  Low-power wide area networks (LPWANs) have been identified as one of the top
emerging wireless technologies due to their autonomy and wide range of
applications. Yet, the limited energy resources of battery-powered sensor nodes
is a top constraint, especially in single-hop topologies, where nodes located
far from the base station must conduct uplink (UL) communications in high power
levels. On this point, multi-hop routings in the UL are starting to gain
attention due to their capability of reducing energy consumption by enabling
transmissions to closer hops. Nonetheless, a priori identifying energy
efficient multi-hop routings is not trivial due to the unpredictable factors
affecting the communication links in large LPWAN areas. In this paper, we
propose epsilon multi-hop (EMH), a simple reinforcement learning (RL) algorithm
based on epsilon-greedy to enable reliable and low consumption LPWAN multi-hop
topologies. Results from a real testbed show that multi-hop topologies based on
EMH achieve significant energy savings with respect to the default single-hop
approach, which are accentuated as the network operation progresses.
