Enhancing Energy Efficiency in Multipath Routing for Industrial Internet of Things

Industrial Internet of Things (IIoT) applications, such as industrial process control, demand ultra-high reliability and bounded delay. The Reliable and Available Wireless (RAW) initiative within the IETF DetNet working group addresses these needs by applying IEEE 802.15.4 time-slotted channel hopping (TSCH) technology and leveraging techniques like Packet Replication, Elimination, and Ordering Functions (PREOF) to ensure deterministic performance for IIoT. However, while PREOF improves reliability, its redundant transmission mechanism inevitably increases energy consumption, conflicting with the energy constraints of TSCH nodes. The existing multipath routing approaches struggle to address this challenge, failing to jointly consider both energy efficiency and deterministic performance. Additionally, these approaches often overlook the delay variation caused by multipath transmissions of different lengths—a key factor that can undermine deterministic performance by increasing buffering requirements and affecting the predictability of data flows. In this paper, we investigate a multipath optimization problem aiming at improving energy efficiency and minimizing delay variation while meeting the requirements of bounded reliability and delay for deterministic flows. Considering the above multipath routing optimization problem, which aims to satisfy multiple objectives under multiple constraints, is typically NP-hard, solving these challenges with traditional methods is highly complex. Thus, we further propose a Energy-Efficient Multi-path Routing (EEMR) algorithm that utilizes deep reinforcement learning (DRL) to optimize the multipath selection, effectively enhancing energy efficiency for deterministism. EEMR can be extended to solve optimization problems in holistic-deterministic multi-domain scenarios, such as smart factories integrating 5G and DetNet. We compare the performance of our proposed method with several baseline methods. Empirical evaluations show that EEMR significantly reduces energy comsumption and delay variation compared to baseline methods under various environment settings.