Low-Code Internet of Things Application Development for Edge Analytics

Lecture Notes in Computer Science

John

et al. 2022

The increasing complexity delivered by the heterogeneity of the cyber-physical systems is being addressed and decoded by edge technologies, IoT development, robotics, digital twin engineering, and AI. Nevertheless, tackling the orchestration of these complex ecosystems has become a challenging problem. Specially the inherent entanglement of the different emerging technologies makes it hard to maintain and scale such ecosystems. In this context, the usage of model-driven engineering as a more abstract form of glue-code, replacing the boilerplate fashion, has improved the software development lifecycle, democratising the access to and use of the aforementioned technologies. In this paper, we present a practical use case in the context of Smart Manufacturing, where we use several platforms as providers of a high-level abstraction layer, as well as security measures, allowing a more efficient system construction and interoperability.

Section: The It Ecosystem: Tools and Technologiesmentioning

confidence: 99%

Model-Driven Engineering in Digital Thread Platforms: A Practical Use Case and Future Challenges

Chaudhary

Lecture Notes in Computer Science

John

et al. 2022

“…Our providing a model-driven approach for edge analytics using two main tools, 1) EdgeX and 2) FiWARE, builds upon previous work already done with DIME and EdgeX [4,5], where we retrieved data from IoT devices using REST API. Both EdgeX and FiWARE technologies provide many functionalities to handle data from the Edge.…”

Section: Towards Model-driven Edge Analytics With Iot Agents and Ekuipermentioning

confidence: 99%

Model-Driven Edge Analytics: Practical Use Cases in Smart Manufacturing

Lecture Notes in Computer Science

Chaudhary

Margaria

2022

In the Internet of Things (IoT) era, devices and systems generate enormous amounts of real-time data, and demand real-time analytics in an uninterrupted manner. The typical solution, a cloud-centred architecture providing an analytics service, cannot guarantee real-time responsiveness because of unpredictable workloads and network congestion. Recently, edge computing has been proposed as a solution to reduce latency in critical systems. For computation processing and analytics on edge, the challenges include handling the heterogeneity of devices and data, and achieving processing on the edge in order to reduce the amount of data transmitted over the network.In this paper, we show how low-code, model-driven approaches benefit a Digital Platform for Edge analytics. The first solution uses EdgeX, an IIoT framework for supporting heterogeneous architectures with the eKuiper rule-based engine. The engine schedules fully automatically tasks that retrieve data from the Edge, as the infrastructure near the data is generated, allowing us to create a continuous flow of information. The second solution uses FiWARE, an IIoT framework used in industry, using IoT agents to accomplish a pipeline for edge analytics. In our architecture, based on the DIME LC/NC Integrated Modelling Environment, both integrations of EdgeX/eKuyper and FiWARE happen by adding an External Native DSL to this Digital Platform. The DSL comprises a family of reusable Service-Independent Building blocks (SIBs), which are the essential modelling entities and (service) execution capabilities in the architecture’s modelling layer. They provide users with capabilities to connect, control and organise devices and components, and develop custom workflows in a simple drag and drop manner.

“…For example, we enabled the remote programming and configuration of a cobot arm (collaborative robot) [24] through an intuitive web application that communicates with and sends commands to the robot through a Domain Specific Language based on the robot's native API. In [25], we established a novel way to develop and deploy IoT applications through Model-Driven Development approaches. Creating the respective Domain Specific Language, we used the DIME and Pyrus application development platforms to develop an application that connects a range of heterogeneous technologies, including sensors using the EdgeX middleware platform [26] and a data analytics pipeline.…”

mentioning

confidence: 99%

MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts

Margaria

2023

Electronics

In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.