For the fact that existing map updating methods are not good at map updating in indoor map environments, a hierarchical indoor map updating method was proposed. Firstly, the activity of indoor objects was taken as a parameter. Then, the division of hierarchy was performed to reduce the amount of updated data. Finally, a Convolutional Neural Network (CNN) was used to determine the attribution level of indoor data in network. The experimental results show that compared with the version update method, the update time of the proposed method is reduced by 27 percentage points, and the update time is gradually reduced compared with the incremental update method after the update item number is greater than 100. Compared with the incremental update method, the update package size of the proposed method is reduced by 6.2 percentage points, and its update package is always smaller than that of the version update method before the data item number is less than 200. Therefore, the proposed method can significantly improve the updating efficiency of indoor maps.

Delay Tolerant Network (DTN) has characteristics of dynamic topology changes and limited node storage space. A DTN Epidemic Routing with Congestion Control strategy (ERC²) method was proposed. The method was based on a Dynamic Storage State Model (DSSM). According to sensing network conditions, the threshold of node's semi-congested state was dynamically adjusted to reduce the possibility of network congestion by nodes. The ACK index and message management queue were added to make node storage state change randomly with network load, dynamically update and actively delete redundant packages. Single or mixed mode was selected for message forwarding according to different congestion states combining with advantages of Epidemic and Prophet routing, so as to achieve the purpose of preventing, avoiding and canceling congestion, realizing adaptive buffer management of nodes and dynamically controlling congestion of network. Simulations were conducted on the ONE(Opportunistic Networking Environment) platform using Working Day Movement (WDM) model. In the simulation, ERC² was 66.18% higher than Prophet in message delivery rate. The average latency of ERC² was decreased by 48.36%, and the forwarding number was increased by 22.83%. The simulation results show that ERC² has better network performance than Epidemic and Prophet routing algorithms in scenarios with different levels of congestion.

With the increase of High Performance Computation (HPC) system scale, it's very important to increase the efficiency of the checkpoint. A model to compute the quasi-optimal period for hierarchical checkpoint protocol was proposed. First, the execution of an application in HPC system was assessed, and checkpoint period optimization problem was abstracted as the nonlinear checkpoint cost model. Second, the hierarchical checkpoint cost formula was derived by simulating the possible fault location; two deceleration parameters and an acceleration parameter were introduced to reflect the impact of message logging on the hierarchical checkpoint. The simulation results show that, compared with the quasi-optimal period checkpoint cost, the average error value of the proposed model is below 5%, which is 20% less than that of the traditional model based on Markov chain. The proposed model can signally increase the efficiency of the hierarchical checkpoint protocol; meanwhile enhance the availability of the HPC system.