Nikos Bizanis

Bio: Nikos Bizanis is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Mobile computing & Network virtualization. The author has an hindex of 1, co-authored 1 publications receiving 193 citations.

SDN and Virtualization Solutions for the Internet of Things: A Survey

Abstract: The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software defined networking (SDN) and network virtualization (NV) are two technologies that promise to cost-effectively provide the scale and versatility necessary for IoT services. In this paper, we survey the state of the art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterward, we move to the study of wireless sensor networks, arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on this topic.

Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

Abstract: Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges

Abstract: Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.