# Enhancing REST HTTP with Random Linear Network Coding in Dynamic Edge   Computing Environments

**Authors:** Cao Vien Phung, Jasenka Dizdarevic, Francisco Carpio, Admela Jukan

arXiv: 1903.03410 · 2021-02-24

## TL;DR

This paper proposes combining REST HTTP with random linear network coding to improve data transmission reliability and reduce reconnection times in highly dynamic, unreliable IoT environments.

## Contribution

It introduces a novel integration of RLNC with REST HTTP to enhance performance in unreliable, mobile IoT networks, addressing a key challenge in current communication protocols.

## Key findings

- RLNC reduces retransmissions in REST HTTP communications.
- The approach decreases reconnection times in dynamic environments.
- It improves data exchange reliability in IoT applications.

## Abstract

The rising number of IoT devices is accelerating the research on new solutions that will be able to efficiently deal with unreliable connectivity in highly dynamic computing applications. To improve the overall performance in IoT applications, there are multiple communication solutions available, either proprietary or open source, all of which satisfy different communication requirements. Most commonly, for this kind of communication, developers choose REST HTTP protocol as a result of its ease of use and compatibility with the existing computing infrastructure. In applications where mobility and unreliable connectivity play a significant role, ensuring a reliable exchange of data with the stateless REST HTTP protocol completely depends on the developer itself. This often means resending multiple request messages when the connection fails, constantly trying to access the service until the connection reestablishes. In order to alleviate this problem, in this paper, we combine REST HTTP with random linear network coding (RLNC) to reduce the number of additional retransmissions. We show how using RLNC with REST HTTP requests can decrease the reconnection time by reducing the additional packet retransmissions in unreliable highly dynamic scenarios.

## Full text

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## Figures

17 figures with captions in the complete paper: https://tomesphere.com/paper/1903.03410/full.md

## References

15 references — full list in the complete paper: https://tomesphere.com/paper/1903.03410/full.md

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Source: https://tomesphere.com/paper/1903.03410