Spatial Queries in Wireless Broadcast Systems

A Hilbert Curve-Based Distributed Index for Window Queries in Wireless Data Broadcast Systems

“…Since objects 8, 9, and 11 are in the same group, the local index in index bucket I1 has three index tuples pointing to the data buckets containing these objects, i.e.,  [8,8], 2 ,  [9, 9], 3 , and  [11, 11], 4 shown in Figure 4. Note that in Figure 4, when index bucket I2 is broadcast, bucket number 2 containing object 8 has been broadcast; therefore, index tuple  [8,8], 2' in index bucket I2 points to bucket number 2 in the next cycle. The index tuples of the local index in index buckets I3, I7 and I8 are in the same manner.…”

“…NN queries return only one data item in the spatial space closest to a given query point. kNN queries return k data items in the spatial space closest to a given query point [8]. Among them, the window query, one of the essential spatial queries, is very useful for spatial selection.…”

“…Window queries find data items that are located within a given window, which is a rectangle in a 2-dimensional space [8]. NN queries return only one data item in the spatial space closest to a given query point.…”

“…The studies in [3,7] are specified for kNN queries. The Hilbert curve index [8] provides an index structure to support window queries and kNN queries. In [5], Lee and Zheng proposed the distributed spatial index (DSI) to improve the performance of the Hilbert curve index.…”

Quad-splitting algorithm for a window query on a Hilbert curve

“…There have been found many applications in a variety of fields including image processing and compression [9,21], spatial query [7], wireless sensor networks [1], wireless broadcast system [26], neural networks [5], genome visualization [11], index of multi-dimensional data [20], spatiotemporal index [25] and bandwidth compression [2].…”