Applying Context State Machines to Smart Elevators: Design, Implementation and Evaluation

Current context-aware computing still lacks good representation models, particularly in modeling for proactive decision making in the era of Ubiquitous Computing and the Internet of Things (IoT), To mitigate this issue, we proposed a state-based model of context in our prior research. Based on the model, we introduced Context State Machines (CSM) for simulating state changes of context attribute, situation, and context, which imply contextual behaviors that are often overlooked while very important to context reasoning and system decisions. This research applies CSMs to a real-world context-aware problem from the literature: a smart elevator control system. We introduce the design and implementation of a CSM engine, evaluate the CSM engine by utilizing two context-aware scenarios of the smart elevator, and show that the model can help automatically capture and reason the context without the inference from the developers. To the best of our knowledge, it is the first time in the literature about applying the state-based context modeling approach and the CSM engine to a real-world context-aware application.