In order to solve the problem of low quality of results returned by spatial keyword bichromatic reverse k nearest neighbor query, a bichromatic reverse k nearest neighbor query method based on distance-keyword similarity constraint was proposed. Firstly, a threshold was set to filter out the low-quality users in the query results, so that the existence of users with relatively long spatial distance in the query results was avoided and the quality of the query results was ensured. Then, in order to support this query, an index of Keyword Multiresolution Grid rectangle-tree (KMG-tree) was proposed to manage the data. Finally, the Six-region-optimize algorithm based on Six-region algorithm was proposed to improve the query processing efficiency. The query efficiency of the Six-region-optimize algorithm was about 85.71% and 23.45% on average higher than those of the baseline and Six-region algorithms respectively. Experimental test and analysis were carried out based on real spatio-temporal data. The experimental results verify the effectiveness and high efficiency of the Six-region-optimize algorithm.

Due to the popularity of smart mobile terminals, sensitive information such as personal location privacy, check-in data privacy and trajectory privacy in the collected spatio-temporal data are easy to be leaked. In the current researches, protection technologies are proposed for the above privacy leakages respectively, and there is not a personalized spatio-temporal data privacy protection method to prevent the above privacy leakages for users. Therefore, a personalized privacy protection model for spatio-temporal data named ( p, q, ε)-anonymity and a Personalized Privacy Protection for Spatio-Temporal Data (PPP _ST) algorithm based on this model were proposed to protect the users' privacy data with personalized settings (location privacy, check-in data privacy and trajectory privacy). The heuristic rules were designed to generalize the spatio-temporal data to ensure the availability of the published data and realize the high availability of spatio-temporal data. In the comparison experiments, the data availability rate of PPP _ST algorithm is about 4.66% and 15.45% higher than those of Information Data Used through K-anonymity (IDU-K) and Personalized Clique Cloak (PCC) algorithms on average respectively. At the same time, the generalized location search technology was designed to improve the execution efficiency of the algorithm. Experiments and analysis were conducted based on real spatio-temporal data. Experimental results show that PPP _ST algorithm can effectively protect the privacy of personalized spatio-temporal data.

The existed TD-FTT (Time Dependent Fast Travel Time) algorithm, for answering K Nearest Neighbors ( KNN) query in time dependent road network, requires that the issued time and the arrival time of a query must be in the same time interval, which costs a long time in the preprocessing phase. To solve these problems, an Improved TD-FTT (ITD-FTT) algorithm based on dynamically selecting heuristic values was proposed. Firstly, in the preprocessing phase, the road network G _min with the minimum cost for each time interval was created by using time functions of edges. Secondly, a parallel method of utilizing Network Voronoi Diagram (NVD) in road network G _min was used to compute the nearest neighbors of nodes to reduce the time cost. Finally, in the query phase, the heuristic value was dynamically selected to get rid of the time interval limitation by calculating the time interval of the current arrival time of nodes. The experimental results show that in the preprocessing phase, the time cost of ITD-FTT is reduced by 70.12% compared with TD-FTT. In the query phase, the number of traversal nodes of ITD-FTT is 46.52% and 16.63% lower than TD-INE (Time Dependent Incremental Network Expansion) and TD-A (Time Dependent A star) algorithm respectively, and the response time of ITD-FTT is 47.76% and 18.24% lower than TD-INE and TD-A. The experimental results indicate that the ITD-FTT algorithm reduces the number of nodes by query expansion, decreases the time of searching the KNN results and improves the query efficiency.

To solve the problems of current multi-screen interaction systems such as high bandwidth occupancy of Wide Local Area Network (WLAN) and unstability between terminal devices and the router, a multi-screen interaction system based on Wi-Fi Direct was proposed, which directly connected two intelligent devices not via any access points and delivered content of one device to the other. The design of the system was detailedly described. According to the the principles of low delay and high compatibility, the proposed system was realized by developing an Android APP used on a smart phone or a smart TV. The test of the proposed system in practice shows that time delay and packet loss rate have been reduced in comparison with conventional multi-screen system depending on the WLAN. Also, the connection provided by Wi-Fi Direct between two devices is stable and the distance has been doubled. Besides, the structure of the proposed system has no request for WLAN bandwidth.