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Internet-of-Vehicles (IoV), an important part of Intelligent Transportation Systems, is one of the most strategic applications in smart cities initiatives. The mMTC and URLLC functions of 5G are especially crucial for ensuring the connectivity and communication needs of rapidly moving IoVs. In this backdrop, network virtualization, cognitive computing along with smart spectrum resource management to the virtual networks will play a key role in solving the spectrum resource challenge. In this article, we propose a dynamic carrier resource allocation scheme for supporting IoV systems in smart cities enabled by cloud radio access networks (CRAN)-based 5G carriers. In CRAN-based 5G networks, the carrier resource allocated to the virtual networks can be centrally managed and shared to meet the dynamic demand of cell capacities caused by the rapid movement of IoVs, and the response to this dynamic allocation will become more time critical. The proposed cognitive carrier resource optimization is achieved by enhancing the ability to predict movement of IoVs, hence the dynamically changing demand for carrier resources. As an enhancement of the traditional Markov Model, our prediction model introduces vehicles' mobility analysis in order to allow the construction of a more precise flow transition matrix to improve the prediction result. Numerical results are provided to show the performance improvement of the proposed method.

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With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.