Optimizing IoT and Web Traffic Using Selective Edge Compression

TL;DR

This paper presents a selective edge compression method for IoT and web traffic that enhances transfer speeds and reduces data size, addressing energy and bandwidth constraints effectively.

Contribution

It introduces a novel mechanism for selective compression at network edges based on data and network conditions, improving transfer efficiency and data savings.

Findings

Speeds up web transfers by over 2x on average

Reduces data size to 19% of original

Effective under both fixed and dynamic network conditions

Abstract

Internet of Things (IoT) devices and applications are generating and communicating vast quantities of data, and the rate of data collection is increasing rapidly. These high communication volumes are challenging for energy-constrained, data-capped, wireless mobile devices and networked sensors. Compression is commonly used to reduce web traffic, to save energy, and to make network transfers faster. If not used judiciously, however, compression can hurt performance. This work proposes and evaluates mechanisms that employ selective compression at the network's edge, based on data characteristics and network conditions. This approach (i) improves the performance of network transfers in IoT environments, while (ii) providing significant data savings. We demonstrate that our library speeds up web transfers by an average of 2.18x and 2.03x under fixed and dynamically changing network conditions respectively. Furthermore, it also provides consistent data savings, compacting data down to 19% of the original data size.