Edge Computing Enabled Resilient Wireless Network Virtualization for Internet of Things

With the successful deployment of wireless networks such as cellular and Wi-Fi networks as well as development of lightweight hand-held devices, wireless communication became the fastest growing sector in communication industry and wireless networks have been part of every business. Over 25 billion devices are expected to be connected to the Internet by 2020. Because of exponentially increasing number of connected devices, we have Internet of Things (IoT) enabled applications that offer socioeconomic benefits. Different IoT applications have different operational requirements and constraints. For instance, IoT enabled transportation cyber-physical system needs lowest latency and high data rate, IoT enabled financial systems or banks need high security to support mobile banking, IoT enabled manufacturing systems need high resiliency to combat fault tolerance and cyber-attacks, IoT enabled cyber-physical power system needs the least latency and highest resiliency to avoid power outage caused by faults or cyber-attacks. In this paper, we propose wireless network virtualization to create different virtual wireless networks (VWNs) through mobile virtual network operators (MVNOs) to support different IoT enabled systems with diverse requirements and resiliency. Wireless virtualization is regarded as an emerging paradigm to enhance RF spectrum utilization, provide better coverage, increase network capacity, enhance energy efficiency and provide security. In order to prevent double-spending (allocating same frequency to multiple network providers) of wireless resources, we have proposed to use blockchain based approach which provides quality-of-service to users. Furthermore, IoT is expected to generate massive amount of data (aka big data), we consider edge computing to process big data when individual devices have limited computing/processing and storage capabilities. Moreover, network segmentation through VWNs provides the security and enhances the network performance. Performance of the proposed approach is evaluated using numerical results obtained from simulation.