Vangelis Gazis

Over the last decade, there has been a growing research interest in machine-to-machine communications and the Internet of Things (IoT) as a milestone embodiment of the continuous convergence between the physical facet of human activities and its reflection on the information world. IoT is understood as the (r)evolutionary transition into an era where physical assets and virtual assets will be treated uniformly and, for all intents and purposes, be largely indistinguishable to the processes involving them. The sheer scale of IoT suggests that harmonized global standards will be paramount in realizing a seamless treatment across the physical facet and the virtual facet of things. In this paper, we survey the-;admittedly babelian-standardization landscape of IoT and present its major efforts. Our survey concludes with a comparative discussion that highlights the key challenges of future standardization in IoT.

The Internet of Things (IoT) brings together a multitude of technologies, with a vision of creating an interconnected world. This will benefit both corporations as well as the end-users. However, a plethora of security and privacy challenges need to be addressed for the IoT to be fully realized. In this paper, we identify and discuss the properties that constitute the uniqueness of the IoT in terms of the upcoming security and privacy challenges. Furthermore, we construct requirements induced by the aforementioned properties. We survey the four most dominant IoT architectures and analyze their security and privacy components with respect to the requirements. Our analysis shows a mediocre coverage of security and privacy requirements. Finally, through our survey we identify a number of research gaps that constitute the steps ahead for future research.

The number of smart things is growing exponentially. By 2020, tens of billions of things will be deployed worldwide, collecting a wealth of diverse data. Traditional computing models collect in-field data and then transmit it to a central data center where analytics are applied to it, but this is no longer a sustainable model. New approaches and new technologies are required to transform enormous amounts of collected data into meaningful information. Technology also will enable the interconnection around things in the IoT ecosystem but further research is required in the development, convergence and interoperability of the different IoT elements. In this paper, we provide a picture of the main technological components needed to enable the interconnection among things in order to realize IoT concepts and applications.

The "Internet of Things" (IoT) represents an em- bodiment of the continuous convergence between the physical facet of human activities and its reflection on the informa- tion world. In the context of “Industrial Internet”, where the operation of complex physical machinery is integrated with networked sensors and software applications, IoT is under- stood as a technological enabler of significant advances in the efficiency of industrial processes. In this paper, we elaborate on the main challenges brought on by the Industrial Internet and identify its key enabling technologies relevant to the Fog Computing paradigm. Further on, we propose the introduction of an Adaptive Operations Platform (AOP) to provide end-to- end manageability of the enabling Fog Computing infrastructure according to the operational requirements of industrial process. We present our prototype setup for AOP and elaborate on its performance evaluation in a typical industrial scenario.

Internet of Things (IoT) is a socio-technical phenomena with the power to disrupt our society such as the Internet before. IoT promises the (inter-) connection of myriad of things proving services to humans and machines. It is expected that by 2020 tens of billions of things will be deployed worldwide. It became evident that the traditional centralized computing and analytic approach does not provide a sustainable model this new type of data. A new kind of architecture is needed as a scalable and trusted platform underpinning the expansion of IoT. The data gathered by the things will be often noisy, unstructured and real-time requiring a decentralized structure storing and analysing the vast amount of data. In this paper, we provide an overview of the current IoT challenges, will give a summary of funded IoT projects in Europe, USA, and China. Additionally, it will provide detailed insights into three IoT architectures stemming from such projects.

With Internet of Things applications covering several application domains (e.g., Smart Grid, Smart City, e-Health) with a diverse range of requirements, it is now becoming increasingly recognized that the variety of wireless technologies employed for Machine-to-Machine (M2M) communication is here to stay. Due to the heterogeneity of M2M appliances and communication modes, research has been investigating autonomic principles as an efficient instrument for the integration of large populations of dissimilar M2M devices to the M2M gateway and the overall end-to-end M2M architecture. Herein we present the status of semantic support for the IoT field by briefly surveying research efforts in IoT dealing with semantic concerns at the architecture level. We also summarize the state of the art in adaptable protocol stacks and make the case for their application as an efficiency enabler instrument for IoT. To this end, we present our work in an ontology used to semantically describe the adaptation options of the dynamic protocol stacks for future communication devices. We present the ontology design principles and artifacts and elaborate on the level of semantic support it enables for IoT. Finally, we conclude the paper with our next steps in the area of semantic support for IoT.

Realizing good system performance in IEEE 802.11 deployments requires thorough network planning, a sophisticated task, which, more often than not, requires expert knowledge. In cases where IEEE 802.11 networks operating under different administrations reside in the same geographical area (as defined by the bounds of radio signal transmission and reception), the problem of efficient radio resource allocation arises. Particularly for IEEE 802.11a/b/g systems, efficient radio resource allocation in a dense urban environment can be quite a challenge, due to the finite number of interference-free channels available. To address this shortcoming, we study the mechanisms provided by the IEEE 802.11k standard upon the performance of radio resource management strategies in WLAN type systems under a dense deployment. To this end, we present a Learning With Communication (LWC) Distributed Channel Assignment (DCA) algorithm that exploits local communication among access points to realize a rapidly converging self-organizing channel assignment scheme. We evaluate the performance of LWC DCA algorithm through simulation scenarios.

The Internet of Things (IoT) is understood as a major embodiment of the convergence between device-oriented sensor networks and data-oriented applications that is facilitated through the Internet portfolio of technologies. From an architecture perspective, the wide range of operational parameters entailed by multiple application domains myriads of combinations of sensors and applications is the most significant challenge brought on by the IoT vision. To this end, the design blueprint of a platform that enables the seamless integration of multiple dissimilar devices and their efficient use by independently contributed applications is an essential architecture concern. Herein we address this concern by introducing a Unified Sensing Platform (USP) designed to accommodate an open set of sensor types and to expose their functional capabilities to applications in an efficient, reusable and context-aware way.