Towards Energy Efficient LPWANs through Learning-based Multi-hop Routing

TL;DR

This paper introduces a reinforcement learning-based routing algorithm for LPWANs that significantly reduces energy consumption by enabling multi-hop communication, especially as the network operates over time.

Contribution

It proposes EMH, a simple epsilon-greedy RL algorithm, to identify energy-efficient multi-hop routes in LPWANs, addressing link unpredictability.

Findings

EMH achieves significant energy savings over single-hop approaches.

Energy savings increase as the network operates longer.

Testbed results validate the effectiveness of EMH in real-world scenarios.

Abstract

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.