Energy Efficient Network Path Reconfiguration for Industrial Field Data

TL;DR

This paper presents a distributed, energy-efficient network path reconfiguration method for industrial networks that maintains functionality after node failures, improving energy use and data reliability at the expense of some latency.

Contribution

It introduces a novel local and distributed path reconfiguration approach that enhances energy efficiency and reliability in industrial networks without centralized recomputation.

Findings

Reduces energy consumption compared to existing protocols.

Improves data delivery success rate.

Maintains acceptable performance despite node failures.

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.