# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network

## TL;DR

This paper introduces a new routing protocol for low-power networks that reduces transmission failures using a deep learning approach.

## Contribution

The novel FRDR protocol uses a deep Q-network and failure risk metrics to optimize multi-hop routing in LPWANs.

## Key findings

- FRDR improves packet delivery rate and network lifetime compared to existing protocols.
- The protocol maintains comparable transmission delay while reducing failure risks.
- A power regulation mechanism and RFRV metric enhance routing decisions.

## Abstract

Multi-hop routing over low-power wide-area networks (LPWANs) has emerged as a promising technology for extending network coverage. However, existing protocols face high transmission disruption risks due to factors such as dynamic topology driven by stochastic events, dynamic link quality, and coverage holes induced by imbalanced energy consumption. To address this issue, we propose a failure risk-aware deep Q-network-based multi-hop routing (FRDR) protocol, aiming to reduce transmission disruption probability. First, we design a power regulation mechanism (PRM) that works in conjunction with pre-selection rules to optimize end-device node (EN) activations and candidate relay selection. Second, we introduce the concept of routing failure risk value (RFRV) to quantify the potential failure risk posed by each candidate next-hop EN, which correlates with its neighborhood state characteristics (i.e., the number of neighbors, the residual energy level, and link quality). Third, a deep Q-network (DQN)-based routing decision mechanism is proposed, where a multi-objective reward function incorporating RFRV, residual energy, distance to the gateway, and transmission hops is utilized to determine the optimal next-hop. Simulation results demonstrate that FRDR outperforms existing protocols in terms of packet delivery rate and network lifetime while maintaining comparable transmission delay.