RL-IoT: Towards IoT Interoperability via Reinforcement Learning

Abstract

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT -- a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions. We assume only to know a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb for our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT opens the opportunity to use RL to automatically explore how to interact with IoT protocols with limited information, and paving the road for interoperable systems.