Simple and Effective Augmentation Methods for CSI Based Indoor Localization

Indoor localization is a challenging task. There is no robust and almost-universal approach, in contrast to outdoor environments where GPS is dominant. Recently, machine learning (ML) has emerged as the most promising approach for achieving accurate indoor localization, yet its main challenge is the requirement for large datasets to train the neural networks. The data collection procedure is costly and laborious as the procedure requires extensive measurements and labeling processes for different indoor environments. The situation can be improved by Data Augmentation (DA), which is a general framework to enlarge the datasets for ML, making ML systems more robust and increases their generalization capabilities. In this paper, we propose two simple yet surprisingly effective DA algorithms for channel state information (CSI) based indoor localization motivated by physical considerations. We show that the required number of measurements for a given accuracy requirement may be decreased by an order of magnitude. Specifically, we demonstrate the algorithms' effectiveness by experiments conducted with a measured indoor WiFi measurement dataset: as little as 10% of the original dataset size is enough to get the same performance of the original dataset. We also showed that, if we further augment the dataset with proposed techniques we get better test accuracy more than three-fold

DeepPos: Deep Supervised Autoencoder Network for CSI Based Indoor Localization

The widespread mobile devices facilitated the emergence of many new applications and services. Among them are location-based services (LBS) that provide services based on user's location. Several techniques have been presented to enable LBS even in indoor environments where Global Positioning System (GPS) has low localization accuracy. These methods use some environment measurements (like Channel State Information (CSI) or Received Signal Strength (RSS)) for user localization. In this paper, we will use CSI and a novel deep learning algorithm to design a robust and efficient system for indoor localization. More precisely, we use supervised autoencoder (SAE) to model the environment using the data collected during the training phase. Then, during the testing phase, we use the trained model and estimate the coordinates of the unknown point by checking different possible labels. Unlike the previous fingerprinting approaches, in this work, we do not store the {CSI/RSS} of fingerprints and instead we model the environment only with a single SAE. The performance of the proposed scheme is then evaluated in two indoor environments and compared with that of similar approaches.Comment: 10 pages, 15 Figure

Wi-Fi Sensing for Indoor Localization via Channel State Information: A Survey

Wireless Fidelity (Wi-Fi) sensing utilization has been widespread, especially for human behavior/activity recognition. It provides high flexibility since it does not require the person/object to carry any device known as device-free. This "passive" concept is also helpful for another application of Wi-Fi sensing, i.e., indoor localization. The "sensing" is conducted using particular parameters extracted from communication links of Wi-Fi devices, i.e., channel state information (CSI). This paper explores the recent trends in CSI-based indoor localization with Wi-Fi technology as its core, including their advantages, challenges, and future directions. We found tremendous benefits can be gained by employing Wi-Fi sensing in localization supported by its performance and integrability for other intelligent systems for activity recognition

Indoor positioning with deep learning for mobile IoT systems

2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas