The ever need for transportation safety, faster and convenient travel, decrease in energy consumption, as well as inter-connectivity has led to the field of intelligent transportation system (ITS). At the core of ITS is the Internet of Vehicles (IoV) combining hardware/sensors, software, and network technologies. Vehicular ad hoc networks (VANETs) create mechanisms to connect IoV main elements, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and Vehicle-to-Sensors (V2S). ITS systems heavily rely on its network connecting different parts of its infrastructure and ensuring data exchanges. However, VANET security is one of the primary challenges faced by connected vehicles. In IoV, the network is accessed by a variety device making the system vulnerable to a multitude of malicious attacks, including distributed denial-of-service (DDoS) and black hole attacks. Since critical vehicle systems can be accessed remotely, successful attacks can lead to fatalities. In VANET, any node can function as a router for the other nodes, therefore a malicious node connected to the network may inject spoofed routing tables to the other nodes thereby affecting the operation of the entire network. To overcome this issue, we proposed a security scheme designed to improve routing protocols in the detection of black hole attack. The proposed approach is demonstrated on a Network Simulator (NS3.27) using different network parameters such as average packet loss rate, end-to-end delay, packet delivery ratio (PDR) and network yield. Simulation results demonstrate the proposed method adds 10-15% improvement (on average) in End-to-End Delay, Packet Delivery Rate, Packet Loss Rate and Network Yield as compared with conventional Greedy Parameter Stateless Routing and Path Aware Greedy Parameter Stateless Routing under the black hole attack.

Multi-core systems are outperforming nowadays. Therefore, various computing paradigms are intrinsically incorporated in the multicore domain to exploit its potential and solve well known computing problems. Parameter tuning is a well-known computing problem in the field of Multicore domain. Addressing the said hurdle would leverage in the performance enhancement of Multicore systems. Various efforts in this direction have been made through the conventional parameter tuning algorithms in a limited scope; however, the problem is yet not addressed completely. In this research article, we have addressed parameter tuning problem by employing applications of graph theory, especially Dijkstra shortest path algorithm to address the said issue. Dijkstra’s principle has been applied to establish correlation among the parameters further tuning by finding the pair of suitable parameters. Two other algorithms which are based on application feedback (to provide performance goals to the system) has been introduced. The proposed algorithms collectively (as a framework), addressed the parameter tuning problem. The effectiveness of the algorithms is verified and further measured in distinct parameter tuning scenarios and promising outcome has been achieved.

This paper comprehensively reviews the recent 5G and future 6G Internet of Things (IoT) protocols/standards, applications, and access networks used. First, most of the IoT protocols/standards and application scenarios are summarized in the form of tables, pictures, and diagrams to facilitate readers to understand and compare current and future Internet of Things technologies more easily and quickly. Second, the terrestrial and aerial radio access networks are analyzed and discussed in detail. The evolution of 5G terrestrial access networks is briefly described and its performance limitations are quantitatively analyzed and discussed. When the operating frequency reaches the sub-millimeter wave band, the terrestrial radio access network will deal with high path loss caused by weather factors, such as oxygen and water vapor absorption in the atmosphere, rainfall, and cloud/fog attenuation. The development of aerial radio access networks is preparing for 6G IoT to solve the coverage and path loss issues. In this survey, the aerial radio access architectures and infrastructure are also surveyed. This survey aims to guide readers to better understand the technical status of 5G IoT and the milestones as well as key performance indicators that need to be reached for 6G IoT in the future.

Live virtual machine migration is a valuable feature for the virtualized data center or cloud environment. This is the process to migrate running virtual machines from one physical host to another host. Live virtual machine migration can be used to provide various benefits such as server consolidation, energy-saving, and maintenance. It is a valuable feature for the virtualized data center or cloud environment. Cloud computing provides IT capabilities as a service and its key technology is virtualization. The key benefit of virtualization is to offer better resource employment by executing various VMs on the same physical system. In this research, we analyze the performance of the various hypervisors based on their migration features and compares the migration feature. Hypervisors are Xen, VMWare, KVM, and their migration feature are XenMotion, vMotion, and KVM migration, respectively. According to our study, we find that there are many factors that affect the performance of the live virtual machine migration such as a long downtime, the large amount of data that is sent in an iteration manner so with a higher dirtying rate the total migration time extends. In our comparison, we show VMWare has the least downtime.  We also identify and discuss the various research challenges in detail to stimulate the researchers to work in this direction.

Probabilistic parametric functions such as density and distribution functions modeled to depict certain stochastic behaviour are used to express the fundamental theories of reliability engineering. In the existing works of literature, a few probability distribution functions have been well reported. However, selecting and identifying the most suitable distribution functions to reliably model and fit datasets remain. This work examines the application of three different methods for selecting the best function to model and fit measured data. The methods comprise the parametric maximum likelihood estimation, Akaike Information Criteria and the Bayesian Information Criteria. In particular, these methods are implemented on Signal Interference to Noise Ratio (SINR) data acquired over an operational Long Term Evolution (LTE) mobile broadband networks in a typical built-up indoor and outdoor campus environment for three months. Generally, results showed a high level of consistency with the Kolmogorov-Semirnov Criteria. Specifically, the Weibull distribution function showed the most credible performance for radio signal analysis in the three study locations. The explored approach in this paper would find useful applications in modeling, design and management of cellular network resources