Automatic Identification of Wireless Sensor Network Topology in a IoT Domestic Setup and Discovery of User Routines

In recent years, Internet of Things has been gaining popularity due to its capabilities and flexible implementation. Current developments make use of several sensor types building large wireless sensor networks, where each sensor can have a degree of connection over the others. It is usually more perceptible with the use of motion sensors in different rooms where physical paths taken by a subject are strongly correlated to temporal sequences detected in the nodes. This study presents two methods for the detection of these correlations between nodes, one requiring the user to perform a path across every sensor and another method that tries to infer information without any explicit human intervention, by analysing the first events of each day where entropy is low. The results show that the latter method, which does not require explicit human intervention, presents some degradation if a low number of sensors is used in the network and these sensors have a high periodic activation. The former method is in general more accurate for small to medium sized networks, but can be problematic in large networks where passing across every sensor can be a tedious or unpractical requirement.