Mobile Internet of Things (IoT) applications are typically deployed on resource-constrained devices with intermittent network connectivity. To support the deployment of such applications, the Publish/Subscribe (pub/sub) interaction paradigm is often employed, as it decouples mobile peers in time and space. Furthermore, pub/sub middleware protocols and APIs consider the Things' hardware limitations and support the development of effective applications by providing Quality of Service (QoS) features. These features aim to enable developers to tune an application by switching different levels of response times and delivery success rates. However, the profusion of pub/sub middleware protocols coupled with intermittent network connectivity result in non-trivial application tuning. In this paper, we model the performance of middleware protocols found in IoT, which are classified within the pub/sub interaction paradigm-both reliable and unreliable underlying network layers are considered. We model reliable and unreliable protocols, by considering QoS semantics for data validity, buffer capacities as well as the intermittent availability of peers. Finally, we perform statistical analysis by varying these QoS semantics, demonstrating their significant effect on the rate of successful interactions. We showcase the application of our analysis in concrete scenarios relating to Traffic Information Management systems, that integrate both reliable and unreliable participants. The consequent PerfMP performance modeling pattern may be tailored for a variety of deployments, in order to control fine-grained QoS policies.