Manually configuring Internet of Things (IoT) work-flows often involves intricate and time-consuming processes. This complexity can lead to an increase in development time and difficulty in managing and scaling IoT systems. The challenge lies in creating efficient and deployable workflows that seamlessly integrate diverse IoT devices and services. To address these challenges, we propose an approach that leverages IoT system orchestration, workflow development, and Large Language Models (LLMs) to convert high-level natural language system descriptions into executable workflows. Utilizing the Web of Things (WoT) framework, Node-RED and LLMs, we enabled the automatic generation of workflow components from natural language descriptions, enhancing the accessibility and usability of IoT system development for non-experts. We describe an assessment framework for the evaluation of abstract workflow descriptions and use this to analyze the implementation of our approach over systems within various domains, comparing the automatically generated workflows with those produced manu-ally. Our approach provides a powerful and versatile solution for IoT system development. This approach not only simplifies the process, but also provides a foundation for future innovations in the IoT landscape, paving the way for more adaptable and user-friendly IoT solutions.

A distributed system’s functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate systems of systems (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity of systems and enabling the programmatic construction of SoSs with minimal developer intervention. We propose a general ontology-based approach to describe distributed systems, seeking to achieve abstraction and enable runtime reasoning between systems. We also propose an architecture for systems that utilizes such ontologies to enable systems to discover and ‘understand’ each other, and potentially compose, all at runtime. We detail features of the ontology and the architecture through three contrasting case studies: one on controlling multiple systems in smart home environment, another on the management of dynamic computing clusters, and a third on autonomic connection of rescue teams. We also quantitatively evaluate the scalability and validity of our approach through experiments and simulations. Our approach enables system developers to focus on high-level SoS composition without being constrained by deployment-specific implementation details. We demonstrate the feasibility of our approach to raise the level of abstraction of SoS construction through reasoned composition at runtime. Our architecture presents a strong foundation for further work due to its generality and extensibility.

The amount of data generated in Industry 4.0 and the introduction of advanced data analytics support establishing “smart factories” and one of its crucial characteristics - predictive maintenance. Current solutions primarily focus on offline predictions and do not provide end-to-end scalable solutions. Furthermore, there is a lack of support for incremental ma-chine learning in predictive maintenance. This paper addresses these limitations by proposing MARTIN, a scalable microservice architecture for predictive maintenance that can collect, store, and analyse data, and make decisions based on the machine state. The architecture uses incremental learning as the basis for predictions. The designed system was implemented and its performance was evaluated experimentally. The results show that the solution can provide high prediction accuracy in terms of practical processing time.

With the current rapid development of Internet of Things (IoT) technology and the widespread popularity of smart home devices, wireless technology has made it possible for IoT devices to integrate with each other in a complex system. Previous works have proposed utilizing ontological descriptions, called Holons, of IoT devices and subsystems to reason about the construction of systems. The holonic description, defined by an ontology, includes parameters, services, and properties of the IoT device. However, these previous works assume that Holon descriptions of IoT devices are already provided e.g., by vendors. This assumption requires device vendors and system engineers to manually create descriptions, which is time-consuming and error-prone given the increasing number of IoT devices that are offered in the market. This paper introduces a method for the automatic generation of Holon descriptions of IoT devices. This method uses the brand and model of a device and utilizes knowledge extraction in natural language processing to automatically generate the ontological description of the IoT device. The experimental results show that the proposed method can generate descriptions with a precision of 96.72% and a recall of 87.53% in a practically acceptable time.

Self-awareness is a crucial capability of autonomous service-based systems that enables them to self-adapt. There are different types of self-awareness whereby certain types of knowledge are captured at various levels. We argue that effective management of the trade-offs of dependability requirements can be achieved through “seamless” switching between different levels of awareness. However, the assessment of the quality of knowledge to enable dynamic switching between self-awareness levels has not been tackled yet. We propose a general architecture that exploits symbiotic simulation in order to tackle the complexity of assessing the quality of knowledge and attaining the meta-self-awareness property, wherein the system can reflect on its different levels of awareness. We conduct a thorough real-world study in the context of volunteer services. We conclude that a system made meta-self-aware using our approach achieves optimal performance by activating the most suitable awareness level. This comes at the cost of a modest computational overhead.

The diversity of cloud offerings motivated the proposition of cloud modelling languages (CMLs) to abstract complexities related to selection of cloud services. However, current CMLs lack the support for modelling service level objectives (SLOs) that are required for the customer applications. Consequently, we propose an application- and provider-independent SLO modelling language (SLO-ML) to enable customers to specify the required SLOs. We also sketch the architecture to realise SLO-ML.

Background : Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments. Objective : We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems. Method : We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in software engineering and explore how self-awareness is engineered and incorporated in software systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others. Results : Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. Though there have been recent attempts to define and engineer self-awareness in software engineering, there is no consensus on the definition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated. Conclusions : Our survey reveals that self-awareness for software systems is still a formative field and that there is growing attention to incorporate self-awareness for better reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems outline possible research directions.

Engineering volunteer services calls for novel self-adaptive approaches for dynamically managing the process of selecting volunteer services. As these services tend to be published and withdrawn without restrictions, uncertainties, dynamisms and 'dilution of control' related to the decisions of selection and composition are complex problems. These services tend to exhibit periodic performance patterns, which are often repeated over a certain time period. Consequently, the awareness of such periodic patterns enables the prediction of the services performance leading to better adaptation. In this paper, we contribute to a self-adaptive approach, namely time-awareness, which combines self-aware principles with dynamic histograms to dynamically manage the periodic trends of services performance and their evolution trends. Such knowledge can inform the adaptation decisions, leading to increase in the precision of selecting and composing services. We evaluate the approach using a volunteer storage composition scenario. The evaluation results show the advantages of dynamic knowledge management in self-adaptive volunteer computing in selecting dependable services and satisfying higher number of requests.

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus- and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.