Generalizable Deep-Learning-Based Wireless Indoor Localization

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization

A Meta-learning based Generalizable Indoor Localization Model using Channel State Information

Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately estimating the position of wireless devices in indoor environments using wireless parameters such as Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). However, despite the success of deep learning-based approaches in achieving high localization accuracy, these models suffer from a lack of generalizability and can not be readily-deployed to new environments or operate in dynamic environments without retraining. In this paper, we propose meta-learning-based localization models to address the lack of generalizability that persists in conventionally trained DL-based localization models. Furthermore, since meta-learning algorithms require diverse datasets from several different scenarios, which can be hard to collect in the context of localization, we design and propose a new meta-learning algorithm, TB-MAML (Task Biased Model Agnostic Meta Learning), intended to further improve generalizability when the dataset is limited. Lastly, we evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learning algorithms.Comment: 6 pages, 6 figures, submitted to IEEE GLOBECOM 202

Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. UltraWideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and NonLine-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environmen

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

A Machine Learning Approach to Indoor Localization Data Mining

Indoor positioning systems are increasingly commonplace in various environments and produce large quantities of data. They are used in industrial applications, robotics, asset and employee tracking just to name a few use cases. The growing amount of data and the accelerating progress of machine learning opens up many new possibilities for analyzing this data in ways that were not conceivable or relevant before. This paper introduces connected concepts and implementations to answer question how this data can be utilized. Data gathered in this thesis originates from an indoor positioning system deployed in retail environment, but the discussed methods can be applied generally. The issue will be approached by first introducing the concept of machine learning and more generally, artificial intelligence, and how they work on a general level. A deeper dive is done to subfields and algorithms that are relevant to the data mining task at hand. Indoor positioning system basics are also shortly discussed to create a base understanding on the realistic capabilities and constraints that these kinds of systems encase. These methods and previous knowledge from literature are put to test with the freshly gathered data. An algorithm based on existing example from literature was tested and improved upon with the new data. A novel method to cluster and classify movement patterns was introduced, utilizing deep learning to create embedded representations of the trajectories in a more complex learning pipeline. This type of learning is often referred to as deep clustering. The results are promising and both of the methods produce useful high level representations of the complex dataset that can help a human operator to discern the relevant patterns from raw data and to be used as an input for subsequent supervised and unsupervised learning steps. Several factors related to optimizing the learning pipeline, such as regularization were also researched and the results presented as visualizations. The research found that pipeline consisting of CNN-autoencoder followed by a classic clustering algorithm such as DBSCAN produces useful results in the form of trajectory clusters. Regularization such as L1 regression improves this performance. The research done in this paper presents useful algorithms for processing raw, noisy localization data from indoor environments that can be used for further implementations in both industrial applications and academia

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table