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DNS-based dynamic context resolution for SCHC

Antoine Bernard, Sandoche Balakrichenan, Michel Marot, Benoit Ampeau

TL;DR

The paper tackles scalable management of SCHC contexts for IPv6 over LPWANs by introducing a DNS-based mechanism to locate and download remote SCHC rule hashes and their HTTP-hosted content. End Devices query DNS to obtain a rule identifier and server location, allowing the Application Server to fetch and cache rules as needed, with freshness checks ensuring updates. Experiments on a real LoRaWAN testbed show that DNS lookups impose modest delays and that overall end-to-end RTT is dominated by LoRa radio timing, though remote rule retrieval remains feasible within LPWAN constraints when caching is effective. The approach enables rapid, scalable deployment of SCHC contexts across large, diverse device populations and can be extended to store additional device metadata in DNS for interoperability and provisioning.

Abstract

LPWANs are networks characterised by the scarcity of their radio resources and their limited payload size. LoRaWAN offers an open, easy-to-deploy and efficient solution to operate a long-range network. To efficiently communicate using IPv6, the LPWAN working group from the IETF developed a solution called Static Context Header Compression (SCHC). It uses context rules, which are linked to a given End Device, to compress the IPv6 and UDP header. Since there may be a huge variety of End Devices profile, it makes sense to store the rules remotely and use a system to retrieve the profiles dynamically. In this paper we propose a mechanism based on DNS to find the context rules associated with an End Device, allowing it to be downloaded from an HTTP Server. We evaluate the corresponding delay added to the communications using experimental measurements from a real testbed.

DNS-based dynamic context resolution for SCHC

TL;DR

The paper tackles scalable management of SCHC contexts for IPv6 over LPWANs by introducing a DNS-based mechanism to locate and download remote SCHC rule hashes and their HTTP-hosted content. End Devices query DNS to obtain a rule identifier and server location, allowing the Application Server to fetch and cache rules as needed, with freshness checks ensuring updates. Experiments on a real LoRaWAN testbed show that DNS lookups impose modest delays and that overall end-to-end RTT is dominated by LoRa radio timing, though remote rule retrieval remains feasible within LPWAN constraints when caching is effective. The approach enables rapid, scalable deployment of SCHC contexts across large, diverse device populations and can be extended to store additional device metadata in DNS for interoperability and provisioning.

Abstract

LPWANs are networks characterised by the scarcity of their radio resources and their limited payload size. LoRaWAN offers an open, easy-to-deploy and efficient solution to operate a long-range network. To efficiently communicate using IPv6, the LPWAN working group from the IETF developed a solution called Static Context Header Compression (SCHC). It uses context rules, which are linked to a given End Device, to compress the IPv6 and UDP header. Since there may be a huge variety of End Devices profile, it makes sense to store the rules remotely and use a system to retrieve the profiles dynamically. In this paper we propose a mechanism based on DNS to find the context rules associated with an End Device, allowing it to be downloaded from an HTTP Server. We evaluate the corresponding delay added to the communications using experimental measurements from a real testbed.

Paper Structure

This paper contains 7 sections, 6 figures.

Figures (6)

  • Figure 1: Context rule example (Source schc)
  • Figure 2: Measurement Platform's Architecture
  • Figure 7: Cumulative distribution function of the Application Server Response Time $t1' - t0'$ (in %) against (logarithmic scale) time in ms for all scenarios.
  • Figure 8: Cumulative distribution function of the DNS Response Time $t1" - t0"$ (in %) against time in ms for Scenario 3 compared and from RIPE Atlas atlas Measurements
  • Figure 9: Cumulative distribution function of the Round Trip Time $t1 - t0$ (in %) against time in ms for all scenarios (all the curves are superposed)
  • ...and 1 more figures