluetooth beacons and RFID that require costly installation of specialized equipment, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that utilize the received signal strength intensity (RSSI) of the already existing Wi-Fi access points (APs) of a building or cellular towers of the surrounding telecommunication infrastructure. The latter approaches, however, face privacy and/or performance issues since they gain knowledge regarding the requested user location during the indoor localization process and/or require high energy, storage and computational power resources in order to localize the user in situ the smartphone device. In this chapter, we introduce a bio-inspired modeling approach for addressing all the aforementioned concerns. In particular, an artificial neural network is used to model an area’s RSSI data at the server side, which is then forwarded to the client side to predict the user’s current location on the smartphone device. The proposed approach improves the smartphone performance during user navigation while maintaining a fine-grain localization accuracy, but also preserving the user’s privacy with respect to conventional fingerprinting approaches. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces.

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Indoor Positioning System (IPS) has always been a focused area of research due to an increasing demand for IPS in a variety of civilian and defence applications. Bluetooth low energy beacons are best suited for IPS owing to their compatibility with mobile devices such as smart phones. In this paper, a standard bluetooth module is configured to work as a Bluetooth-Beacon (BT-Beacons) and an IPS is implemented using the RSSI values of signals from the BT-Beacons. The position of user in indoors is computed using the principle of trilateration. The proposed system achieved good positioning accuracy and is economical. Further, the proposed system can be improved to assist localization and navigation in indoor environment such as huge buildings.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

To overcome the limitation of Global positioning system (GPS) in indoor environments, various indoor positioning system have been developed using Wi-Fi, Bluetooth, Ultrawideband (UWB) and radio-frequency identification (RFID). Amongst them, Wi-Fi technologies are most commonly used for indoor navigation. Wi-Fi signals may be unavailable in some areas due to obstacles and unreachable coverages. Despite of it, the accuracy achieved by Wi-Fi is between 5–15 m that is unfavorable for visually impaired people. The popularity of beacons for positioning and smartphones with built-in inertial sensors plays a vital role in developing potential indoor navigation system. This paper presents a framework for visually impaired person (VIP) based on inertial sensors of smartphones and Bluetooth beacons. Beacons/proximity sensors in a building can help a pedestrian to navigate between two landmarks/points of interest via turn-by-turn navigation. However, there are certain areas in the building where external sensing is absent in a big hallway or dark alley. This model demonstrates that inertial sensors are useful to track a VIP in dark areas. Also. minimizes the use of external sensors between two landmarks/beacons. The performance of the proposed framework with the fusion algorithm in an android application is examined by conducting trajectory test on a smartphone. The experimental results of the walking traces show that the system has high accuracy with almost 1.5-2 m mean position error which could be improved further by implementing magnetometer based position learning techniques.

Indoor positioning opens up opportunities for a wide range of applications, including active marketing, accessibility and security. Although GPS (Global Positioning System) is widely used for outdoor location, it is inaccurate and in some cases unavailable indoors. One of the solutions is to use Bluetooth Beacons to determine the distance between the device and the beacon indoors using the Received Signal Strength Indicator (RSSI). The location of the object in the environment can be determined using at least three beacons and methods such as trilateration. This work aims to evaluate the use of artificial neural networks (ANN) to determine the distance and location of the laptop in an indoor environment. A first experiment compares the Log Distance Path Loss (LDPL) model and the ANN to determine the distance between the beacon and a laptop. A second experiment compares which method is best to determine the position of the laptop in a room. The following methods were evaluated: a) trilateration with distance calculation using the LDPL method; b) trilateration with distance calculation using an ANN; and c) position determination using an ANN. The results show that RSSI values can vary due to obstacles and the position of the antenna between the beacon and the laptop.

The Global Positioning System (GPS) uses signals from satellites to specialised devices to detect an entity’s location on Earth’s surface. However, GPS is ineffective for indoor navigation due to high power consumption and signal obstruction by walls. This study proposes a low-cost, energy-efficient alternative using Bluetooth 4.0-enabled Bluetooth Low Energy (BLE) beacons to enable Indoor Positioning and guide users in confined spaces, such as libraries. Tiny, low-energy BLE beacons transmit Advertising Packets at regular intervals. In contrast, Anchor Nodes send and receive signals to construct a Received Signal Strength Indication (RSSI)-based signal-to-distance model, enabling triangulation-based localisation. The system monitors behaviour and environmental interaction through RSSI analysis and user interaction with Beacons. Proven in a library setting, the BLE-based system proved extremely accurate in guiding users to destinations, bridging GPS shortcomings at minimal energy expenditure and cost.

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.

As technology gets cheaper and wireless networks grow, more efforts have been put into developing smart buildings and homes. A key characteristic of these smart buildings is to provide indoor navigation and localization services to its occupants. As this demand increases, simple, scalable solutions are sought after. Current localization technologies, such as GPS, do not work in indoor environments, nor does it provide the level of accuracy required for an effective indoor navigation solution. This paper explores the feasibility of a wireless indoor location system based on Bluetooth Low Energy beacons (BLE). Five popular BLE beacon devices are compared in terms of energy consumption and proximity accuracy. Experiments are conducted in three different rooms and three filtering techniques are also examined, to improve the performance of each beacon. According to experimental results, a proper selection of the filtering technique can help to improve their proximity estimation.

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

Indoor Positioning System helps to locate, monitor and track the devices using the radio signals. This can be used to find the people who are trapped inside a building. Outdoor localization problems can be solved by using Global Positioning System (GPS). Since GPS signals are lost inside the buildings, Indoor Positioning still remains a problem. This paper provides a technique for indoor positioning by using WiFi fingerprint. The intensity of the received signal is measured and is stored in a database. The database consists of the list of signal strength and their corresponding locations. The similarity of the signal detected and those stored in the database can used to detect the position of the user. The prevailing WiFi infrastructure can be used for this purpose. We use deep neural network with stacked autoencoders to determine the floor and building. The classifier is used to detect the building and the floor level .

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.