Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

Peer-to-Peer Secure Updates for Heterogeneous Edge Devices

We consider the problem of securely distributing software updates to large scale clusters of heterogeneous edge compute nodes. Such nodes are needed to support the Internet of Things and low-latency edge compute scenarios, but are difficult to manage and update because they exist at the edge of the network behind NATs and firewalls that limit connectivity, or because they are mobile and have intermittent network access. We present a prototype secure update architecture for these devices that uses the combination of peer-to-peer protocols and automated NAT traversal techniques. This demonstrates that edge devices can be managed in an environment subject to partial or intermittent network connectivity, where there is not necessarily direct access from a management node to the devices being updated

Blockchain for IoT Access Control: Recent Trends and Future Research Directions

With the rapid development of wireless sensor networks, smart devices, and traditional information and communication technologies, there is tremendous growth in the use of Internet of Things (IoT) applications and services in our everyday life. IoT systems deal with high volumes of data. This data can be particularly sensitive, as it may include health, financial, location, and other highly personal information. Fine-grained security management in IoT demands effective access control. Several proposals discuss access control for the IoT, however, a limited focus is given to the emerging blockchain-based solutions for IoT access control. In this paper, we review the recent trends and critical needs for blockchain-based solutions for IoT access control. We identify several important aspects of blockchain, including decentralised control, secure storage and sharing information in a trustless manner, for IoT access control including their benefits and limitations. Finally, we note some future research directions on how to converge blockchain in IoT access control efficiently and effectively

GRAPH-BASED FOG COMPUTING NETWORK MODEL

IoT networks generate numerous amounts of data that is then transferred to the cloud for processing. Transferring data cleansing and parts of calculations towards these edge-level networks improves system’s, latency, energy consumption, network bandwidth and computational resources utilization, fault tolerance and thus operational costs. On the other hand, these fog nodes are resource-constrained, have extremely distributed and heterogeneous nature, lack horizontal scalability, and, thus, the vanilla SOA approach is not applicable to them. Utilization of Software Defined Network (SDN) with task distribution capabilities advocated in this paper addresses these issues. Suggested framework may utilize various routing and data distribution algorithms allowing to build flexible system most relevant for particular use-case. Advocated architecture was evaluated in agent-based simulation environment and proved its’ feasibility and performance gains compared to conventional event-stream approach

Enhancing Data Security for Cloud Computing Applications through Distributed Blockchain-based SDN Architecture in IoT Networks

Blockchain (BC) and Software Defined Networking (SDN) are some of the most prominent emerging technologies in recent research. These technologies provide security, integrity, as well as confidentiality in their respective applications. Cloud computing has also been a popular comprehensive technology for several years. Confidential information is often shared with the cloud infrastructure to give customers access to remote resources, such as computation and storage operations. However, cloud computing also presents substantial security threats, issues, and challenges. Therefore, to overcome these difficulties, we propose integrating Blockchain and SDN in the cloud computing platform. In this research, we introduce the architecture to better secure clouds. Moreover, we leverage a distributed Blockchain approach to convey security, confidentiality, privacy, integrity, adaptability, and scalability in the proposed architecture. BC provides a distributed or decentralized and efficient environment for users. Also, we present an SDN approach to improving the reliability, stability, and load balancing capabilities of the cloud infrastructure. Finally, we provide an experimental evaluation of the performance of our SDN and BC-based implementation using different parameters, also monitoring some attacks in the system and proving its efficacy.Comment: 12 Pages 16 Figures 3 Table

An elastic software architecture for extreme-scale big data analytics

This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the ELASTIC Project (www.elastic-project.eu), grant agreement No 825473.Peer ReviewedPostprint (published version