Modeling and Code Generation Framework for IoT

Sharaf, Mohammad; Abusair, Mai; Eleiwi, Rami; Shana'a, Yara; Saleh, Ithar; Muccini, Henry

doi:10.1007/978-3-030-30690-8_6

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…ThingML consists of a modeling language and tools to support code generation. In [11], the authors proposed a code generation framework CAPSml based on the CAPS modeling framework. Through a graphical user interface, the CAPS framework supports the creation of IoT system architectures.…”

Section: Related Studiesmentioning

confidence: 99%

“…The above approaches [10][11][12][13][14][17][18][19][20], as well as IFTTT and Samsung SmartThings, support LCNC code generation in the network domain only. LCNC for the device domain are not addressed by these approaches.…”

Section: Related Studiesmentioning

confidence: 99%

“…Also, as a GUI-based low-code integration approach, IoTtalk minimizes the coding complexity. The developers are not required to have the IoT technical knowledge, and only Most LCNC IoT approaches have focused on code generation in the network domain, which connects the IoT devices and manipulates the data/messages delivered among these devices [10][11][12][13][14][15]. These approaches assume that the software installed in an IoT device (the sensor logic and the driver for communications to the IoT server) already exists.…”

Section: Introductionmentioning

confidence: 99%

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DeviceTalk: A No-Code Low-Code IoT Device Code Generation

Chen

Lin

Yen

et al. 2022

Sensors

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The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeviceTalk: A No-Code Low-Code IoT Device Code Generation

Chen

Lin

Yen

et al. 2022

Sensors

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show abstract

“…Several research approaches have been shown interest in applying ThingML as their modelling or code generation framework. To mention a few, in [22] ThingML has been used to generate code for CAPS, an architecture-driven modelling framework for the development of IoT Systems. In [23] ThingML has been used to specify the behaviour of distributed software components, and later it has been extended with mechanisms to monitor and debug the execution flow of a ThingML program.…”

Section: The Thingml Frameworkmentioning

confidence: 99%

Towards a modeling and analysis environment for industrial IoT systems

Ihirwe,

Di Ruscio,

Mazzini

et al. 2021

Preprint

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The development of Industrial Internet of Things systems (IIoT) requires tools robust enough to cope with the complexity and heterogeneity of such systems, which are supposed to work in safety-critical conditions. The availability of methodologies to support early analysis, verification, and validation is still an open issue in the research community. The early real-time schedulability analysis can help quantify to what extent the desired system's timing performance can eventually be achieved. In this paper, we present CHESSIoT, a model-driven environment to support the design and analysis of industrial IoT systems. CHESSIoT follows a multi-view, component-based modelling approach with a comprehensive way to perform event-based modelling on system components for code generation purposes employing an intermediate ThingML model. To showcase the capability of the extension, we have designed and analysed an Industrial real-time safety use case.

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“…ThingML [34] combines well-proven software-modeling constructs aligned with UML (statecharts and components) and an imperative platform-independent action language to specify IoT applications. Due to its popularity, other languages include a transformation to ThingML [46]. However, modeling of communication in ThingML is rather minimal.…”

Section: Domain-specific Languages For Iiotmentioning

confidence: 99%

Model-driven development of asynchronous message-driven architectures with AsyncAPI

et al. 2021

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In the Internet-of-Things (IoT) vision, everyday objects evolve into cyber-physical systems. The massive use and deployment of these systems has given place to the Industry 4.0 or Industrial IoT (IIoT). Due to its scalability requirements, IIoT architectures are typically distributed and asynchronous. In this scenario, one of the most widely used paradigms is publish/subscribe, where messages are sent and received based on a set of categories or topics. However, these architectures face interoperability challenges. Consistency in message categories and structure is the key to avoid potential losses of information. Ensuring this consistency requires complex data processing logic both on the publisher and the subscriber sides. In this paper, we present our proposal relying on AsyncAPI to automate the design and implementation of these asynchronous architectures using model-driven techniques for the generation of (part of) message-driven infrastructures. Our proposal offers two different ways of designing the architectures: either graphically, by modeling and annotating the messages that are sent among the different IoT devices, or textually, by implementing an editor compliant with the AsyncAPI specification. We have evaluated our proposal by conducting a set of experiments with 25 subjects with different expertise and background. The experiments show that one-third of the subjects were able to design and implement a working architecture in less than an hour without previous knowledge of our proposal, and an additional one-third estimated that they would only need less than two hours in total.

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Modeling and Code Generation Framework for IoT

Cited by 13 publications

References 11 publications

DeviceTalk: A No-Code Low-Code IoT Device Code Generation

DeviceTalk: A No-Code Low-Code IoT Device Code Generation

Towards a modeling and analysis environment for industrial IoT systems

Model-driven development of asynchronous message-driven architectures with AsyncAPI

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