Masaaki Tanizaki

Randomization in traffic information sharing systems

In this paper, we consider a traffic information sharing system based on Floating Car Data (FCD). FCD is one of the methods used to gather traffic information; it uses vehicles as sensor nodes that transmit their speed to the server. The traffic information sharing system broadcasts speed information updated by such transmission. Vehicles receiving broadcasted speed information can calculate travel time and select a minimum time route. The communication cost and the load of the server are issues, because such a traffic information sharing system can generate a lot of wireless communication between the vehicles and the server [2][3]. However, reducing the amount of communication lowers the accuracy of information provided by the server. In this paper we propose an Information Cost Model to quantify a trade-off relationship between the communication cost of the system and the accuracy of information. Additionally, we propose a randomized method to reduce the number of messages from clients to the server by avoiding redundant transmissions. We compared the performance of our proposed method with that of a conventional method, using real traffic data from Chicago highways. The result shows that our proposed method generally outperforms the conventional method.

Dual-heap k NN: k -nearest neighbor search for spatial data retrieval in embedded DBMS

In this paper, we present a kNN search method called the dual-heap kNN method, which is used in an embedded database management system (DBMS) for in-vehicle information systems. The dual-heap kNN method is based on two conventional kNN methods: (1) the RKV method and (2) the HS method. The RKV method and the HS method based on depth-first traversal and best-first traversal, respectively, shorten the search time. Our method not only shortens the search time but also reduces the capacity of the memory usage. Our simulation experiments suggest that our method results in the same number of disk accesses as that of the HS method, which is up to 12% smaller than the RKV method. Our method results in a memory usage capacity that is up to 24% larger than that of the RKV method and up to 68% smaller than that of the HS method. In addition, our prototype evaluation using actual data indicates that our method is applicable to in-vehicle information systems.