[Paper Review] Low-code Engineering for Internet of things: A state of research

Felicien Ihirwe, Davide Di Ruscio+3 more|arXiv (Cornell University)|Oct 19, 2020

IoT and Edge/Fog Computing26 references71 citations

TL;DR

This paper presents a state-of-the-art analysis of 16 low-code and model-driven engineering (MDE) platforms for IoT systems, identifying key features, limitations, and research gaps. It proposes a taxonomy based on feature modeling and highlights critical shortcomings such as lack of standards, limited multi-view modeling, weak testing support, and insufficient cloud-based MDE support, aiming to guide future development of secure, interoperable low-code IoT platforms.

ABSTRACT

Developing Internet of Things (IoT) systems has to cope with several challenges mainly because of the heterogeneity of the involved sub-systems and components. With the aim of conceiving languages and tools supporting the development of IoT systems, this paper presents the results of the study, which has been conducted to understand the current state of the art of existing platforms, and in particular low-code ones, for developing IoT systems. By analyzing sixteen platforms, a corresponding set of features has been identified to represent the functionalities and the services that each analyzed platform can support. We also identify the limitations of already existing approaches and discuss possible ways to improve and address them in the future.

Motivation & Objective

• To assess the current state of the art in low-code and model-driven engineering (MDE) platforms for IoT system development.
• To identify common features, capabilities, and limitations across existing IoT development platforms.
• To highlight critical gaps in standards, multi-view modeling, testing support, and cloud-based MDE support.
• To guide future research by proposing improvements for secure, interoperable, and extensible low-code IoT platforms.
• To support the Lowcomote project’s goal of advancing low-code engineering for trustworthy IoT systems.

Proposed method

• Conducted a manual literature search on Google Scholar using keywords related to MDE and IoT.
• Selected 16 platforms based on criteria: availability of tools, post-2010 publication, use of MDE, and at least three scientific citations.
• Developed a taxonomy of 16 features to characterize platform capabilities, formalized as a feature diagram.
• Classified platforms into two categories: Eclipse-based (EMF, GMF, Papyrus) and tailor-made low-code platforms.
• Analyzed platforms based on the taxonomy to evaluate support for requirements, modeling, deployment, testing, and cloud integration.
• Identified limitations through comparative analysis and mapped them to future research directions.

Experimental results

Research questions

• RQ1 What are the key features and capabilities of existing low-code and MDE platforms for IoT system development?
• RQ2 How do current platforms support multi-view modeling, requirements specification, and system analysis before deployment?
• RQ3 What are the main limitations in standards, interoperability, cloud-based model management, and testing support across these platforms?
• RQ4 How do Eclipse-based platforms differ from tailor-made low-code platforms in terms of deployment, extensibility, and engineering support?
• RQ5 What improvements are needed to enable secure, trustworthy, and interoperable low-code IoT development platforms?

Key findings

• A significant lack of support for requirement specification is observed, with only SysML4IoT and FRASAD providing formalized requirements modeling at the PIM level.
• Most platforms offer limited or no analysis support, which hinders early verification of system behavior before deployment.
• While Eclipse-based tools are predominantly deployed locally, all low-code platforms (LCDPs) are cloud-based, with only a few supporting on-premise deployment.
• Only a minority of platforms—CAPS, MED4IoT, Atmospheric IoT, and Mendix—support multi-view modeling, which is essential for separation of concerns.
• There is a notable absence of standardized meta-modeling approaches, leading to poor interoperability between platforms despite the existence of reference models like ITU-T Y.2060.
• Testing and analysis capabilities are underdeveloped across platforms, representing a major challenge for ensuring responsiveness and correctness in complex, heterogeneous IoT systems.