Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy

Citació

  • Barrachina-Muñoz S, Bellalta B. Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017. [5 p.]. DOI: 10.1109/PIMRC.2017.8292373

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Descripció

  • Resum

    Despite being a relatively new communication technology, Low-Power Wide Area Networks (LPWANs) have shown their suitability to empower a major part of Internet of Things applications. Nonetheless, most LPWAN solutions are built on star topology (or single-hop) networks, often causing lifetime shortening in stations located far from the gateway. In this respect, recent studies show that multi-hop routing for uplink communications can reduce LPWANs' energy consumption significantly. However, it is a troublesome task to identify such energetically optimal routing through trial-and-error brute-force approaches because of time and, especially, energy consumption constraints. In this work we show the benefits of facing this exploration/exploitation problem by running centralized variations of the multi-arm bandit's e-greedy, a well-known online decision-making method that combines best known action selection and knowledge expansion. Important energy savings are achieved when proper randomness parameters are set, which are often improved when conveniently applying similarity, a concept introduced in this work that allows harnessing the gathered knowledge by sporadically selecting unexplored routing combinations akin to the best known one.
  • Descripció

    Comunicació presentada al 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), celebrat els dies 8 a 13 d'octubre de 2017 a Montreal, Canadà.
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