Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular received signal strength (RSS) readings vary for different types of phones. In this paper, we introduce a number of techniques for addressing the phones heterogeneity problem in the deep-learning based localization systems. The basic idea is either to approximate a function that maps the cellular RSS measurements between different devices or to transfer the knowledge across them. Evaluation of the proposed techniques using different Android phones on four independent testbeds shows that our techniques can improve the localization accuracy by more than 220% for the four testbeds as compared to the state-of-the-art systems. This highlights the promise of the proposed device heterogeneity handling techniques for enabling a wide deployment of deep learning-based localization systems over different devices.

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.

Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in a real-world testbed demonstrates PassiFi's exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy.