Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system by combining both PVA and smart parking. A VFC- aware parking reservation auction is proposed to guide the on- the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of fog system. The simulation results confirmed the win-win performance enhancement to the fog node controller (FNC), the smart vehicles and the parking places from the proposed design.

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases, such as augmented reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

Femtocells are a promising technology to improve network performance with short-range, low-power, and cost-beneficial small base stations. There will be many prospective benefits by applying the femtocell technique to wireless communication standards such as the Global System for Mobile Communications (GSM), WiMAX, and Long-Term Evolution. This paper studies two main design issues in orthogonal frequency-division multiple-access (OFDMA) femtocell networks, i.e., resource sharing and femtocell access control. Unlike other previous works, our presented analysis encompasses modeling wireless users' selfish characteristic and private traffic information. In addition, this provides a more comprehensive perspective on self-organizing femtocell networks, where users optimize their performance in a distributed manner. Our contributions are threefold: 1) We design a resource allocation mechanism that gathers selfish users' true private traffic information. 2) The proposed mechanism achieves efficient and fair resource sharing. 3) Based on the proposed mechanism, we create incentives for subscribers to share their femtobase stations with general users. This further improves the overall system performance.

With the rapid development of deep learning technology, the modern internet-of-things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to mobile edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this paper, we provide Decomposable Intelligence on a Cloud-Edge IoT (DICE-IoT) framework to support joint latency-and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a Generalized Benders Decomposition (GBD) based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win-win-win solution to the IoT device, the MEC, and the cloud stratums.

The Internet of Things (IoT) is one of the most promising technologies of the fifth-generation (5G) mobile broadband system. Data-driven wireless services of 5G systems require unprecedented capacity and availability. In this article, we introduce the potential issues of consumer devices under a unifying 5G framework. We provide a state-of-theart overview with an emphasis on the technical challenges of applying millimeter-wave (mm-wave) technology to support massive IoT applications. Our discussion highlights the challenges and solutions, particularly for communication/computation requirements in consumer devices under the mmwave 5G framework.