Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

/pdf/agile-methodologies-applied-to-the-development-of-internet-3blixttq.pdf

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

The rapid advancement of machine learning (ML) and artificial intelligence (AI) has created an increasing demand for efficient and automated processes in training and deploying AI models. In cloud environments, where vast computational resources are available, orchestrating the entire lifecycle of machine learning workflows becomes crucial to leverage the scalability and flexibility offered by the cloud infrastructure. This research study proposes a novel system architecture and simulation model for machine learning orchestration in cloud environments, aiming to automate the training and deployment of using Distributed Machine Learning (DML) AI model. The proposed system architecture consists of three key components: Job Manager, Resource Manager, and Model Repository. The Job Manager handles the scheduling and coordination of machine learning tasks, ensuring efficient resource allocation and utilization. The Resource Manager dynamically manages the allocation and provisioning of computing resources based on workload demands. The Model Repository acts as a centralized repository for storing and versioning AI models, enabling seamless model deployment and updates. To evaluate the effectiveness and performance of the proposed system architecture, a simulation model is developed. The simulation model provides a virtual environment that mimics real-world cloud scenarios, allowing for extensive experimentation and analysis. Various performance metrics such as training time, resource utilization, and scalability are measured and compared against baseline approaches to demonstrate the superiority of the proposed system architecture. The simulation results indicate that the machine learning orchestration system in the cloud environment significantly improves the efficiency and automation of training and deploying Distributed Machine Learning (DML) AI model. The proposed architecture optimizes resource allocation, minimizes training time, and enhances scalability, leading to cost savings and increased productivity. Moreover, the simulation model provides valuable insights into the behaviour and performance of the system under different workload scenarios, facilitating the fine-tuning and optimization of the orchestration process.

Machine Learning Orchestration in Cloud Environments: Automating the Training and Deployment of Distributed Machine Learning AI Model

The concept of the cloud-to-thing continuum addresses advancements made possible by the widespread adoption of cloud, edge, and IoT resources. It opens the possibility of combining classical symbolic AI with advanced machine learning approaches in a meaningful way. In this paper, we present a thing registry and an agent-based orchestration framework, which we combine to support semantic orchestration of IoT use cases across several federated cloud environments. We use the concept of virtual sensors based on machine learning (ML) services as abstraction, mediating between the instance level and the semantic level. We present examples of virtual sensors based on ML models for activity recognition and describe an approach to remedy the problem of missing or scarce training data. We illustrate the approach with a use case from an assisted living scenario.

Orchestrating Heterogeneous Devices and AI Services as Virtual Sensors for Secure Cloud-Based IoT Applications.

As households are equipped with smart meters, supervised Machine Learning (ML) models and especially Non-Intrusive Load Monitoring (NILM) disaggregation algorithms are becoming increasingly important. To be robust, these models require a large amount of data, which is difficult to collect. Consequently, the generation of meaningful synthetic data is becoming more relevant. We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability. We demonstrate that the method of data generation is crucial to train ML models in a meaningful way. The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability. The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available. Since we observed that NILM models can provide unstable results, we develop a reliable evaluation methodology, based on Cochran's sample size. Finally, we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data. 

Generation of meaningful synthetic sensor data - evaluated with a reliable transferability methodology

Recently, an increasing number of Artificial Intelligence services have been developed for a variety of domains. Machine Learning and especially Deep Learning services require a large amount of data to provide their functionality. Since data collection is typically complex and difficult, there is often not enough data available. Machine learning services such as anomaly detection or disaggregation algorithms are also being developed in the smart living domain. In practice, however, only a few energy datasets are publicly available, as the collection of such data is expensive and time-consuming due to the equipment required. One way to generate more smart meter data is to use a simulation. Developing such a simulation that is capable of generating meaningful data is a complex task. Therefore, in this paper, we present the Synthetic Time Series Data Generator (SynTiSeD), a multi-agent-based simulation tool that generates meaningful synthetic energy data based on real-world data. Furthermore, SynTiSeD allows generating data of critical situations, which are important for the development of such services, but which cannot be provoked in the real world. For transferability, we demonstrate that Nonintrusive Load Monitoring algorithms trained on synthetic data generated by SynTiSeD provide meaningful results that are even better than those of models trained on real data. 

SynTiSeD – Synthetic Time Series Data Generator

The concept of the cloud-to-things continuum addresses advancements made possible by the widespread adoption of cloud, edge, and IoT resources. It opens the possibility to combine classical symbolic AI and advanced machine learning approaches in a meaningful way. In this paper we present a thing registry and an agent-based orchestration framework which we combine to support semantic orchestration of IoT use cases across several federated cloud environments. We illustrate the approach with a use case from an assisted living scenario.

From Things into Clouds – and back

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Orchestrating Heterogeneous Devices and AI Services as Virtual Sensors for Secure Cloud-Based IoT Applications.

From Things into Clouds – and back