Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees

Abstract: With the advancement of Internet of Things (IoT) technologies, high-precision indoor positioning has become essential for Location-Based Services (LBS) in complex indoor environments. Fingerprint-based localization is popular, but traditional algorithms and deep learning-based methods face challenges such as poor generalization, overfitting, and lack of interpretability. This paper applies conformal prediction (CP) to deep learning-based indoor positioning. CP transforms the uncertainty of the model into a non-conformity score, constructs prediction sets to ensure correctness coverage, and provides statistical guarantees. We also introduce conformal risk control for path navigation tasks to manage the false discovery rate (FDR) and the false negative rate (FNR).The model achieved an accuracy of approximately 100% on the training dataset and 85% on the testing dataset, effectively demonstrating its performance and generalization capability. Furthermore, we also develop a conformal p-value framework to control the proportion of position-error points. Experiments on the UJIIndoLoc dataset using lightweight models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet show that the conformal prediction technique can effectively approximate the target coverage, and different models have different performance in terms of prediction set size and uncertainty quantification.

Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees

The paper titled "Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees" addresses the significant challenges faced by fingerprint-based indoor positioning systems, particularly in complex environments. The authors propose the application of conformal prediction (CP) techniques to enhance deep learning models for indoor localization. This approach serves to provide statistical certainty regarding model predictions, which is paramount in systems where spatial accuracy is critical.

Overview of Challenges and CP Solution

Fingerprint-based localization systems, which rely on data from various sensors like Wi-Fi, Bluetooth, and geomagnetic sensors, often encounter issues like poor generalization and overfitting when implemented through traditional algorithms and deep learning methods. To mitigate these issues, the authors employ CP—a model-agnostic, distribution-free method that allows for uncertainty quantification and prediction set construction based on non-conformity scores. CP provides correctness coverage guarantees—the likelihood that a prediction set constructed for a given test input contains the true location. This statistical rigor enhances reliability in environments with signal fluctuations or non-obvious signal patterns.

Numerical Highlights and Model Performance

Numerical results from experiments conducted on the UJIIndoLoc dataset reveal the efficacy of CP in the context of deep learning-based positioning frameworks. The model demonstrated near-perfect accuracy on the training dataset, achieving approximately 100%, and maintained substantial generalization capability with an 85% accuracy on test dataset evaluations. Models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet were tested, showing efficient approximation to target coverage despite varying prediction set sizes. The use of the conformal p-value framework further exemplifies robustness by allowing dynamic assessment of prediction reliability.

Implications and Applications

Practically, this research holds significant implications for location-based services (LBS) deployed in environments where GPS signals weaken due to obstructions like tunnels or large structures. The adoption of CP within such systems enhances their reliability, enabling more effective single-point and path navigation tasks. The conformal risk control approach introduced in this paper systematically manages false discovery and false negative rates. This advancement is crucial for navigational accuracy in complex indoor environments, ultimately supporting smart city implementations and resource-intensive real-time applications.

Future Prospects

The research opens avenues for further exploration of CP techniques in diverse indoor positioning systems, especially those incorporating multimodal data from different sensor types. Exploring efficient computational strategies to reduce overhead in real-time applications is another crucial step. Integration with emerging technologies in AI and IoT may lead to systems capable of adapting rapidly to environmental dynamics, maintaining reliable and precise positioning in ever-evolving indoor landscapes.

The paper presents a substantive contribution to the domain of indoor positioning, delivering valuable insights on leveraging statistical prediction techniques to enhance model reliability and applicability in real-world deployments.