Mobile cloud computing (MCC) as an emerging computing paradigm enables mobile devices to offload their computation tasks to nearby resource-rich cloudlets so as to augment computation capability and reduce energy consumption of mobile devices. However, due to the mobility of mobile devices and the admission of cloudlets, the connection between mobile devices and cloudlets may be unstable, which will affect offloading decision, even cause offloading failure. To address such an issue, in this paper, we propose a robust computation offloading strategy with failure recovery (RoFFR) in an intermittently connected cloudlet system aiming to reduce energy consumption and shorten application completion time. We first provide an optimal cloudlet selection policy when multiple cloudlets are available near mobile devices. Furthermore, we formulate the RoFFR problem as two optimization problems, i.e., local execution cost minimization problem and offloading execution cost minimization problem while satisfying the task-dependency requirement and application completion deadline constraint. By solving both optimization problems, we present a distributed RoFFR algorithm for CPU clock frequency configuration in local execution and transmission power allocation and data rate control in cloudlet execution. Experimental results in a real testbed show that our distributed RoFFR algorithm outperforms several baseline policies and existing offloading schemes in terms of application completion cost and offloading data rate.

Most indoor navigation technologies need to provide structure maps of buildings, but it is difficult to obtain these structure maps in practice. Therefore, we design a geomagnetism-based indoor navigation system without structure map of buildings by applying offloading strategy in narrow band Internet of Things. Why the geomagnetic signal can be used to achieve indoor navigation is due to its stability. We divide the indoor navigation into two parts: 1) the construction of signal fingerprint database and 2) the user navigation. First, the navigator carries out the signal acquisition of navigation route, and transfers the original signals to cloud server so as to construct the fingerprint database. Second, users can choose online navigation or offline navigation according to the actual situation. When choosing offline navigation, the users will employ dynamic time warping algorithm for fingerprint matching. When users would like to obtain better navigation results, they can choose online navigation. In this case, navigation system will offload the users' sampling data to remote cloud, and then the cloud matches the fingerprint of geomagnetic signals by using particle filter algorithm and sends the computation results to the users so as to indicate the users' moving direction. Experiment results demonstrate that our method is effective and the turning error is only 5 cm.