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Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) for a platoon of wireless power transmission (WPT)-enabled vehicles. Our objective is to maximize the system-wide computation capacity under both communication and computation resource constraints. We incorporate the coupled effects of the platooning vehicles and the flying UAV, air-to-ground (A2G) and ground-to-air (G2A) communications, onboard computing and energy harvesting into a joint scheduling optimization model of communication and computation resources. To tackle the resulting optimization problem, we propose a successive convex programming method based on a second-order convex approximation, in which feasible search directions are obtained by solving a sequence of quadratic programming subproblems and used to generate feasible points that can approach a local optimum. We also theoretically prove the feasibility and convergence of the proposed method. Moreover, simulation results are provided to validate the effectiveness of our proposed method and demonstrate its superior performance over other conventional schemes. [ABSTRACT FROM AUTHOR]

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server’s privacy perception of collaboration events. [ABSTRACT FROM AUTHOR]

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions, and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions. [ABSTRACT FROM AUTHOR]

Advances in future computing and communications to support IoT are becoming more important as the need to better utilize resources and make them energy-efficient grows. As a result, it is predicted that intelligent devices and networks, including WSNs, will become the new interfaces to support future IoT applications. However, many open challenges remain, which are mostly due to the resource constraints imposed by various hardware platforms and complex characteristics of applications wishing to make use of IoT systems. Thus, it becomes critically important to study how the current approaches incorporating both computing and communications in this area can be improved, and at the same time better understand the opportunities for the research community to utilize the proposed approaches. To this end, this article presents an overview of our latest research results in sensor edge computing and lightweight communication protocols as well as their potential applications. [ABSTRACT FROM AUTHOR]

With continued advancements of mobile computing and communications, emerging novel multimedia services and applications have attracted lots of attention and been developed for mobile users, such as mobile social network, mobile cloud medical treatment, mobile cloud game. However, because of limited resources on mobile terminals, it is of great challenge to improve the energy efficiency of multimedia services. In this paper, we propose a cloud-assisted green multimedia processing architecture (CGMP) based on mobile cloud computing. Specifically, the tasks of multimedia processing with energy-extensive consumption can be offloaded to the cloud, and the face recognition algorithm with improved principal component analysis and nearest neighbor classifier is realized on CGMP based cloud platform. Experimental results show that the proposed scheme can effectively save the energy consumption of the equipment. [ABSTRACT FROM AUTHOR]

In recent years, information communication and computation technologies are deeply converging, and various wireless access technologies have been successful in deployment. It can be predicted that the upcoming fifth generation mobile communication technology (5G) can no longer be defined by a single business model or a typical technical characteristic. 5G is a multi-service and multi-technology integrated network, meeting the future needs of a wide range of big data and the rapid development of numerous businesses, and enhancing the user experience by providing smart and customized services. In this paper, we propose a cloud-based wireless network architecture with four components, i.e., mobile cloud, cloud-based radio access network (Cloud RAN), reconfigurable network and big data centre, which is capable of providing a virtualized, reconfigurable, smart wireless network. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including Routing Protocol for Low-Power and Lossy Networks (RPL) for Internet of Things (IoT), IoT cloud, and global IoT services (GloTS) concept.