The Internet of Things presents the user with a novel means of communicating with the Web world through ubiquitous object-enabled networks. Cloud Computing enables a convenient, on demand and scalable network access to a shared pool of configurable computing resources. This paper mainly focuses on a common approach to integrate the Internet of Things (IoT) and Cloud Computing under the name of CloudThings architecture. We review the state of the art for integrating Cloud Computing and the Internet of Things. We examine an IoT-enabled smart home scenario to analyze the IoT application requirements. We also propose the CloudThings architecture, a Cloud-based Internet of Things platform which accommodates CloudThings IaaS, PaaS, and SaaS for accelerating IoT application, development, and management. Moreover, we present our progress in developing the CloudThings architecture, followed by a conclusion.

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help the gathering of health data and health-related event detection in real time from the user as well as from their environment. In the larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where the comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. A Smart Mask prototype was developed with accompanying user application, backend and heath AI to study the concept.

We describe ubiGaze, a novel wearable ubiquitous method to augment any real-world object with invisible messages through gaze gestures that lock the message into the object. This enables a context and location dependent messaging service, which users can utilize discreetly and effortlessly. Further, gaze gestures can be used as an authentication method, even when the augmented object is publicly known. We developed a prototype using two wearable devices: a Pupil eye tracker equipped with a scene camera and a Sony Smartwatch 3. The eye tracker follows the users' gaze, the scene camera captures distinct features from the selected real-world object, and the smartwatch provides both input and output modalities for selecting and displaying messages. We describe the concept, design, and implementation of our real-world system. Finally, we discuss research implications and address future work.

Augmented reality (AR) is a promising technology for building applications in an Internet of Things (IoT) environment, utilized for visualizing information provided by IoT devices. In this paper, we enable Web-based mobile AR applications with mobile agents in a resource-oriented IoT system architecture. We present an adaptable mobile agent composition that contains the data representation logic and mappings between AR applications and system resources. Thus, mobile agents and AR application-specific data structures are exposed as global system resources. System resource linkages are considered between real-world objects and their virtual representations for mobile agent-based AR applications. The agent composition also complies with the REST principles for resource access and control system-wide. This allows dynamic runtime adaptation and addressing the device and resource heterogeneity, thus eliminating the need for application-specific communication protocols. Moreover, we utilize a Web-based mobile AR application framework, running completely in a Web browser, which facilitates straightforward AR application development. Lastly, a proof of concept mobile AR application is implemented, where a coffee maker with a visual tag is connected to a low-power resource-constrained wireless sensor network node as an IoT device. A mobile agent is injected into the IoT environment to expose the state changes of the coffee maker. Through the visual tag, AR applications are able to visualize the state changes of the coffee maker in their user interface.