No abstract

Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the "data sparsity problem", i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis

Most future large-scale sensor networks are expected to follow a two-tier architecture which consists of resource-rich master nodes at the upper tier and resource-poor sensor nodes at the lower tier. Sensor nodes submit data to nearby master nodes which then answer the queries from the network owner on behalf of sensor nodes. Relying on master nodes for data storage and query processing raises severe concerns about data confidentiality and query-result correctness when the sensor network is deployed in hostile environments. In particular, a compromised master node may leak hosted sensitive data to the adversary; it may also return juggled or incomplete query results to the network owner. This paper, for the first time in the literature, presents a suite of novel schemes to secure multidimensional range queries in tiered sensor networks. The proposed schemes can ensure data confidentiality against master nodes and also enable the network owner to verify with very high probability the authenticity and completeness of any query result by inspecting the spatial and temporal relationships among the returned data. Detailed performance evaluations confirm the high efficacy and efficiency of the proposed schemes.

No abstract

No abstract