Abstract

As we are within the era of the internet of things (IoT) its increasing integration to our everyday lives means that the devices involved produce massive amounts of data every second from billions of devices. The current approach used to handle this data is cloud computing. However because of its requirement of data centres this can become infeasible for the processing of data from IoT due to distance between these IoT smart objects (e.g., sensors) and the data centre. If this data holds any importance to minimal delay then the travel time between the end device and the clouds data centre could affect the relevance of that data. Therefore, to deal with these issues a new network paradigm placed closer to the IoT end devices is introduced called "Fog computing" to help address these challenges. If introduced effectively then fog computing can lead to the improvements in the quality of service (QoS) offered to systems that require the processing of delay sensitive data like healthcare systems that could benefit from the quick processing of data from sensors to allow the monitoring of patients. This paper has a main focus on healthcare systems. An architecture containing three layers; things (i.e., sensors), fog nodes and a cloud data centre is proposed alongside a framework incorporating this architecture. This framework offers collaboration among fog nodes with optimal management of resources and job allocation, which is able to achieve a high QoS (i.e., low latency) within the scenario of a healthcare system.