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Abstract-The Internet of Things (IoT) has a great potential to change our lives. Billions of heterogeneous, distributed, intelligent, and sometimes mobile devices, will be connected and offer new types of applications and ways to interact. The dynamic environment of the IoT, the involvement of the human in the loop, and the runtime interactions among devices and applications, put additional requirements on the systems' architecture. In this paper, we use the Emergent Configurations (ECs) concept as a way to engineer IoT systems and propose an architecture for ECs. More specifically, we discuss (i) how connected devices and applications form ECs to achieve users goals and (ii) how applications are run and adapted in response to runtime context changes including, e.g., the sudden unavailability of devices, by exploiting the Smart Meeting Room case.

The international community has largely recognized that the Earth's climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.

The rapid proliferation of the Internet of Things (IoT) is changing the way we live our everyday life and the society in general. New devices get connected to the Internet every day and, similarly, new IoT services and applications exploiting them are developed across a wide range of domains. The IoT environment typically is very dynamic, devices might suddenly become unavailable and new ones might appear. Similarly, users enter and/or leave the IoT environment while being interested in fulfilling their individual needs. These key aspects must be considered while designing and realizing IoT systems. In this paper we propose ECo-IoT, an architectural approach to enable the automated formation and adaptation of Emergent Configurations (ECs) in the IoT. An EC is formed by a set of things, with their services, functionalities, and applications, to realize a user goal. ECs are adapted in response to (un)foreseen context changes e.g., changes in available things or due to changing or evolving user goals. In the paper, we describe: (i) an architecture and a process for realizing ECs; and (ii) a prototype we implemented for (iii) the validation of ECo-IoT through an IoT scenario that we use throughout the paper.

The Internet of Things (IoT) has enabled physical objects and devices, often referred to as things, to connect and communicate. This has opened up for the development of novel types of services that improve the quality of our daily lives. The dynamicity and uncertainty of IoT environments, including the mobility of users and devices, make it hard to foresee at design time available things and services. Further, users should be able to achieve their goals seamlessly in arbitrary environments. To address these challenges, we exploit Artificial Intelligence (AI) to engineer smart IoT systems that can achieve user goals and cope with the dynamicity and uncertainty of their environments. More specifically, the main contribution of this paper is an approach that leverages the notion of Belief-Desire-Intention agents and Machine Learning (ML) techniques to realize Emergent Configurations (ECs) in the IoT. An EC is an IoT system composed of a dynamic set of things that connect and cooperate temporarily to achieve a user goal. The approach enables the distributed formation, enactment, adaptation of ECs, and conflict resolution among them. We present a conceptual model of the entities of the approach, its underlying processes, and the guidelines for using it. Moreover, we report about the simulations conducted to validate the feasibility of the approach and evaluate its scalability.

The Internet of Things (IoT) is a fast-spreading technology that enables new types of services in several domains such as transportation, health, and building automation. To exploit the potential of the IoT effectively, several challenges have to be tackled, including the following ones that we study in this thesis. First, the proposed IoT visions provide a fragmented picture, leading to a lack of consensus about IoT systems and their constituents. To piece together the fragmented picture of IoT systems, we systematically identified their characteristics by analyzing existing taxonomies. More specifically, we identified seventeen characteristics of IoT systems, and grouped them into two categories, namely, elements and quality aspects of IoT systems. Moreover, we conducted a survey to identify the factors that drive the deployment decisions of IoT systems in practice. A second set of challenges concerns the environment of IoT systems that is often dynamic and uncertain. For instance, due to the mobility of users and things, the set of things available in users' environment might change suddenly. Similarly, the status of IoT systems’ deployment topologies (i.e., the deployment nodes and their interconnections) might change abruptly. Moreover, environmental conditions monitored and controlled through IoT devices, such as ambient temperature and oxygen levels, might fluctuate suddenly. The majority of existing approaches to engineer IoT systems rely on predefined processes to achieve users’ goals. Consequently, such systems have significant shortcomings in coping with dynamic and uncertain environments. To address these challenges, we used the concept of Emergent Configurations (ECs) to engineer goal-driven IoT systems. An EC is an IoT system that consists of a dynamic set of things that cooperate temporarily to achieve a user goal. To realize ECs, we proposed an abstract architectural approach, comprising an architecture and processes, as well as six novel approaches that refine the abstract approach. The developed approaches support users to achieve their goals seamlessly in arbitrary environments by enabling the dynamic formation, deployment, enactment, and self-adaptation of IoT systems. The approaches exploit different techniques and focus on different aspects of ECs. Moreover, to better support users in dynamic and uncertain environments, we investigated the automated configuration of those environments based on users' preferences.

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The Internet of Things (IoT) pervades more and more aspects of our lives and often involves many types of smart connected objects and devices. User's IoT environment changes dynamically, e.g., due to the mobility of the user and devices. Users can fully benefit from the IoT only when they can effortlessly interact with it. To accomplish this in a dynamic and heterogenous environment, we make use of Emergent Configurations (ECs), which consist of a set of things that connect and cooperate temporarily through their functionalities, applications, and services, to achieve a user goal. In this paper, we: (i) present the IoT-FED architectural approach to enable the automated formation and enactment of ECs. IoT-FED exploits heterogeneous and independently developed things, IoT services, and applications which are modeled as Domain Objects (DOs), a servicebased formalism. Additionaly, we (ii) discuss the prototype we developed and the experiments run in our IoT lab, for validation purposes.