Performance- and Energy-Aware Gait-Based User Authentication With Intermittent Computation for IoT Devices

Internet of Things (IoT) makes it possible to utilize a multitude of small- and medium-scale devices allowing for increased functionality in flexible networks. However, user authentication on IoT devices is as important as it can be challenging. Due to limitations on the available energy, interface, and processing power, IoT devices can be the weakest link in their networks. Multiple authentication techniques have been developed to address these challenges. However, the existing techniques are limited in terms of performance, overheads, and efficiency. In contrast, our proposed authentication method uses a user's walking gait as an input, because gait is unique to every user and can be collected using low-power inertial sensors found on all handheld devices. Our authentication method uses a lightweight neural network (NN) which is further complimented with early exits to further optimize computational cost(s). We also propose reinforcement learning that considers the energy consumption to dynamically determine which of the exits should be chosen to strike a balance between performance and computational and energy cost. Though effective, one of the challenges with IoT devices is their power supply such as dependence on batteries. Discharge of the battery or any other interruptions can lead to recomputations, which are expensive on already limited battery-operated IoT devices. To address such challenges, especially for user authorization, we introduce intermittent computation to our proposed authentication framework. Intermittent computation can store the state of the NNs at checkpoints. In the case of power disruption, the execution will be resumed from the saved checkpoints instead of performing the whole execution. Most implementations of intermittent computation take place at the compiler level, which makes for a very efficient design, however, that also makes them hardware specific. Our method is implemented on software and is hardware-agnostic, allowing us to create checkpoints to save and retrieve the authentication framework state, in case of power interruption. The proposed authentication framework can authenticate users with up to more than 85% accuracy and can save up to 34% of computations due to the proposed intermittent computing.