The Need for Model-driven Engineering in the Development of IoT Software for Public Transportation Systems

The integration of domain-specific concepts in a model-driven engineering (MDE) approach raises a number of interesting research questions. There are two possibilities to represent these concepts. The first one focuses on models that contain domain-specific concepts only, i.e. domain-specific modelling languages (DSML). The second one advocates the integration of domain-specific concepts in general-purpose models, using what we will refer to in this paper as domain-specific modelling annotation languages (DSMAL). In this position paper, we argue that each approach is particularly suited for specific activities and specific actors, and show how they can be developed and used together. We also highlight the challenges created by the use of two representations, such as the evaluation of models OCL constraints and the synchronisation between the two representations. As an illustration, we present rbacUML, our approach for integrating role-based access control (RBAC) concepts into an MDE approach.

Hardware-in-the-loop (HIL) testing is an integral part of the development of power system automation solutions. HIL test beds facilitate system-level testing and validation of controllers against their specifications. The information required for HIL test bed configurations is typically scattered across different tools used in the development of power system automation solutions. These tools often have different formats and naming schemes. This implies that a significant effort is required for consolidating data from the tools and for creating valid test configurations from the consolidated data. This paper presents a Model-Driven Engineering (MDE) approach for creating HIL test configurations in a tool-, platform-, and protocol-agnostic manner. It shows how the MDE approach can be applied in practice to derive test configurations based on the information originating from different tools in a semi-automated fashion.

Defining low power design intent for a system-on-chip (SoC) consists in specifying its power management architecture and strategy according to specific low power techniques such as power gating and multi-voltage scaling requirements. Choosing the most-energy efficient power intent for a final system contributes widely to reduce its overall power consumption. At Transaction-Level, a rapid exploration of different power intent alternatives can be made. In this paper, we present a Model Driven Engineering (MDE) approach to automate low power design intent specifications and accelerate Low Power Design Intent Space Exploration (LPDISE) using a Transaction-Level power-aware design methodology. This MDE approach mainly relies on a high level abstraction of the Unified Power Format (UPF) standard concepts that fit a TLM approach use. Then, the MDE approach is applied to automatically generate a UPF code defining the most energy-efficient power intent and being a reference file for Register Transfer Level (RTL) design team. This task focuses on a smart deduction of adequate UPF commands from the high level abstraction semantics. The effectiveness of the proposed MDE approach is illustrated by an example.

Nowadays, Web Applications (WAs) are complex software sys- tems, used by multiple users with different roles and often devel- oped to support and manage business processes. Due to the chang- ing nature of the supported processes, WAs need to be easily and quickly modified, to adapt and align them to the processes they support. In recent years, Model Driven Engineering (MDE) ap- proaches have been proposed and used to develop and evolveWAs. However, the definition of appropriate MDE approaches for the de- velopment of flexible process-centric WAs is still limited. In par- ticular, (flexible) workflow models have never been integrated with the models (e.g., presentation, information models) used in MDE approaches to develop this type of applications. In this paper, we present M3D (Model Driven Development with Declare), a tool for developingWAs that integrates three MDE metamodels used to represent the main components of aWA with the metamodel of De- clare, a declarative language to model business processes. The tool exploits and combines the declarative nature of Declare and the ad- vantages of MDE to get an efficient roundtrip engineering support to develop and evolve flexible process-centric WAs.

Today's Web Applications (WAs) are complex multi-user and multi-tenant software systems, used by users with different roles and often developed to support and manage complex business processes. Due to the changing nature of such processes, WAs need to be easily and quickly modified, to be adapted and aligned to the processes they support. In recent years, Model Driven Engineering (MDE) approaches have been used to support the development and the evolution of WAs. However, the definition of appropriate MDE approaches for the development of flexible process-centric WAs is still limited. In particular, flexible workflow models have never been integrated with the models used in MDE approaches to develop this type of applications. This paper proposes an MDE approach for the development and the evolution of flexible process-centric WAs that integrates four MDE metamodels used to represent the main components of a WA with the metamodel of Declare, a declarative language to model business processes. A case study, carried out to validate the feasibility, the correctness and the effectiveness of the proposed approach is presented and discussed.

During the last decade, Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories and Eclipse Modeling Framework (EMF) are among the most representatives MDE approaches. Nowadays, it is well recognized that model transformation is at the heart of MDE approaches and, consequently represents one of the most important operations in MDE. However, despite the multitude of model transformation language proposals emerging from academic world and industry, these transformations are often manually specified; which is a tedious and error-prone task, and therefore an expensive process. Matching operation between metamodels is the keystone toward a (semi-)automatic transformation process. In this paper, the authors review metamodel matching techniques of the literature and then analyze their pros and cons in order to show how they can be useful for a semi-automatic transformation process. The result is a comparison of metamodel matching techniques, highlighting their similarities and differences in terms of information used for matching, demonstrating significant similarities between these techniques. Next, the authors compare four well-known metamodel matching techniques namely Similarity flooding, SAMT4MDE+ (extended Semi-Automatic Matching Tool for Model Driven Engineering), ModelCVS and AML (AtlanMod Matching Language) on ten couples of metamodels. For this comparison, the authors define a set of six criteria inspired from the database schema matching. One among these criteria is relevant to the quality of matching and for which we define a quality measure metrics. Furthermore, the authors develop a plug-in under Eclipse to support our comparison using ten couples of metamodels.

SummaryWith the development of Internet of Things (IoT)‐related technologies and artificial intelligence (AI) technologies, various IoT services are becoming more intelligent, and their use range is increasing and diversifying. IoT hardware and IoT software must support AI‐related functions to provide an intelligent IoT service. In general, IoT devices powered by batteries have limited computing performance when compared to general computing environments. Therefore, it is essential to provide AI‐related functions at low power in IoT devices to implement and offer various intelligent services. Neuromorphic computing devices or neuromorphic computing architectures can operate with low power energy consumption. If applied to IoT devices, AI‐related functions can be implemented in a resource‐constrained IoT device environment. The proposed neuromorphic architecture abstraction (NAA) model dynamically selects the proper neuromorphic architecture by comparing the parameter size of a given SNN model. It also considers the specifications and error probability of the available neuromorphic architecture. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model and dynamic selection scheme can reduce the execution time for training and inferencing. It reduces the training and inferencing time of a given model compared with the method of randomly specifying the neuromorphic architecture.

An activity-based approach for the early identification and resolution of problems in the development of IoT systems in academic projects

Model-Driven Engineering (MDE) is changing the way we build, operate, and maintain our software-intensive systems. Several projects using MDE practices are reporting significant improvements in quality and performance but, to be able to handle these projects, software engineers need a set of technical and interpersonal skills that are currently not widely available. The MDE postgraduate diploma intends to fill this gap by offering a full-time one year formation onMDE. The course syllabus is designed to teach students how to work at a higher abstraction level by the rigorous use of (software) models as the main artifacts in all software engineering activities. Contents include the conceptual framework of MDE plus all techniques and tools (e.g. for defining modeling languages, models, and model transformations) required to successfully complete software engineering projects following a MDE approach. The organizational impact (and challenges) of adopting MDE are exemplified using real industrial experiences. This article describes the MDE Diploma and the lessons that we learned after completing its first edition.

The Internet of Things (IoT) contributes to the advancement and optimization of systems in different contexts by reducing human effort, cost, and time. It is estimated that by 2020 there will be more than 20 billion connected IoT devices. Though justified by emerging IoT applications, such growth requires the implementation of more robust, and consequently, more complex IoT systems. Hence, the optimization of IoT hardware and software design is fundamental towards achieving scalability. It allows for the control and capture of increased data from more connected devices. For this purpose, this work analyzes and compares the scalability of three architectures for IoT devices abstraction of IoT management platforms, and for this, two experiments were carried out. The results show that the Architecture NxN has made better use of computing resources, obtained the average latency value close to zero and connected 100,000 physical emulated devices. Also, being the most optimized architecture for IoT device abstraction of IoT management platforms, in the context of big data, it works in real time.

Internet of Things (IoT) is a rapidly growing field of great interest to students and educators. However, designing and teaching a project-based IoT course can be challenging. While some textbooks and courses are already available in IoT, limited information is available on how to successfully pair the IoT curriculum with hardware and hands-on projects. More work and open discussion are needed to develop courses which successfully and effectively combine hardware and software. In the present work, we summarize how we designed and taught a graduate level IoT course at California State University, East Bay (CSUEB) by effectively pairing lectures with hands-on labs and projects. The course provided a broad introduction to IoT topics, software, and hardware through the use of hands-on projects and low-cost hardware kits (≈$544 each). By strategically combining lectures with hands-on practice, we successfully introduced computer science students to key IoT and hardware skills such as assembling devices, debugging issues, reviewing specs and data, and optimizing performance. We also share the equipment and software packages used, topics covered, labs and projects given, and lessons learned. This course was favorably reviewed by students, led to several successful capstone projects, and even helped one student to secure an internship in IoT. We anticipate that these project-based approaches to teaching and learning IoT will be beneficial to many students and educators who are interested in the IoT field.

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.

The proliferation of autonomous Internet of Things (IoT) systems powered by deep learning and artificial intelligence has ushered in a new era of data-driven convenience and automation. However, this innovation comes hand in hand with heightened concerns regarding data privacy. This paper presents a comprehensive framework for Privacy Assurance in Autonomous IoT Systems (PAIS), which amalgamates cutting-edge technologies and best practices to safeguard individual privacy in the era of pervasive connectivity and autonomous decision-making. The PAIS framework comprises multifaceted strategies to address privacy challenges in autonomous IoT ecosystems. It leverages advanced encryption techniques, robust access control mechanisms, and anonymization protocols to ensure data confidentiality. Moreover, differential privacy mechanisms are deployed to protect the identities of individuals within data streams. An innovative aspect of PAIS is the integration of AI-driven privacy monitoring, which constantly evaluates data for potential breaches and triggers immediate responses when anomalies are detected. Ensuring regulatory compliance is a paramount facet of the PAIS framework, as it aligns with evolving data protection regulations globally. Users are afforded control and transparency through intuitive interfaces, enabling them to manage their data usage preferences effectively. The ethical implications of AI in privacy preservation are also examined within the framework, emphasizing the importance of fairness and bias mitigation. PAIS promotes a privacy-by-design approach, where privacy considerations are integral to the inception and development of IoT systems. Regular risk assessments are performed to identify potential privacy vulnerabilities, ensuring that the framework adapts to emerging threats. Education and training programs are provided to stakeholders to foster awareness and adherence to privacy best practices.

BRCode: An interpretive model-driven engineering approach for enterprise applications

Model Driven Engineering (MDE) is a software development approach family based on the use of models in the software construction. It allows the exploitation of models to simulate, estimate, understand, communicate and produce code.Mobile agents are a very interesting technology to develop applications for mobile and distributed environments. A mobile agent is essentially a computer program that acts autonomously on behalf of a user and travels through a network of heterogeneous computers. By moving to remote computers, the mobile agent processes data there and hence saves remote communications and local computation.Many mobile-agent platforms have been developed. While some of them have been abandoned, others continue to release new versions that fix bugs detected or offer new interesting features.In this paper, we present a reflection on how one can realize the migration operation when the mobile-agent applications are built using an MDE approach.