produce stable matchings among EVs, and then, devise a user-satisfaction model
to ensure realistic matchings. The real-life dataset from commercially
available EVs is used to test the performances of the algorithms, and the
results show the effectiveness and feasibility of the proposed EV-to-EV
matching strategy.

###  6.5 Summary

In this section, we have reviewed the development of emerging HEVs, PHEVs, and
EVs and different energy consumption models. Various factors may influence the
EV energy consumption, including the internal electricity generation
technologies and combined heat and power scale, the external traffic
conditions such as vehicle speed and acceleration, the road conditions such as
the grade and surface roughness, the environmental factors such as the
temperature and wind speed, and driving behaviour factors, etc. Current
researches mainly adopt DP-based and RL-based optimization algorithms to
reduce energy consumptions of different types of vehicles. For the EVs and
HEVs, the vehicle velocity and route become the main optimization variables,
while the engine power management has been paid great attention for PHEVs.

In addition, it is important to design novel EV charging management approaches
for improving the energy utilization efficiency of an IoV system. For the
traditional EV-to-grid charging service, current researches focus on
deployment and selection of charging stations, as well as coordination among
multiple charging stations for improving charging efficiency. With the
development of WPT technology, EVs can be charged on the move and the energy-
efficiency can be improved via optimal matching and cooperation among
charging/discharging EVs. However, EV-to-EV charging still faces several
challenges. For example, there is a lack of related studies on improving the
power transmission efficiency and reducing the cost for establishing power
transfer system. Besides, it is also challenging to reduce the influence of
vehicular mobility on the charging efficiency and achieve seamless switching
among different charging facilities.

##  7 Energy Harvesting and Sharing

Energy supply for the IoV devices and infrastructures is critical to provide
high-quality services. In this context, the energy harvesting technologies
will be important which have received significant attentions in the research
community to deal with the problem of energy constraints in the wireless and
IoT devices. Renewable energy harvesting and RF harvesting are two common
energy harvesting techniques. Fig. 7 shows various energy harvesting sources,
including the renewable energy sources such as solar panels and wind turbines,
as well as the RF signal harvesting through which energy in the
electromagnetic signals can be converted to electricity for the IoV devices
[89]. In the following, we first give the energy harvesting model and review
the green IoV related researches considering the two energy harvesting
techniques.

###  7.1 Energy Harvesting Model

The energy harvesting process can be modeled as successive energy packet
arrivals [189]. Denote by $E_{H}^{t}$ the unit of energy arrival at the mobile
mobile (i.e., phones or EVs) at the $t$th time slot, which can be assumed as
an independent and identically distributed (i.i.d.) random variable with the
maximum value of $E_{H}^{\max}$. The i.i.d. model is able to characterize the
stochastic and intermittent nature of the renewable energy harvesting process
[190, 191]. Denote by $e^{t}$ the part of arrived energy in the $t$th time
slot, then,

$0\leq{e^{t}}\leq E_{H}^{t},t\in T.$ (16)

The energy $e^{t}$ will be stored in the battery for executing/transmitting
the computation task arrived at the following time slots. In the computation
offloading applications, the values of $e_{t}$ can be optimally determined to
optimize the battery energy efficiency. Denote the energy consumption of the
mobile device at time slot $t$ as $E_{m}^{t}$, which consists of the local
computation energy consumption and the transmission energy consumption of
offloaded tasks. $E_{m}^{t}$ satisfies the energy causality constraint:

$E_{m}^{t}\leq{B^{t}}<+\infty,t\in T,$ (17)

where $B^{t}$ is the battery energy level at time slot $t$, and it is
constrained by ${B^{t}}=0$ and ${B^{t}}<+\infty,t\in T,$. Then, the battery
energy level at the next time slot $t+1$ can be expressed as:

${B^{t+1}}={B^{t}}-E_{m}^{t}+{e^{t}},t\in T.$ (18)

In the computation offloading scenario with energy harvesting enabled mobile
devices, the offloading decisions are hard to make due to the varying battery
energy level and the coupled relationship of energy consumption/harvesting at
successive time slots, and the energy efficiency should be evaluated in the
whole scheduling period.

###  7.2 Renewable Energy Sources

Utilizing the renewable energy has been an attractive option for environmental
sustainability. While the renewable energy sources can be used for electronic
power generation in a cost-effective way, designing an intelligent energy-
harvesting and energy management framework for IoV is challenging. For
example, how to design the energy harvesting and energy management policies
for the RSUs according to the prediction of real-time service requests, so as
to maximize the energy utilization? Second, for satisfying huge amount of
real-time requests, methods for efficient collaboration among different energy
sources deserve to be further studied. Third, since the EVs can store and
transfer energy to other EVs and charging stations, it is natural to consider
how to design the charging and discharging strategies in realistic traffic
environments with varying traffic lights and potential road congestions, etc.

The concept of ‘energy internet’ has been proposed to enable the sharing of
renewable energy (i.e., storing, distributing and controlling) in the
information Internet [166]. A lot of researches have been conducted in the
architecture design of electric power distribution system [192], design and
deployment of energy routers (i.e., which can dynamically adjust the energy
distribution) [193, 194], and the charging/discharging behaviours between
energy nodes and vehicles [195]. As battery-powered moving vehicles, the EVs
can store and distribute a wide variety of renewable energy when driving in
cities. For example, the EVs can store excess electricity such as the solar
energy, and return to power grid during on-peak periods through vehicle-to-
grid (V2G) connectivity. Then, the other EVs can charge from the power grid
and indirectly benefit from the renewable energy [196, 173].

Figure 7: Common energy harvesting sources

In addition, the renewable energy has also been applied to support other
common IoV infrastructures for saving energy, such as the solar-powered RSUs
[44] and wind-powered RSUs [43]. The conventional RSUs are deployed with
connections to the power lines and wired backhauls, which is not feasible in
areas with under-developed power systems. Meanwhile, the next generations of
ITS applications will require a dense deployment of RSUs to support V2I
communication, and their deployment cost will be very high. Therefore,
numerous researches have considered to adopt the ‘green RSU’ in future IoV
systems. Q. Ali et al. [197] present the design procedure of an RSU armed with
the solar energy harvesting circuit and power management module, so as to
extend the communication coverage of the IoV infrastructure, and achieve green
vehicular communication. With solar-powered RSUs or wind-powered RSUs, to
harvest renewable energy as power source, the energy harvesting rate and the
RSU density can be further optimized to minimize the network deployment cost
[198].

###  7.3 Renewable Energy Harvesting

The renewable energy management mechanism needs to be designed for energy
harvesting, storing, distributing between vehicles and IoV infrastructures for
improving the energy utilization efficiency. In [47], the authors present an
energy-harvesting framework with battery-enabled RSUs and EVs for achieving
green IoV. RSUs are equipped with wind turbines in one battery cycle, and can
sell redundant energy to passing-by EVs via RF energy transfer technology. On
the contrary, when the RSU is in mid-level and low-level in battery capacity,
and/or the renewable energy generation speed is lower than the energy
consumption speed, then the RSU can purchase energy from nearby EVs. On one
hand, RSUs intend to maximize the benefits through selling redundant energy to
passing-by EVs and minimize the costs of purchasing energy from EVs. On the
other hand, EVs hope to minimize the cost of purchasing energy from RSUs and
maximize the benefit of selling energy to RSUs. By modeling the requirement
processing at the RSU as a queueing system and analyzing the utilities of the
RSUs and EVs, the authors further propose a three-stage Stackelberg game-based
energy-harvesting strategy, which can balance the utility and energy
consumption of the RSUs and EVs.

Furthermore, by adaptive traffic management and energy cooperation based on
the energy harvesting level of IoV infrastructures, the energy utilization can
be improved [199, 200, 201]. With traffic management, the network energy
consumption can be reduced through adjusting the served traffic (i.e., by
resource allocation) [202] and network conditions (i.e., the BS ON/OFF
switching, zooming the cell size, and users association, etc.) [203] according
to the renewable energy level of the IoV infrastructures. The energy
cooperation approach can reduce network energy consumption by sharing the
renewable energy among IoV infrastructures according to the traffic demands
and energy conditions. For instance, the authors in [201] adopt an energy
sharing and traffic management approach to achieve green IoV infrastructure.
An energy-efficient downlink traffic scheduling strategy is designed for the
RSUs when they are empowered by renewable energy source such as solar panel.
Then, an online algorithm is developed to allocate RSU and service time slots
according to the V2I communication distances and the energy provisioning of
RSUs. In [200], the authors design an adaptive traffic management and energy
cooperation algorithm to reduce the energy consumption of BSs, which are
powered by both on-grid and renewable energy sources. They consider a
stochastic system model with uncertain user arrivals, channel conditions and
the amount of harvested energy, and adopt a queueing model to manage the
traffic demands and energy levels. Then, a Lyapunov optimization-based
algorithm is proposed to jointly decide on the energy sharing among BSs, the
user association to BSs, and resource allocation in the BSs.

###  7.4 Radio Frequency (RF) Harvesting

Compared to the traditional energy harvesting techniques which utilize the
ambient sources such as the solar and wind, the RF energy harvesting is more
reliable due to independence on environmental factors. The hybrid access point
(HAP) is a kind of RF energy source, which can be configured to support energy
transfer and communication demands [204, 205, 206]. However, it consumes extra
energy when the HAP sends dedicated signals for energy transfer to associated
devices. Therefore, energy-efficient resource allocation methods are required
for determining the optimal energy supply by the HAP to associated devices
[207, 208]. The authors in [209] consider a wireless-powered communication
network (WPCN) which uses a HAP to serve a set of wireless devices over
orthogonal frequency channels. The HAP can not only provide energy to the
devices, but can also collect information from devices. The energy signal
transmission and information collection are assumed to be over two separate
frequency bands. In a WPCN, the HAP can learn the transmission power
consumption of the associated wireless transmitter, and further determine the
suitable energy signal for the device. They also propose a robust unsupervised
Bayesian learning method to avoid attacks from an adversary who tries to alter
the HAP’s learning results. The power selection problem for the HAP’s energy
signal is formulated as a discrete convex optimization problem, and the
optimal transmission power is determined from a close-formed solution.
Experimental results show that the robust Bayesian learning approach can
reduce the packet drop rates without jeopardizing the energy consumption of
the HAP. Similarly, J. Kwan et al. [210] also consider a WPCN with several
HAPs to collect sensed data from the on-body sensors of a user. Then sensors
can temporarily store the harvested energy using storage capacitors. When all
the HAPs are out of sensor range, the sensors can only harvest energy from
unintended RF energy sources (i.e., nearby mobile phone users). They propose a
coordinated ambient/dedicated (CA/D) protocol to optimize the energy
harvesting operations of sensors from both the intended RF sources (i.e.,
HAPs) and the unintended RF sources. The CA/D protocol uses two machine
learning techniques (i.e., linear regression-based and ANN-based techniques)
to make the optimal energy harvesting decisions with the unpredictable
availability of unintended sources and dynamically changing channel conditions
between the sensors and unintended RF sources.

There has been considerable interest in utilizing the RF techniques to improve
IoV energy efficiency. Since IoV applications require vehicles to periodically
upload/download traffic data and scheduling decisions, a large amount of
energy is consumed in both transmission and computation processes. Therefore,
deploying RF energy harvesting becomes a promising solution for an IoV system
to improve energy and spectral efficiency [211]. H. Xiao et al. [71] consider
a radio-frequency-energy-powered cognitive radio network (RF-CRN) for
connected vehicles, where the secondary users (SUs) (i.e., vehicles) can
harvest energy from RF signals of the primary network (PN) via wireless
transmission in the downlink, and then, the SUs can use the harvested energy
for uplink data transmissions. Due to co-channel interference among multiple
SUs, the authors propose to jointly optimize the transmission time and power
control of all the users for maximizing the energy-efficiency of RF-CRN under
the given QoS requirement. The non-convex energy optimization problem is
transformed into a tight lower-bound convex approximation problem, and the
optimal solution is obtained by the Frank-Wolfe (FW) and one-dimensional
linear programming approaches.

###  7.5 Summary

Various forms of energy from renewable energy sources can be transformed into
electronic power. By equipping the EVs and RSUs with renewable energy
harvesting units and designing efficient strategies for energy storage and
distribution, the energy-efficiency of IoV systems can be improved. Design of
efficient frameworks for energy management, coordination among the energy
harvesting sources, energy transfer and cooperative traffic scheduling are
critical challenges to be addressed in future IoV research. In addition, the
time-varying energy demands of EVs and the dynamic energy supply due to
intermittent generation of energy should be jointly considered to improve the
energy utilization.

##  8 Future Research Trends

###  8.1 Green IoV Infrastructures Management

To provide seamless connectivity for future autonomous driving vehicles, the
roadside cameras, radars, RSUs, MEC servers, and even UAVs will be densely
deployed to monitor the real-time traffic conditions, and quickly respond to
emergencies. In addition, the BSs will support V2X communications and provide
computation services for mobile vehicles. In the 6G era, the number of BSs is
also expected to higher than that in the 5G networks. These IoV
infrastructures will consume a huge amount of energy in information collection
and processing. Meanwhile, heterogenous hardware, network access, and
computing architectures, high dynamicity in the communication environment, and
universal connectivity requirement will increase the difficulty in achieving
green IoV systems. To realize green IoV service, the deployment and working
state adjustment solutions for the IoV infrastructures will need to be well
designed. In addition, allocations of the workloads and the resources (for
communications and computing) will be critical to improve user performance and
reduce energy consumption. Besides, energy harvesting will be a key technique
to reduce the grid electricity demand of the IoV infrastructures.

###  8.2 Green Space-Aerial-Terrestrial-Sea Communication

The space-air-ground integrated networking (SAGIN) will be one of the key
technologies in the 6G era. However, the heterogenous hardware platforms,
time-varying channel conditions and time-sensitive application requirements
complicate the efficient deployment and operation of green space-air-ground
communication systems. AI-based channel condition prediction and service
requirement prediction, and adaptive online resource allocation techniques
will be required in future SAGIN. Current researches mainly focus on the
performance optimization of a single layer, such as the joint optimization of
flying trajectory and task scheduling between UAVs and vehicles. To improve
the overall energy-efficiency of IoV systems, cross-layer and end-to-end
energy optimization of systems integrating satellites, MEC-enabled High-
Altitude Balloons (HABs), UAVs, and vehicles will be required.

###  8.3 Joint Optimization for EV Charging

Existing researches have proposed a lot of green EV-to-EV and EV-to-grid
charging strategies to improve the EV charging efficiency. However, the
existing works lack in joint consideration of the availability and fuel
capacities of the charging stations and the EVs, the renewable energy
harvesting states, EV driving routes, and even the realistic traffic
conditions including the traffic lights and road congestion. How to deal with
these complicated factors in EV charging is an open issue. In addition, with
the emerging various charging schemes and distributed charging scenarios, AI-
based algorithms (i.e., machine learning, state-of-the-art deep learning) for
optimizing the charging paths and charging decisions in real time are
appealing for future EV charging scenarios. Where to deploy the AI-based
algorithms and how to realize quick convergence in real-time scheduling
environment and protect the privacy of participants are unsolved issues.
Meanwhile, various wireless power transfer technologies can be used for EV
charging, such as the resonant beam charging and radio charging. However, the
hardware design in improving the charging speed and stability with moving EVs
is still very challenging. The cost for establishing the wireless power
transfer system will also need to be optimized.

###  8.4 Intelligent Energy Harvesting and Sharing

For the energy harvesting techniques which are based on the renewable
resources such as the solar, winding and tide, it is important to predict the
future energy harvesting states and real-time energy demands of nearby IoV
devices, so as to make the optimal charging/discharging decisions. In
addition, the joint design of intelligent energy-harvesting framework and
resource utilization strategies is critical in realizing green IoV scenarios.
On one hand, the resource coordination mechanism between IoV devices will
facilitate the design of energy harvesting equipments. On the other hand, a
well-designed energy-harvesting framework will affect the efficient energy
sharing among distributed IoV devices. In order to improve the overall energy-
efficiency, mechanisms for efficient collaboration and seamless handover among
different energy sources will need to be investigated.

###  8.5 Green Heterogenous V2X Communication

Since V2X communications are supported by heterogenous communication
interfaces (i.e., DSRC and C-V2X), it is necessary to develop new protocols
which enable seamless handover between various communication interfaces, so as
to achieve high-reliability and low-latency communication in a dynamic traffic
environment, and improve the energy efficiency. In addition, with a dense
deployment of BSs in future 6G networks, frequent switching between the IoV
users and the BSs will be unavoidable, and rapidly growing number of IoV users
will further increase the energy consumption in communication and computation
switching processes. Therefore, integrated network interfaces and novel
communication protocols should be designed to reduce handover cost and improve
energy-efficiency for 6G-enabled green IoV systems.

###  8.6 Security Issue in Green IoVs

In the 6G era, more vehicles will be connected to the same IoV infrastructure
(i.e., BS or RSU). It will be critical to ensure security in communication and
privacy protection among vehicles of different owners. The major security
threats come from the unauthenticated users who may cheat the transmitters and
receivers by returning misleading information, and achieve malicious access to
system resources. In future AI-driven IoV, the adversaries may generate
malicious data to mislead the learning direction and cause inefficiency in
training performances.

The federated learning is a decentralized learning framework that utilizes
distributed servers to train AI models without uploading data to a centralized
server and scheduler [212]. This provides both data security and efficient
knowledge sharing. The centralized server only needs the training model
parameters from distributed servers, which can save the energy consumption in
transmitting data to the central scheduler. In addition, featured by the
decentralization, transparency, and immutability, the Blockchain technique can
be adopted to ensure trust in an IoV system [213]. In blockchain-enabled IoVs,
excessive energy may be consumed due to ledger-updates, peer-to-peer smart
contracts and transactions. Therefore, how to design an energy-efficient
transaction model which can effectively manage the number of transactions and
conserve a maximum amount of available energy becomes a critical issue [214].
Meanwhile, the blockchain technique has been considered for energy
trading/sharing scheme for Internet of EVs [215, 216, 137]. Different from the
traditional centralized power systems, new challenges are emerging in
distributed energy trading/sharing environment, for example, how to encourage
HEVs/PHEVs/EVs to participate in EV-to-EV energy transactions for improving
energy utilization, and design promising consensus mechanism among vehicle to
reduce the resource consumption of consensus.

###  8.7 AI-based Green IoVs

AI techniques include the conventional heuristic algorithms, machine learning
(ML) algorithms and deep learning (DL) algorithms, etc. Compared to the
traditional optimization algorithms which are mainly used in problems with
given assumptions and fixed parameters, AI techniques are able to handle
problems with unclear relationships between numerous network parameters, and
can be applied in complex scenarios where the solution space is extremely huge
and network condition is highly dynamic. In addition, the AI-based algorithms
avoid the computation overhead of iterative optimization process, and is more
suitable for real-time vehicular network environment. However, the energy
consumption of these AI-based methods and algorithms will need to be
considered [217]. By combining the ML/DL models with heuristic-based or game
theoretic models (e.g. in a multi-agent scenario), the system energy-
efficiency can be improved. In an intelligent traffic management system, AI
techniques can be used for adaptive traffic light control with predicted
traffic volume at the intersection according to the historical data. In
vehicular edge computation offloading scenarios, by predicting the future
resource requirements and workload arrivals, the AI techniques can facilitate
the switching of the IoV edge servers (i.e., BSs and RSUs) to active/sleep
modes, so as to reduce the infrastructure energy consumption. In an energy-
harvesting system, AI techniques can track the dynamic energy harvesting
states and optimize network configuration by combining the predicted future
energy requirements.

##  9 Conclusion

We have provided a comprehensive survey on the development of green IoV
systems. The architecture of a green IoV system has been presented and five
scenarios, namely, V2X communication, vehicular edge computing, intelligent
traffic management, electric vehicles and their energy management, and lastly,
energy harvesting and management have been focused. For different scenarios of
communication, computation, traffic management, electric vehicles, and energy
harvesting management, we have presented the challenges and reviewed the
state-of-the-art approaches to achieve energy efficiency. The general energy
consumption models for V2X communication, vehicular edge computing, vehicle
driving, EV charging and energy harvesting have been also discussed. Finally,
we have outlined some open issues in developing 6G-enabled green IoV systems.
We anticipate that this article will serve as an important and useful
reference in the area of green IoV and spur new researches on this topic.

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|  Junhua Wang received the BS degree and PhD degree in computer science from
Chongqing University, in 2014, and 2019, respectively. From 2017 to 2018, she
was a visiting scholar in University of Houston, USA. Since 2019, she has been
working with the College of Computer Science and Technology, Nanjing
University of Aeronautics and Astronautics. Her research interests include
mobile computing, vehicular ad-hoc networks, and wireless networks.
---|---
|  Kun Zhu (M’16) is currently a Professor in the College of Computer Science
and Technology, Nanjing University of Aeronautics and Astronautics, and the
Collaborative Innovation Center of Novel Software Technology and
Industrialization, Nanjing, China. He is also a Jiangsu specially appointed
professor. He received his Ph.D. degree in 2012 from School of Computer
Engineering, Nanyang Technological University, Singapore. His research
interests include resource allocation in 5G, wireless virtualization, and
self-organizing networks. He has published more than fifty technical papers
and has served as TPC for several conferences. He won several research awards
including IEEE WCNC 2019 Best paper awards, ACM China rising star chapter
award.
---|---
|  Ekram Hossain (F’15) is currently a Professor and the Associate Head
(Graduate Studies) in the Department of Electrical and Computer Engineering at
University of Manitoba, Canada. He is a member of the College of the Royal
Society of Canada (Class of 2016), a Fellow of the Canadian Academy of
Engineering, and a Fellow of the Engineering Institute of Canada. His research
interests include design, analysis, and optimization of wireless and mobile
communication networks and applied machine learning for intelligent wireless
connectivity with emphasis on beyond 5G cellular networks. He was elevated to
an IEEE Fellow for contributions to spectrum management and resource
allocation in cognitive and cellular radio networks. He was listed as a
Clarivate Analytics Highly Cited Researcher in Computer Science in 2017, 2018,
2019, and 2020. He was an elected member of the Board of Governors of the IEEE
Communications Society (2018-2020). He received the 2017 IEEE ComSoc TCGCC
(Technical Committee on Green Communications & Computing) Distinguished
Technical Achievement Recognition Award “for the Outstanding Technical
Leadership and Achievement in Green Wireless Communications and Networking”.
He currently serves as the Editor-in-Chief of the IEEE Press (2018-2021).
Previously, he served as the Editor-in-Chief for the IEEE COMMUNICATIONS
SURVEYS AND TUTORIALS (2012-2016).
---|---