Soroush, Emad2008-08-192008-08-1920082008-08-19http://hdl.handle.net/1828/1078In many emerging applications, data streams are monitored in a network environment. Due to limited communication bandwidth and other resource constraints, a critical and practical demand is to online compress data streams continuously with quality guarantee. Although many data compression and digital signal processing methods have been developed to reduce data volume, their super-linear time and more-than-constant space complexity prevents them from being applied directly on data streams, particularly over resource-constrained sensor networks. In this thesis, we tackle the problem of online quality guaranteed compression of data streams using fast linear approximation (i.e., using line segments to approximate a time series). Technically, we address two versions of the problem which explore quality guarantees in different forms. We develop online algorithms with linear time complexity and constant cost in space. Our algorithms are optimal in the sense that they generate the minimum number of segments that approximate a time series with the required quality guarantee. To meet the resource constraints in sensor networks, we also develop a fast algorithm which creates connecting segments with very simple computation. The low cost nature of our methods leads to a unique edge on the applications of massive and high speed streaming environment, low bandwidth networks, and heavily constrained nodes in computational power (e.g., tiny sensor nodes). We implement and evaluate our methods in the application of an acoustic wireless sensor network.enAvailable to the World Wide WebData StreamingOnline Linear Approximation AlgorithmsWireless Sensor NetworksUVic Subject Index::Sciences and Engineering::Applied Sciences::Computer scienceThesisSoroush, E., Wu, K., and Pei, J. 2008. Fast and quality-guaranteed data streaming in resource-constrained sensor networks. In Proceedings of the 9th ACM international Symposium on Mobile Ad Hoc Networking and Computing (Hong Kong, Hong Kong, China, May 26 - 30, 2008). MobiHoc '08. ACM, New York, NY, 391-400. DOI= http://doi.acm.org/10.1145/1374618.1374670