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The Internet of Things (IoT) requires scalability, extensibility and a transparent integration of multi-technology in order to reach an efficient support for global communications, discovery and look-up, as well as access to services and information. To achieve these goals, it is necessary to enable a homogenous and seamless machine-to-machine (M2M) communication mechanism allowing global access to devices, sensors and smart objects. In this respect, the proposed answer to these technological requirements is called Glowbal IP, which is based on a homogeneous access to the devices/sensors offered by the IPv6 addressing and core network. Glowbal IP's main advantages with regard to 6LoWPAN/IPv6 are not only that it presents a low overhead to reach a higher performance on a regular basis, but also that it determines the session and identifies global access by means of a session layer defined over the application layer. Technologies without any native support for IP are thereby adaptable to IP e.g. IEEE 802.15.4 and Bluetooth Low Energy. This extension towards the IPv6 network opens access to the features and methods of the devices through a homogenous access based on WebServices (e.g. RESTFul/CoAP). In addition to this, Glowbal IP offers global interoperability among the different devices, and interoperability with external servers and users applications. All in all, it allows the storage of information related to the devices in the network through the extension of the Domain Name System (DNS) from the IPv6 core network, by adding the Service Directory extension (DNS-SD) to store information about the sensors, their properties and functionality. A step forward in network-based information systems is thereby reached, allowing a homogenous discovery, and access to the devices from the IoT. Thus, the IoT capabilities are exploited by allowing an easier and more transparent integration of the end users applications with sensors for the future evaluations and use cases. A.J. Jara et al. / Glowbal IP: An adaptive and transparent IPv6 integration in the IoT factors, and can even include clinical devices in e-Health and solutions for Ambient Assisted Living environments [7].Flexibility, ubiquity and scalability are properties found in the current Internet, and that is why the aforementioned challenges can be solved not only with the new capabilities to link Internet with everyday sensors and devices, but also with the exploitation of data captured from the Future Internet through the so-called Internet of Things (IoT).Future Internet and the IoT represent unprecedented growth in the number of devices and users connected to the Internet. Therefore, the devices should be as autonomous as possible in satisfying the so-called 'self-* functionalities', such as self-management, self-healing, and self-discovery. These properties are especially challenging in the Internet of Things, where many devices are mobile and, consequently, can change their location in the network.For that reason, this research is focused on operating on top ...

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

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Internet of Things (IoT) is presenting an enormous growing, in numbers, it is estimated that over 50 billion of devices will be connected to Internet by 2020. Therefore, it presents a high scalability requirement to manage every resource connected to the network. Therefore, It is required a high capability for autonomous registration and discovery of resources and services. In addition, it should be dynamically adapted with the inclusion of new devices in the network and changes of the existing ones. Nowadays, the most extended discovery architecture for the Internet is the Domain Name Systems (DNS), which is offering through the extensions multicast DNS (mDNS) and DNS Service Directory (DNS-SD) the query and discovery of services by type and properties. It has been already carried out some initial works on mDNS and DNS-SD for the discovery of things. Thereby, it can satisfy the discovery of resources from the IoT point of view, and discovery of services, i.e. WebServices such as CoAP from the Web of Things point of view. But, it has not been yet analyzed the impact of DNS for Smart Objects, since it cannot be directly applied, because these protocols are designed for hostbased requirements, where they are not taking into account the design issues and constraints from the Smart Objects. For that reason, this paper analyzes the requirements and design issues to apply these discovery techniques in Smart Objects, carries out an overview of the satisfaction of them in the initial solutions for IoT, in order to finally offer an evaluation of different ways to apply mDNS and DNS-SD for Smart Objects, concluding with a set of recommendations and lessons learned to build a lightweight implementation of mDNS and DNS-SD for resource discovery and directory.