FTG+PM for the Model-Driven Development of Wireless Sensor Network based IoT Systems

Karaduman, Burak; Mustafiz, Sadaf; Challenger, Moharram

doi:10.1109/models-c53483.2021.00052

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…In [18], the authors developed IoT network applications based on the formalism transformation graph (FTG) process model (PM) approach and described the model-driven engineering (MDE) process of developing applications for different platforms or operating systems. FTG + PM tackles the complexity of multi-paradigm systems using MDE to improve the usability, precision and automation of these systems.…”

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%

“…DeviceTalk is an extension of our previous work, IoTtalk [2]. Following the MDE process approach [10,14,15,18,20,21], IoTtalk is an IDE environment similar to [17] The mapping is established by the join links in Figure 1, creating the network application following the LCNC approach described in Section 1.…”

Section: The Devicetalk Architecturementioning

confidence: 99%

“…DeviceTalk is an extension of our previous work, IoTtalk [2]. Following the MDE process approach [10,14,15,18,20,21], IoTtalk is an IDE environment similar to [17]. IoTtalk defines an abstract model, 𝑑, for the same type of IoT devices.…”

Section: The Devicetalk Architecturementioning

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: The Devicetalk Architecturementioning

confidence: 99%

Section: The Devicetalk Architecturementioning

confidence: 99%

See 2 more Smart Citations

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

Chen

Lin

Yen

et al. 2022

Sensors

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“…Thus, some proposals to deal with this complexity are available [25] proposed FRASAD, a model-driven software development framework to manage the complexity of IoT applications. [26] proposed another approach to deal with this complexity. Their approach includes activities such as requirements development, domain-specific design, verification, simulation, analysis, calibration, deployment, code generation, and execution.…”

Section: Related Workmentioning

confidence: 99%

Semiotics: An Approach to Model Security Scenarios for IoT-Based Agriculture Software

Hurtado,

Antonelli,

López

et al. 2024

TecnoL.

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Agriculture is a vital human activity that contributes to sustainable development. A few decades ago, the agricultural sector adopted the Internet of Things (IoT), which has played a relevant role in precision and smart farming. The IoT developments in agriculture require that numerous connected devices work cooperatively. This increases the vulnerability of IoT devices, mainly because they lack the necessary built-in security because of their context and computational capacity. Other security threats to these devices are related to data storage and processing connected to edge or cloud servers. To ensure that IoT-based solutions meet functional and non-functional requirements, particularly those concerning security, software companies should adopt a security-focused approach to their software requirements specification. This paper proposes a method for specifying security scenarios, integrating requirements and architecture viewpoints into the context of IoT for agricultural solutions. The method comprises four steps: (i) describe scenarios for the intended software, (ii) describe scenarios with incorrect uses of the system, (iii) translate these scenarios into security scenarios using a set of rules, and (iv) improve the security scenarios. This paper also describes a prototype application that employs the proposed algorithm to strengthen the incorrect use scenario based on the correct use scenario. Then, the expert can complete the information for the analysis and subsequent derivation of the security scenario. In addition, this paper describes a preliminary validation of our approach. The results show that the proposed approach enables software engineers to define and analyze security scenarios in the IoT and agricultural contexts with good results. A survey administered to five security experts found that the proposed security scenario method is generally useful for specifying agricultural IoT solutions but needs improvement in different areas.

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