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The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with Fog Computing (FC), known as Software Defined Vehicular based FC (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about (i) maximizing resource utilization at the fog layer, and (ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

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The Internet of Things (IoT) has inspired the development of emerging new applications. Backed by the resourceful fog computing, the IoT devices are capable to meet the demands of tasks, even the most computationally-intensive ones. However, many existing IoT applications are unable to perform well, while communicating with the fog server due to different QoS requirements. Constantly changing traffic demands of applications is another challenge to consider. The demand for real-time applications includes communicating over a path that is less prone to delay. Moreover, applications that offload computationally-intensive tasks to the fog server need a reliable path that has lower probability of link failure. This results in a trade-off among continuously changing objectives, i.e., minimizing the end-to-end delay and maximizing the reliability of paths between IoT devices and the fog server. To tackle this problem, we propose a novel approach that considers an SDNenabled multi-hop scenario for the IoT-Fog environment. We evaluated the reliability level of links by employing the real-life network statistics recorded for a period of over 5 months. We use a multi-objective optimization (MOO) algorithm to search the Pareto-optimal paths by considering the two conflicting objectives. Our experimental evaluation considers two different applications -a real-time application (App-1) using UDP sockets and a task offloading application (App-2) using TCP sockets. Our results show that: (i) using MOO, the trade-off between the two objectives can be optimized, and (ii) the SDN controller was able to make adaptive decision on-the-fly to choose the best path from the Pareto-optimal set. The App-1, communicating over the selected path finished its execution in 13% less time than communicating over the shortest path. The App-2 had 41% less packet loss using the selected path compared to using the shortest path.

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Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile cloud (MC) computing can meet the demands of even the most computationally and resource-intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a trade-off. To counter this problem, we propose a data-driven technique that (1) does instrumentation by allowing class-, method-, and hybrid-level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade-off. Our experimental evaluation considers two MC hybrid Android-based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations.The analysis of our results shows that from the measured data, Pareto-efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth.

Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile-cloud (MC) computing can meet the demands of even the most computationally and resource intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a tradeoff. To counter this problem we propose a technique that: 1) measures, at run time, how well the MC application meets these two objectives; and 2) allows arbitrary configurations to be applied to the MC application in order to optimize the efficiency tradeoff. Our experimental evaluation considers two MC hybrid applications. We modularized them first, based on computationally-intensive tasks, and then executed them using a simple MC framework while measuring the power and bandwidth consumption at run-time. Analysis of results shows that efficient configurations of the apps can be obtained in terms of minimizing the two objectives. However, there remain challenges such as scalability and automation of the process, which we discuss.

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