IoT & Mesh

Code On’s proprietary coding technology, Random Linear Network Coding (RLNC) brings radical new tools to mesh and IoT networks, including powerful routing, dissemination, collection and reliability paradigms, all the while breaking latency barriers

Target Markets

The growth of Internet-of-Things (IoT) is making mesh networks ubiquitous. Faced with new latency, reliability, and scalability requirements, RLNC stands to play a major role as a base ingredient in future mesh networks. Prospective conventional wireless markets include:

  • WiFi networks,

  • Small cell,

  • Smart grids, and

  • Device-to-device (D2D) in LTE-A.

A number of emerging wired and Internet-based markets also stand to gain from RLNC’s mesh features, including:

  • Peer-to-Peer (P2P) networks and applications,

  • WebRTC,

  • Software-Defined Networking (SDN) and Network Function Virtualization (NFV),

  • Cloud services,

  • Cloud security, and

  • Distributed and dynamic storage applications.

Most importantly, a number of new IoT markets for RLNC have recently emerged, revolving mainly around embedded systems. Their flagship applications include:

  • Vehicle-to-Vehicle (V2V),

  • Machine-to-Machine (M2M),

  • Radio-Frequency Identification (RFID),

  • Sensor Networks, and

  • Home Automation.

 

IoT Mesh Applications

Mesh networks are becoming ubiquitous in both wireless access and wide-area overlay networks. The Internet of Things (IoT), the tagging and virtual representation of everyday appliances as a smart network, is the ultimate embodiment of a global mesh network. Such a vision is starting to be realized through the development and integration of various sensor networks, wireless local and metro networks, device-to-device (D2D) networks, as well as satellite networks.

Whether they are built of wireless links or overlay tunnels, mesh networks are often subject to harsh packet losses.

The ITU-T G.hn family of home network standards, for instance, specifies local mesh networks built over power lines, phone lines, and coaxial cables. While coaxial segments benefit from higher rates, noisy power lines pose particular technical challenges and support limited rates. Wireless sensor networks such as monitoring networks are often vulnerable to weather conditions and geographical layout, also leading to high packet losses.

To counter packet losses, mesh networks resort to frequent packet retransmissions. The resulting large energy consumption represents a fundamental limitation in network planning, not only for wireless sensor networks but also in Wi-Fi meshes.

RLNC reduces signalling by simplifying broadcasting, dissemination, and retransmission operations. Furthermore, it minimizes the required number of transmissions across the network in dynamic loss and connectivity conditions, leading to significant latency and energy gains.

Furthermore, RLNC enables new mesh routing and cooperation protocols. By facilitating D2D cooperative networks, RLNC creates new opportunities for V2V, file sharing, gaming, and sensor applications.

How It Works

A number of RLNC technologies are combined to create superior wireless mesh networks.

First, RLNC-enhanced mesh protocols allow nodes that are adjacent to any given route, also called “helper” nodes, to opportunistically store overheard packets and code them to create new redundancy. Although they have similar sizes, coded packets are more resilient since they can substitute for any of the original packets that were used to encode them. Novel protocols making use of RLNC are capable of reducing network latencies and improving throughput significantly across mesh networks.

Second, the ability of adjacent nodes to support communication across any given link is increased through RLNC’s unique recoding feature. Recoding enables helper and intermediate nodes to create new coded packets from existing coded packets without any need to decode them first (i.e., recoded packets).

Owing to the nature of RLNC, no packet-level coordination is required between source, intermediate, helper, and destination nodes, thus improving network resilience and latency.

Third, RLNC has the unique ability to code on the fly. This means that streaming or intermediate nodes can inject redundancy locally within the stream, without the need for defining and buffering coding blocks. This is called sliding window coding. Even when packets are divided into blocks and transported as generations, on-the-fly coding enables the source and intermediate nodes to start creating redundancy as soon as a few packets are present. Coding on the fly, whether it is in generations or over a sliding window, results in significant latency gains while maintaining highly tuneable levels of reliability.

Performance Improvements

Throughput is multiplied in a wireless relay and mobile mesh networks.

  • Latency (as measured through ping delay) is reduced by one order of magnitude in wireless meshes, while file transfer delays are significantly decreased in D2D applications.

  • Energy consumption is greatly reduced in wireless mesh networks

  • Ad-hoc wireless mesh networks achieve similar QoE targets using only a fraction of the bandwidth.

  • Ad-hoc wireless mesh networks increase their range by an order of magnitude.

  • RLNC-enabled minimum-cost multicast routing significantly reduces bandwidth usage and energy consumption in provider backbone networks.

  • P2P reliability and security are improved, reducing overhead over E2E coding by more than an order of magnitude at low attack probabilities.