DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization (2407.16927v1)

Abstract: 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.

• The paper introduces a novel provider-side fingerprinting approach using unlabeled cellular data and deep learning to achieve a 29-meter median localization accuracy.
• It systematically addresses challenges like data sparsity and noise by employing spatial augmentation and a virtual grid for enhanced scalability and precision.
• The framework operates with zero extra energy consumption, extending accurate localization capabilities to low-end devices and outpacing traditional client-based methods.

DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization

The paper introduces DeepCell, a novel provider-side localization system that enhances the accuracy of cellular-based outdoor positioning. Traditional GPS systems, while broadly utilized, are often limited by energy consumption, line-of-sight requirements, and availability in low-end devices. Existing fingerprinting alternatives have been largely client-based, restricting their widespread implementation and precision. DeepCell addresses these limitations by employing provider-side cellular data to construct a high-accuracy, ubiquitous localization framework applicable to all cell phones, including low-end models.

Key Contributions and Methodology

DeepCell innovatively constructs its fingerprinting model using unlabeled cellular measurements from providers, synchronizing these with labeled position data from selective client devices. It subsequently capitalizes on deep neural networks to train the localization model. This approach circumvents the limitations of client-side data collection, enabling broader scalability and finer accuracy without incurring overhead.

Several challenges are systematically addressed within the DeepCell framework:

• Utilizing Unlabeled Data: By leveraging provider-maintained data, DeepCell avoids the constraints of client-side data collection.
• Noise and Sparsity Management: The system implements spatial augmentation techniques to create a comprehensive dataset despite the initial sparsity and noise in the provider data.
• Scalability: Through a virtual grid approach, DeepCell ensures the model's applicability across expansive areas, optimizing data collection and processing.
• Energy Efficiency: The architecture achieves zero additional energy consumption, operating effectively alongside standard phone functions.

Evaluation and Results

The system demonstrates a median localization accuracy of 29 meters, surpassing state-of-the-art client-based systems by a significant margin of 75.4%. This performance is consistent across varied environments and device categories, notably extending accurate localization capabilities to low-end phones. Such results exemplify the robustness of DeepCell’s deep learning model and the efficacy of its provider-centric data utilization.

Implications and Future Directions

The implications of DeepCell are substantial for both the practical deployment and theoretical advancements in localization technology. By shifting the fingerprinting responsibility to the provider, the system lays the groundwork for more sustainable, scalable, and energy-efficient positioning solutions.

Potential future developments might focus on refining the spatial augmentation techniques to further enhance data quality or exploring alternative deep learning architectures that could offer improved performance with less computational demand. Additionally, as cellular technologies evolve (e.g., 5G and beyond), integrating these advancements into DeepCell's framework could potentially yield even finer localization granularity and broader applicability across various network conditions.

In summary, DeepCell represents a significant step forward in cellular-based localization system design, emphasizing the provider-side approach's advantages in achieving superior accuracy and efficiency. Its methodologies and outcomes provide a valuable reference point for ongoing research and practical advancements in the field of mobile computing and networking.