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 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 represent a fundamental limitation in network planning, not only for wireless sensor networks but also in WiFi meshes.

RLNC reduces signaling 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.