For the routing problem that nodes in mobile wireless sensor network, based on random moving model, a low energy consumption routing strategy named DTVC (Data Transmission based on Virtual Currency) was proposed. When two nodes met each other, the buyer and the seller determined the price of data message and selected relay node according to node attributes and data message attributes. To improve the network performance, the number of the data message's replicas was controlled according to node type and data messages in the queue were sorted according to each message's delay tolerance. The nodes in the network were divided into source nodes and relay nodes for each data message and only the source node could copy it. The smaller the delay tolerance was, the greater the priority was. In order to reduce the energy consumption in the network, the data message in the storage queue that had been transmitted successfully was deleted according to the message broadcast by the sink node. The simulation results on Matlab showed that the data delivery rate of DTVC was increased by at least 2.5%, and the average number of replicas was reduced by at least 25% than those of FAD (the message Fault tolerance-based Adaptive data Delivery scheme), FLDEAR (Fuzzy-Logic based Distance and Energy Aware Routing protocol) and a routing algorithm based on energy consumption optional evolution mechanism.

Energy efficient routing is a challenging problem in resource constrained Mobile Wireless Sensor Network (MWSN). Focused on the issue that the energy consumption of the inter-community routing in the mobile sensor network is too fast, an Energy-balanced Routing Algorithm for Inter-community (ERAI) was proposed. In ERAI, a new routing metric FC (Forwarding Capacity) based on the residual energy of nodes and the probability of encounter was designed. Then, this metric FC and the directional information of encountered nodes were used for selection of a relay node to forward the messages. The experimental data show that the death time of the first node of ERAI was later than that of Epidemic and PROPHET by 12.6%-15.6% and 4.5%-8.3% respectively, and the residual energy mean square deviation of ERAI was less than that of Epidemic and PROPHET. The experimental results show that the ERAI can balance the energy consumption of each node to a certain extent, and thus prolongs the network lifetime.

To solve the user similarity between trajectories formed by mobility data, an algorithm based on Location Sequence Generalized Suffix Tree (LSGST) was proposed. First, the location sequence was extracted from mobility data. At the same time the location sequence was mapped to a string. The transformation from the processing of location sequence to the processing of string was completed. Then the location sequence generalized suffix tree between different users was constructed. The similarity was calculated in detail from the number of similar positions, longest common subsequence and the frequent common position sequence. The theoretical analysis and simulation results show that the proposed algorithm has ideal effect in terms of similarity measure. Besides, compared to the ordinary construction method, the proposed algorithm has low time complexity. In the comparison with dynamic programming and naive string-matching, the proposed algorithm has higher efficiency when searching for the longest common sub-string and frequent public position sequence. The experimental results indicate that the LSGST can measure the similarity effectively, meanwhile reduces the trajectory data when searching for the measurement index, and has better performance in time complexity.