Mid Sweden University

The Internet of Things (IoT) enables users to gather and analyze data from a large number of devices. Knowledge obtained by these systems is valuable in order to understand, control, and enhance the monitored process. The mass of information to process leads however to new challenges related to required resources for both data processing and data transportation. Two critical metrics are latency and consumed energy to complete a given task. Both metrics might be exceed if all processing is done locally at the sensor device level. Cloud and Fog computing concepts can help to mitigate this effect. However, using such offloading concepts add complexity and overhead to the system. In this paper, we study the latency for processing and communication tasks in a distributed IoT systems with respect to cloud or fog offloading and derive characteristic cost functions for the studied tasks. Our results give valuable insights into the tradeoffs and constraint within our example scenario. The developed characterization methodology can however be applied to any kind of IoT system and thus allowing more general analysis.