  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 routings 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 epsilon-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.
