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Incremental firmware update using an efficient differencing algorithm: poster abstract

Published:16 November 2020Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on November 25, 2020. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

ABSTRACT

Modern IoT solutions require minimisation of the transmitted data during a firmware update, in order to save energy for the constrained devices. To accommodate this requirement, IoT nodes can be updated incrementally using only parts of the current firmware, which is already stored in their flash memory in order to reconstruct the new firmware locally. This the role of the so-called differencing algorithm that executes in a firmware server and aims to detect common segments between the current and the new firmware, producing an encoded small delta script, which is finally transmitted to the IoT nodes. In this work, we present a differencing algorithm that operates in byte-level and can compute optimal, in terms of size, delta scripts in O(nlogn) time and O(n) space complexity.

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References

  1. K. Arakadakis et al. Firmware over-the-air programming techniques for IoT networks - A survey. Accessed: 2020-10-02. 2020. url: https://arxiv.org/abs/2009.02260.Google ScholarGoogle Scholar
  2. J. Hu et al. "Reprogramming with Minimal Transferred Data on Wireless Sensor Network". In: Proc. of the 6th International Conference on Mobile Adhoc and Sensor Systems. IEEE, 2009.Google ScholarGoogle Scholar
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  4. B. Mo et al. "An efficient differencing algorithm based on suffix array for reprogramming wireless sensor networks". In: Proc. of the 2012 IEEE International Conference on Communications (ICC). 2012, pp. 773--777.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Incremental firmware update using an efficient differencing algorithm: poster abstract

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      • Published in

        cover image ACM Conferences
        SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
        November 2020
        852 pages
        ISBN:9781450375900
        DOI:10.1145/3384419

        Copyright © 2020 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 November 2020

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        Overall Acceptance Rate174of867submissions,20%

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