On the Data Freshness for IoT Traffic Modelling in Real-Time Emergency Observation Systems

Internet of things (IoT) and fog computing based observation systems are gaining more importance as Internet becomes the main infrastructure to augment the pervasiveness in remote monitoring of the physical world. Considering the explosion in the number of connected "things", the increase of data traffic density on interconnection devices (i.e., IoT gateways) becomes an important problem for scalable real-time emergency detection and monitoring. Thus, data traffic analysis and modeling of fog services become an important research area to get more insights into real-time behavior of such systems. The outcomes of such analysis are important for prediction of IoT system behavior in a given network topology. In this paper, we elaborate on an architectural solution for periodic data acquisition from a wireless sensor network(WSN). To this end, we propose a publish/subscribe (P/S) based observation scheme which simultaneously interconnects clients to different kind of sensor devices over a fog layer service. Then, we examine the data freshness which is a critical traffic modeling parameter for real-time emergency observation. With using such scheme, we devise an analysis for understanding the behavior of the overall system in the context of data freshness. The results obtained from our experimental setup illustrate the appropriateness of freshness time calculation methods for obtaining the required service quality.