Fast and Quality-Guaranteed Data Streaming in Resource-Constrained Sensor Networks

TL;DR

This paper presents fast, online algorithms for data stream compression in resource-limited sensor networks, achieving quality guarantees with minimal computational and space resources, suitable for real-time applications.

Contribution

It introduces linear-time, constant-space online algorithms for quality-guaranteed data stream compression, optimized for resource-constrained sensor networks.

Findings

Algorithms are optimal in segment minimization.

Methods operate in linear time and constant space.

Validated on acoustic wireless sensor networks.

Abstract

In 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 paper, 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 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 fast streaming environment, low bandwidth networks, and heavily constrained nodes in computational power. We implement and evaluate our methods in the application of an acoustic wireless sensor network.