Sampling based algorithms for quantile computation in sensor networks

We study the problem of computing approximate quantiles in large-scale sensor networks communication-efficiently, a problem previously studied by Greenwald and Khana [12] and Shrivastava et al. [21]. Their algorithms have a total communication cost of O(k log2 n/ǫ) and O(k log u/ǫ), respectively, where k is the number of nodes in the network, n is the total size of the data sets held by all the nodes, u is the universe size, and ǫ is the required approximation error. In this paper, we present a sampling based quantile computation algorithm with O( √ kh/ǫ) total communication (h is the height of the routing tree), which grows sublinearly with the network size except in the pathological case h = Θ(k). In our experiments on both synthetic and real data sets, this improvement translates into a 10 to 100-fold communication reduction for achieving the same accuracy in the computed quantiles. Meanwhile, the maximum individual node communication of our algorithm is no higher than that ...

• Type: Journal
• Year: 2011
• Where: SIGMOD
• Authors: Zengfeng Huang, Lu Wang, Ke Yi, Yunhao Liu
• Keywords: Analysis Of Algorithms, Approximation Error, Database, Problem Complexity, SIGMOD 2011