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A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management | | Emilio Ancillotti
; Carlo Vallati
; Raffaele Bruno
; Enzo Mingozzi
; | | Date: |
1 Jun 2022 | | Abstract: | Over the last few years, standardisation efforts are consolidating the role
of the Routing Protocol for LowPower and Lossy Networks (RPL) as the standard
routing protocol for IPv6 based Wireless Sensor Networks (WSNs). Although many
core functionalities are well defined, others are left implementation
dependent. Among them, the definition of an efficient link quality estimation
(LQE) strategy is of paramount importance, as it influences significantly both
the quality of the selected network routes and nodes’ energy consumption. In
this paper, we present RLProbe, a novel strategy for link quality monitoring in
RPL, which accurately measures link quality with minimal overhead and energy
waste. To achieve this goal, RLProbe leverages both synchronous and
asynchronous monitoring schemes to maintain up-to-date information on link
quality and to promptly react to sudden topology changes, e.g. due to mobility.
Our solution relies on a reinforcement learning model to drive the monitoring
procedures in order to minimise the overhead caused by active probing
operations. The performance of the proposed solution is assessed by means of
simulations and real experiments. Results demonstrated that RLProbe helps in
effectively improving packet loss rates, allowing nodes to promptly react to
link quality variations as well as to link failures due to node mobility. | | Source: | arXiv, 2206.00273 | | Services: | Forum | Review | PDF | Favorites | |
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