Distributed Data Access in Industrial Edge Networks

TL;DR

The paper addresses the challenge of distributed data access in multi-hop wireless industrial edge networks by formulating DDA, which assigns data pieces to cache nodes while meeting a delay bound L_max and maximizing network lifetime T(x). It proves DDA is NP-complete and proposes a practical two-step solution: CPS to generate delay-bounded path sets and DCA to select paths that prolong lifetime; this is complemented by online enhancements DCA+ and ProportionallyFairRotation (PFR) for dynamic reconfiguration. Experimental validation on a real testbed and extensive simulations show that the distributed, low-power approach, especially PFR, markedly extends network lifetime and improves energy efficiency compared to centralized schemes, while preserving delay guarantees. The findings support deploying distributed data management in Industry 4.0 environments to meet real-time data needs at scale with substantial energy advantages. $L_{ ext{max}}$ and $T(x)$ are central to the optimization, with $T_u(x)=E_u/\sum_{v\in N_u}\epsilon_{uv}a_{uv}$ guiding lifetime computations and path selection under delay constraints.

Abstract

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Figures (6)

• Figure 1: Industrial edge deployment.
• Figure 2: Toy example of a graph transformation in DDA.
• Figure 3: Experimental setup.
• Figure 4: Initialization measurements.
• Figure 5: Experimental results.

Theorems & Definitions (1)

• Theorem 1