A Tree-Structured Index Allocation Method with Replication over Multiple Broadcast Channels in Wireless Environments
Issue No.03 - March (2005 vol.17)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.36
Broadcast has often been used to disseminate frequently requested data efficiently to a large volume of mobile units over single or multiple channels. Since mobile units have limited battery power, the minimization of the access and tuning times for the broadcast data is an important problem. There have been many research efforts that focus on minimizing access and tuning times by providing indexes on the broadcast data. In this paper, we have studied an efficient index allocation method for broadcast data with skewed access frequencies over multiple physical channels which cannot be coalesced into a single high bandwidth channel. Previously proposed index allocation techniques have one of two problems. The first problem is that they require equal size for both index and data. The second problem is that their performance degrades when the number of given physical channels is not enough. These two problems will result in an increased average access time for the broadcast data. To cope with these problems, we propose a tree-structured index allocation method. Our method minimizes the average access time by broadcasting the hot data and their indices more frequently than the less hot data and their indexes over the dedicated index and data channels. We present an in-depth experimental and theoretical analysis of our method by comparing it with other similar techniques. Our performance analysis shows that it significantly decreases the average access and tuning times for the broadcast data over existing methods.
Mobile databases, multiple broadcast channels, alphabetic Huffman trees, index allocation method, data dissemination, broadcast data.
Byungkyu Lee, Sakti Pramanik, "A Tree-Structured Index Allocation Method with Replication over Multiple Broadcast Channels in Wireless Environments", IEEE Transactions on Knowledge & Data Engineering, vol.17, no. 3, pp. 311-325, March 2005, doi:10.1109/TKDE.2005.36