Vehicular Energy Network

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Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

Opportunistic Routing for Vehicular Energy Network

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TDMA-Based MAC (CVTMAC) in Green Vehicular Networks

The growing need to reduce the carbon footprint and the operation expenditure (OPEX) in communication networks necessitates the deployment of wind powered base stations (BSs) and roadside units (RSUs) for vehicular communication networks in windy countries with limited solar irradiation. This system finds ready application in sparse areas like countryside and motorways that lack the supply from the national grid for economic reasons. The stringent performance requirement of vehicular communication systems owed to their critical services poses challenges to their greening efforts. In this paper, we design a robust time-division multiple access (TDMA) based MAC for an infrastructure based green vehicular network in a motorway scenario and investigate the network performance against the stringent quality of service (QoS) thresholds. We call the proposed Centralised Vehicular TDMA based MAC as CVTMAC for short. To obtain a realistic performance evaluation, we model and simulate the proposed MAC protocol with the real channel characteristics of the motorway environment fully incorporated. The off grid RSU is powered solely by an economical and easy to deploy small standalone wind energy conversion systems (SSWECS). Wind energy-based rate adaptation is deployed in the RSU to enhance the efficient utilization of available energy (considering the intermittent nature of wind energy). In this study the real vehicular traffic profiles and wind data for a specified motorway region have been utilised. Both analytic and simulation results reveal that with the introduction of small battery capacity (27 Ah), the green vehicular network is able to support QoS for data, audio and video-related applications at each hour of the day in a motorway vehicular environment

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy