iPaaS in Agriculture 4.0: An Industrial Case
This addresses the need for customizable and affordable automation solutions in agriculture and other industries, though it is incremental as it builds on existing open-source tools.
The paper tackles the lack of adaptable and cost-effective Integration Platforms as a Service (iPaaS) for industrial automation by proposing a generic open-source iPaaS architecture, demonstrating in a case study on grain storage that it can automate tasks currently done manually.
Current automation approaches in the Industry 4.0 have generated increased interest in the utilization of Integration Platforms as a Service (iPaaS) cloud architectures in order to unify and synchronize several systems, applications, and services in order to build smart solutions for automated and adaptive industrial process management. Existing iPaaS solutions present several out-of-the-box connectors and automation engines for easier integration of customers' projects, but show issues regarding overall adaptation outside their scope, brand locking, and occasionally high prices. Moreover, existing platforms fail to respond adequately to the needs of deploying multiple decision models capable of offering automated or semi-automated management of processes, thanks to the integration of the large diversity of data and event sources as well as the different physical or logical action entities. With the popularization of open-source software and applications such as BPM Engines, Machine Learning libraries, and Integration suites and libraries, it is possible to develop a fully customizable and adaptable, open-source iPaaS that can be used both in and outside industrial applications. In this paper, we propose a generic iPaaS architecture implemented on the basis of several open source solutions boasting integration, interoperability, and automated decision-making capabilities in the domain of Agriculture 4.0. A proof-of-concept based on these solutions is presented, as well as a case study on MA{Ï}SADOUR's grain storage process with a comparison with the currently human-operated tasks.