Edge Reasoning on Context-Aware Environments: Application for the indoor displacement problem

Nowadays, populations in buildings are heterogeneous. They cohabit with robots and machines that move inside buildings. The capacity of each individual to process and perceive its environment is as miscellaneous as the populations themselves. These individuals deal with simple and quotidian problems of indoor displacement, which reduce their productivity, comfort and even the performance of buildings. The need of a context-aware environment that provides quick, safe and direct solutions to this issue is evident. We present in this paper a Context-Aware Edge Layer named CAEL, made with Internet Of Things-based cyber-physical system and ontologies. CAEL takes reasoning skills on a context-aware system close to the source of data to create interoperability. This latter intends to improve mobility in indoor environments. Our approach addresses the displacement problem from two different perspectives: the occupants and the building. CAEL improves occupants' perception and processing by enriching their knowledge and reducing processing load respectively, while the building re-configures its topology to improve mobility. To validate our prototype, we constructed a physical scale model of a building. Inside, we placed small autonomous mobile robots to represent populations. We evaluate CAEL performance to improve indoor mobility by running multiple use cases. Results have shown evidence of cooperation between the environment and its occupants in terms of mobility. This study paves the work for a complete reasoning distributed architecture.