Systematic Network Coding with Overlap for IoT Scenarios

Abstract

The presence of IoT in current networking scenarios is more relevant every day. IoT covers a wide range of applications, ranging from wearable devices to vehicular communications. With the consolidation of Industry 4.0, IIoT (Industrial IoT) environments are becoming more common. Communications in these scenarios are mostly wireless, and due to the lossy nature of wireless communications, the loss of information becomes an intrinsic problem. However, loss recovery schemes increase the delay that characterizes any communication. On the other hand, both reliability (robustness) and low delay are crucial requirements for some applications in IIoT. An interesting strategy to improve both of them is the use of Network Coding techniques, which have shown promising results, in terms of increasing reliability and performance. This work focuses on a possible new coding approach, based on systematic network coding scheme with overlapping generations. We perform a thorough analysis of its behavior. Based on the results, we draw out a number of conclusions for practical implementations in wireless networks, focusing our interest in IIoT environments.

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

“…We find that rateless recoding with a finite recoding window size reduces the packet delay and increases the recoding and decoding throughput compared to recoding with a prescribed code rate or infinite recoding window size. Section IV-C compares the three specified SpaRec strategies operating with idle slot utilization, rateless recoding, and a finite window size against conventional recoding [67], [68], systematic RLNC [60]- [66], and a conventional recoding with small buffer benchmark [14]. We find that the proposed SpaRec strategies substantially reduce the packet losses while reducing the mean in-order packet delays down to approximately half of the benchmarks.…”

“…Systematic coding is generally preferred for low-latency communication [56]- [59] and there have been extensive studies on low-latency communication schemes employing systematic RLNC, see e.g., [60]- [66]. Therefore, it is important to develop and evaluate sparsity-preserving recoding strategies for sparse systematic RLNC.…”

“…With the systematic Random Linear Network Coding (RLNC) coding scheme for a prescribed code rate c [60]- [66], the source first sends the entire generation of G uncoded packets, followed by p coded packets, whereby c = G/(G+p). Intermediate nodes follow the same scheme for the recoding.…”

An Irregular Graph Based Network Code for Low-Latency Content Distribution

“…It is noteworthy that in networks with known topologies, e.g., (parallel) line networks, there exists sparse RLNC schemes with low decoding costs and almost zero overhead, e.g., [17,[30][31][32][33][34]. However, we note that these schemes do not apply to our interested scenarios where the network topology may be not known a priori, dynamically changing, and/or has cycles.…”

Robust QUIC: Integrating Practical Coding in a Low Latency Transport Protocol

“…This offers a different view on convolutional codes, where when one of the concurrent generations is finished, it is replaced with a new one. This view was exploited in [26], focusing on concurrent generations rather than on a coding window.…”