Energy Efficient Network Path Reconfiguration for Industrial Field Data

TL;DR

The paper addresses energy-efficient, reliable data distribution in dynamic industrial wireless networks by introducing a distributed, lifetime-guided path reconfiguration framework (DistrDataFwd). It leverages an epoch-based lifetime metric to drive local, loop-free reconfigurations that handle nodes going offline and returning online, reducing central reconfiguration overhead. Through Matlab-based simulations, it shows that DistrDataFwd achieves energy consumption comparable to centralized reconfigurations while delivering data with higher reliability and generally favorable latency compared to RPL, especially under disruptions. The work advances practical, scalable strategies for sustaining industrial IoT workloads with strict energy constraints and evolving network topologies.

Abstract

Energy efficiency and reliability are vital design requirements of recent industrial networking solutions. Increased energy consumption, poor data access rates and unpredictable end-to-end data access latencies are catastrophic when transferring high volumes of critical industrial data in strict temporal deadlines. These requirements might become impossible to meet later on, due to node failures, or excessive degradation of the performance of wireless links. In this paper, we focus on maintaining the network functionality required by the industrial, best effort, low-latency applications after such events, by sacrificing latency guarantees to improve energy consumption and reliability. We avoid continuously recomputing the network configuration centrally, by designing an energy efficient, local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, our method locally reconfigures the data distribution paths, when a network node fails. Additionally, our method also regulates the return to an operational state of nodes that have been offline in the past. We compare the performance of our method through simulations to the performance of other state of the art protocols and we demonstrate performance gains in terms of energy consumption, data delivery success rate, and in some cases, end-to-end data access latency. We conclude by providing some emerging key insights which can lead to further performance improvements.

Figures (6)

• Figure 1: Industrial field architecture.
• Figure 2: Loop avoidance - forward loop.
• Figure 3: Loop avoidance - backward loop.
• Figure 4: Performance results when nodes and links can go offline.
• Figure 5: Performance results when nodes can return online.