Performance of wireless networks for Internet of Things (IoT) is suffering from exponential growth of lightweight hand-held and smart IoT devices as well as the data flow in the network. Static assignment of wireless resources is creating bottleneck to the further development of emerging applications including IoT and Cyber Physical Systems (CPS). Wireless network virtualization through slicing of primary wireless resources is regarded as an emerging approach for enhancing overall network performance to solve the bottleneck problem for those emerging applications. In this paper, we study wireless virtualization where a three-layer game is formulated for Wireless Infrastructure Providers (WIPs), Mobile Virtual Network Operators (MVNOs), and IoT devices (or end users). In the proposed three-layer game, players play sequentially to find out their optimal strategies. With the help of a controller, WIPs divide and reconfigure their RF bands to sublease frequency slices to MVNOs based on their service-level-agreement (SLA). MVNOs let end users use their subleased frequency slices. For the game, we present a unique optimal solution that facilitates a trade-off between quality of service (QoS) of end users, payoffs of MVNOs and payoff of WIPs. We also present a necessary condition and closed form for the existence of an equilibrium in the game. Furthermore, performance of the proposed approach is evaluated using numerical results which show that the payoffs of WIPs and MVNOs are maximized while offering competitive price for the subleased RF spectrum for their users.

One of the challenges that many security systems face is the inability to allow the system to perform certain functions without an operator monitoring the progress of the system, automatically. People cannot operate twenty-four hours a day. Human operators have a limit to operate during the time they are awake. People will always require sleep and even when they are awake they cannot sit in one place for a long time without any distraction. That is why there are unique features in the multi-camera video system that can make significant impacts in the security industry. The technical aspect of the technology is exceptional and it can assist many people to counter security challenges they face in their daily lives. It is important to consider these aspects when selecting which type of security systems are needed to adopt. Our approach could detect and recognize a human target from videos taken from cameras mounted on the wall to cover a target area. The proposed approach consists of detection and tracking of any targets that might be a threat. The decision-making process then decides whether or not the person is a threat or not.

Smart microgrids as a component of Industry 4.0 practices and a higher integration of renewable power resources have brought about unique cybersecurity problems. The objective is to provide the necessary immunity against cyber threats to keep the grid and infrastructure intact. A new multiple-layer cybersecurity framework is introduced in this paper to counter the risks posed by smart microgrids. The framework comprises best practices such as advanced anomaly detection systems, blockchain-based authentication, and real-time threat mitigation strategies employing AI techniques. According to the assessment, the developed framework launched has a better 96.5% detection accuracy compared to the 85% that traditional approaches offered. In addition, the framework adds another filter that decreases the false positive rate to 2.1 against 10 for conventional solutions. Strictly speaking, RIN increases the communication delay by 66.7% (from 15 to 25 ms) and energy overhead by 50% (from 3% to 4.5%), while the mitigation efficiency is the highest at 89%, and the resilience of the proposed system is 92%. The grid stability effect is still relatively small at 3.8%, so the fluctuating renewable power systems can continue. It also synthesizes knowledge for furthering Critical Energy infrastructure protection under the setting of Industry 4.0 and for future work to optimize the scalability and actual application of smart microgrids.

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.