Future Communications and Energy Management in the Internet of Vehicles: Toward Intelligent Energy-Harvesting

As an emerging communication platform in the Internet of Things, IoV is promising to pave the way for the establishment of smart cities and provide support for various kinds of applications and services. Energy management in IoV has been attracting an upsurge of interest in both academia and industry. Currently, green IoV mainly focuses on two aspects: energy management of battery- enabled RSUs and EVs. However, these two issues are always resolved separately while ignoring their interactions. This standalone design may cause energy underutilization, a mismatch between traffic demands and energy supplies, as well as high deployment and sustainable costs for RSUs. Therefore, the integration of energy management between battery-enabled RSUs and EVs calls for comprehensive investigation. This article first provides an overview of several promising research fields for energy management in green IoV systems. Given the significance of efficient communications and energy management, we construct an intelligent energy-harvesting framework based on V2I communications in green IoV communication systems. Specifically, we develop a three-stage Stackelberg game to maximize the utilities of both RSUs and EVs in V2I communications. After that, a real-world trajectory-based performance evaluation is provided to demonstrate the effectiveness of our scheme. Finally, we identify and discuss some research challenges and open issues for energy management in green IoV systems.

[1]  Jin Chen,et al.  Cooperative Optimization of Electric Vehicles in Microgrids Considering Across-Time-and-Space Energy Transmission , 2019, IEEE Transactions on Industrial Electronics.

[2]  Feng Xia,et al.  Green and Sustainable Cloud of Things: Enabling Collaborative Edge Computing , 2019, IEEE Communications Magazine.

[3]  Gun-Woo Moon,et al.  Wireless Power Transfer System With an Asymmetric Four-Coil Resonator for Electric Vehicle Battery Chargers , 2016, IEEE Transactions on Power Electronics.

[4]  Jaafar M. H. Elmirghani,et al.  Energy and QoS Evaluation for a V2R Network , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[5]  Jia Yuan Yu,et al.  A Reinforcement Learning Technique for Optimizing Downlink Scheduling in an Energy-Limited Vehicular Network , 2017, IEEE Transactions on Vehicular Technology.

[6]  Chadi Assi,et al.  Energy harvesting in vehicular networks: a contemporary survey , 2016, IEEE Wireless Communications.

[7]  Zhu Han,et al.  Wireless Networks With RF Energy Harvesting: A Contemporary Survey , 2014, IEEE Communications Surveys & Tutorials.

[8]  Lei Guo,et al.  Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling , 2019, IEEE Network.

[9]  Bo Gao,et al.  Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective , 2017, IEEE Transactions on Vehicular Technology.

[10]  Jaafar M. H. Elmirghani,et al.  Greening vehicular networks with standalone wind powered RSUs: A performance case study , 2013, 2013 IEEE International Conference on Communications (ICC).

[11]  Jianping Pan,et al.  Delay Minimization for Data Dissemination in Large-Scale VANETs with Buses and Taxis , 2016, IEEE Transactions on Mobile Computing.

[12]  Shusen Yang,et al.  A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging , 2017, IEEE Communications Magazine.

[13]  Lei Wang,et al.  Privacy-Preserving Content Dissemination for Vehicular Social Networks: Challenges and Solutions , 2019, IEEE Communications Surveys & Tutorials.