IoT (Internet of Things) is increasingly becoming more popular mainly due to the fact that almost all the smart devices nowadays are network enabled to facilitate many current and emerging applications. However, some important issues still need to be addressed before fully realizing the potential of IoT applications. One of the most important issues is to have effective approaches to planning various device actions to satisfy user requirements efficiently and securely in mobile IoT applications. A mobile IoT application can be composed of mobile cloud systems and devices, such as wearable devices, smart phones and smart cars. In this type of systems, mobile networks with elastic resources from various mobile clouds are effective to support IoT applications. In this paper an effective approach to intelligent planning for mobile IoT applications is presented. This approach includes a learning technique for dynamically assessing the users' mobile IoT application and a MDP (Markov Decision Process) planning technique for enhancing efficiency of IoT device action planning. Simulation results are presented to show the effectiveness of our approach.