Secure Indoor Localization on Embedded Devices with Machine Learning

Indoor localization, positioning, and navigation are an upcoming application domain for the navigation and tracking of people and assets. Ubiquitously available Wi-Fi signals have enabled low-cost fingerprinting-based localization solutions. Further, the rapid growth in mobile hardware capability now allows high-accuracy deep learning-based frameworks to be executed locally on mobile devices in an energy-efficient manner. However, existing deep learning-based indoor localization frameworks are vulnerable to access point (AP) attacks. This chapter presents an analysis into the vulnerability of a convolutional neural network (CNN)-based indoor localization solution to AP security compromises. Based on this analysis, we propose a novel methodology to maintain indoor localization accuracy, even in the presence of AP attacks. The proposed secured framework (called S-CNNLOC) is validated across a benchmark suite of paths and is found to deliver up to 10× more resiliency to malicious AP attacks, compared with its unsecured counterpart.