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Disjoint multipath RPL for QoE/QoS provision in the Internet of Multimedia Things

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Abstract

RPL is the IETF standardized IPv6 Routing Protocol for Low-power and lossy networks (LLNs). The major effort is therefore made on handling low data rate traffic. Meanwhile, the Internet of Multimedia Things emerged as one hot topic of the Internet of Things. In LLNs, providing a descent quality of service (QoS) along with a user quality of experience (QoE) for multimedia applications is challenging. High bandwidth and significant computation capabilities are necessary while LLNs are very resource constrained. To raise the available bandwidth to accommodate high data rate applications, we propose to simultaneously transmit a flow on multiple disjoint paths. To do so, we exploit the DODAG structure, already maintained by RPL, to build disjoint paths without incurring extra overhead. To reduce the amount of data to send while considering the low computation resources of video sensors, we propose to apply a priority-based low-complexity encoding scheme on the captured pictures. Highest priority data can be replicated on more than one path. Our conducted experiments using simulations as well as a real testbed show that the best QoS and QoE are obtained when multiple paths are used along with the replication of highest priority data packets.

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Notes

  1. https://www.iot-lab.info/docs/boards/iot-lab-m3/.

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Acknowledgements

Authors thank FIT IoT-LAB project for providing testbed and tools to perform this paper experiments. This work was supported in part by the PHC TASSILI 21MDU323.

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Authors and Affiliations

  1. RELA(CS)2 Labs, Larbi Ben M’hidi University, Oum El Bouaghi, Algeria

    Souhila Kettouche & Lakhdar Derdouri

  2. Lorraine University, CNRS, CRAN, 54000, Nancy, France

    Moufida Maimour

Authors
  1. Souhila Kettouche
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  2. Moufida Maimour
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  3. Lakhdar Derdouri
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Correspondence to Moufida Maimour.

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Kettouche, S., Maimour, M. & Derdouri, L. Disjoint multipath RPL for QoE/QoS provision in the Internet of Multimedia Things. Computing 104, 1677–1699 (2022). https://doi.org/10.1007/s00607-022-01054-9

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