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Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking
Junhua Wang, Kun Zhu, Ekram Hossain
TL;DR
The paper addresses the energy burden created by expanding 6G-enabled IoV infrastructure, connectivity, computation, and intelligent services. It reviews green IoV across communication, computation, traffic, EV, and energy-harvesting scenarios, compares energy-optimization approaches, and identifies enabling technologies, challenges, and future directions. A representative result reports that AP sleep strategies can save about 80% of transmission energy during off-peak hours.
Problem
6G-enabled IoV faces rising energy consumption from dense infrastructure, increasing network nodes, higher-frequency communication, demanding computation, and stringent QoS requirements.
Method
The paper reviews green IoV research across communication, computation, traffic management, EVs, and energy harvesting, comparing optimization approaches and energy-efficiency factors.
Results
AP sleep strategies can save about 80% of transmission energy during off-peak hours.
Takeaways & Limitations
Green IoV research spans resource allocation, workload scheduling, routing, traffic control, charging, energy harvesting, and energy sharing, alongside emerging 6G technologies.
Takeaways & Limitations
The communication discussion notes that energy-efficient collaboration among mmWave and THz radios and coordination between NOMA and orthogonal schemes remain insufficiently explored.
Abstract
from arXiv · showhide
As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.
1 INTRODUCTION
The paper frames green IoV as necessary for sustainable 6G vehicular communications because expanding connectivity, infrastructure, computation, and stringent intelligent-service requirements increase energy consumption. It reviews energy-efficient techniques, emerging 6G-enabled scenarios, challenges, technologies, and future research directions.
- IoV foundations: IoV connects vehicles with infrastructure, pedestrians, networks, grids, clouds, and other entities through V2X communication.The paper distinguishes IoV from traditional VANETs by emphasizing broader information services and heterogeneous networking.
- 6G motivation: 6G expands IoV capabilities through higher rates, longer transmission distances, and services including uMUB, uHDD, and uHSLLC.These services support space-aerial-terrestrial-sea communication, high-density reliable connectivity, and ultrahigh-speed low-latency communication.
- Energy challenge: Energy efficiency becomes critical because connected devices, communication and computation demands, higher-frequency bands, infrastructure costs, vehicle emissions, and AI-based QoS requirements raise energy burdens.The paper links these pressures to the challenge of building sustainable vehicular communication and networking infrastructure.
- Paper scope: The review covers energy-efficient IoV techniques across communication, computation, traffic management, EV energy management, and energy harvesting.Its stated contribution is a comprehensive comparison of techniques from networking and computation through traffic and energy management.
- Contributions: The paper introduces promising 6G-enabled scenarios, discusses current challenges and emerging networking technologies, and identifies future directions for sustainable IoV.Examples include satellite/UAV-aided V2X, dynamic energy harvesting, and AI-based EV charging decisions.
2 GREEN IOV SCENARIOS AND ARCHITECTURE
Green IoV scenarios combine communication, computing, traffic management, EV charging, and energy harvesting within architectures intended to improve energy utilization. The paper highlights both enabling technologies and the complexity and coordination challenges that limit efficient large-scale operation.
- Scenario overview: Green IoV encompasses communication and computing, intelligent traffic management, EV energy management, and energy harvesting and sharing.These scenarios are treated as distinct areas for reviewing research trends and challenges.
- Green communications: ISAC can use sensing and communication jointly to support better driving decisions, channel-state information, interruption recovery, and communication efficiency.The paper gives vehicle platooning as an example in which multihop V2V sensing supports speed and direction adjustment.
- Green computing: Cloud and edge computing can improve communication and computation energy utilization through resource scheduling, but offloading also increases transmission and processing energy.The trade-off is especially relevant for computation-intensive vehicular tasks.
- Green traffic management: AI-based traffic management can improve scheduling and fuel efficiency, but real-time collection and updating of global traffic information increases communication energy and operating cost.The paper therefore identifies low-complexity self-learning and adaptive-update algorithms as attractive for large-scale distributed networks.
- EV management: EV charging requires coordinated scheduling across multiple stations and vehicles while improving power-transfer efficiency and reducing infrastructure cost and vehicular impacts.The paper considers wired and wireless transfer and schemes such as EV-to-grid, EV-to-EV, and EV-to-UAV.
- Architecture and harvesting: Renewable-powered infrastructure, traffic and trajectory forecasting, collaborative energy management, and RF energy transfer can coordinate energy supply among IoV devices, RSUs, and EVs.The green SDN-based architecture places centralized control in the cloud, with distributed SDN edge nodes collecting traffic and request information.
3 GREEN IOV COMMUNICATIONS
Green IoV communications improve energy efficiency through coordinated infrastructure operation, transmission control, scheduling, routing, relaying, and caching. The section reviews these mechanisms across V2X communication scenarios and models their energy use.
- Communication requirements and challenges: 6G IoV communication must support massive connectivity and ultra-high-rate, reliable services while addressing energy efficiency in dynamic vehicular environments.The review highlights challenges involving beamforming, multi-radio coordination, NOMA resource allocation, and satellite/UAV-assisted V2X.
- Energy consumption model: The BS energy model combines sleep power, active-mode overhead, transmission power, usage rate, and operating state to evaluate infrastructure consumption.Switching idle BSs to sleep mode and optimizing user association and resource allocation can reduce energy consumption.
- Energy consumption model: Vehicle transmission energy depends on uplink power, channel gain, bandwidth, noise, interference, and transmission workload.The reviewed model links transmission rate and workload size to vehicle communication energy.
- Energy-efficient communication mechanisms: Communication efficiency can be improved by adjusting transmission rates, selecting time slots, using edge caching, and switching infrastructure nodes between sleep and active modes.These controls address both communication energy and the energy required to keep IoV infrastructure operational.
- Energy-efficient communication mechanisms: Sleep strategies at access points can save about 80% of transmission energy during off-peak hours.The result concerns proactive and reactive random sleep strategies at an access point.
- Relay-assisted scheduling and edge caching: Relay selection, multihop forwarding, and edge-device caching reduce communication energy by exploiting favorable locations, shorter links, and suitable backup resources.The reviewed approaches include minimum-cost-flow, cluster-based, greedy, offline, online, and finite-window scheduling methods.
4 GREEN IOV COMPUTATION
Green IoV computation examines how vehicles and edge/cloud servers divide workloads while accounting for communication, computation, and resource constraints. The surveyed approaches cover local execution, offloading, workload sharing, UAV-assisted edge servers, and vehicle-provided computing.
- Energy consumption model: Local execution consumes computation energy at the vehicle, whereas offloading indivisible workloads adds transmission energy and edge-server computation energy.For divisible tasks, vehicles and edge servers can share the computation workload.
- Optimization problems: Offloading decisions, workload sharing, and communication and computation resource allocation are central factors affecting system energy consumption and efficiency.Limited vehicle and edge-server capacities make resource allocation critical.
- Vehicular edge computing: Vehicular edge computing enables computation-intensive tasks to be offloaded to roadside, cloud, UAV, or vehicle-based servers to save vehicle energy.Offloading can reduce vehicle-side energy, but transmission and server processing add energy costs.
- UAV-assisted edge computing: UAV-assisted computing jointly considers UAV trajectory, computation offloading, transmission power, and channel allocation to improve communication throughput and energy efficiency.UAV battery limitations and wireless charging further complicate resource and energy management.
- Vehicle-based edge computing: Vehicles can act as dynamic edge servers, but incentives are required because processing nearby users’ tasks creates substantial energy costs.Contract mechanisms can associate users’ required resources with vehicles’ rewards.
5 GREEN TRAFFIC MANAGEMENT
Green traffic management models energy use through vehicle speed, load, type, road conditions, and route characteristics. The surveyed approaches address signal timing, routing, driving behavior, and joint traffic optimization using real-time traffic information.
- Traffic-management challenges: Real-time green traffic management is difficult because large heterogeneous networks make global information retrieval challenging and frequent strategy updates energy-consuming.The problem is intensified by highly dynamic vehicular networks.
- Survey scope: Green traffic management approaches include traffic signal timing, green vehicular routing, driving behavior control, and joint optimization of these strategies.Table 3 surveys the main approaches across these categories.
- Energy consumption models: Fuel-consumption models represent energy use using variables such as vehicle speed, load, vehicle type, road grade, rolling resistance, and route length.The surveyed literature adopts different models for different traffic scenarios.
- Energy consumption models: Road-based energy models can incorporate sensor-level vehicle speed and segment distance, with road coefficient ϕ related to road grade and rolling resistance.The road grade can be calculated from road altitude and length information obtained through GPS.
X. Ge, 2014 [31]
Green traffic management reduces vehicular energy use through traffic-signal coordination, speed control, route planning, and their joint optimization. These approaches use real-time traffic information collected through V2I and V2V communications to manage congestion, fuel consumption, emissions, and travel delay.
- Traffic-energy factors: Urban stop-and-go traffic increases energy consumption through frequent acceleration, braking, static inertia, and sliding friction.Smooth driving primarily consumes energy overcoming rolling friction and wind resistance.
- Traffic-signal management: V2X-collected vehicle types, speeds, locations, and acceleration data support fuel-consumption models and traffic-signal timing decisions.Optimization methods include iterative search, heuristic, and learning-based approaches.
- Traffic-signal management: Coordinated traffic control improves global throughput and reduces average waiting time and total traffic-related costs across adjacent intersections.An aging process updates road-segment priority to maintain fairness and support green-wave driving on arterial roads.
- Green vehicle routing: Green vehicle routing targets congestion, CO2 emissions, and overall energy use by selecting routes according to traffic, road conditions, vehicle loads, and driving behavior.Compatibility prediction with five machine-learning techniques achieved 2 to 3 times higher packet delivery performance in the evaluated datasets.
- Speed control: Signal-aware speed selection helps vehicles pass intersections smoothly, while practical fuel-consumption models improve speed planning across multiple adjacent intersections.V2X infrastructure can broadcast signal-cycle information, and traffic data can support prediction of intersection delay and passing probability.
- Joint optimization: Joint optimization of signal timing, vehicle trajectories, routing, and driving behavior is presented as more promising for green transportation than isolated controls.Combining coordinated traffic-light control with cooperative routing reduces acceleration frequency between intersections and thereby decreases fuel consumption.
6 GREEN ENERGY MANAGEMENT FOR EVS
This section surveys EV energy management from consumption modeling and driving optimization to EV-to-grid and EV-to-EV charging. It compares model-based, learning-based, and charging-management approaches while identifying practical limitations and deployment challenges.
- EV Energy Consumption Model: EV energy consumption models account for vehicle, road, and traffic factors to support accurate estimation and energy-efficient route or driving design.Factors include vehicle powertrain and efficiency parameters, speed profiles, traffic signals, road grade, kinetic-energy changes, rolling resistance, aerodynamic resistance, accessories, and internal losses.
- EV Energy Consumption Model: A traction-energy model decomposes consumption into kinetic and potential-energy changes, rolling and aerodynamic resistance, and internal energy loss.The model expresses traction energy as Etractive = ∆Ekinetic+∆Epotential+Erolling+Eaero+Eloss.
- EV Energy Consumption Model: Regression-based energy-rate estimation uses cumulative positive and negative kinetic-energy changes together with vehicle mass and fitted coefficients.PKE and NKE represent cumulative positive and negative changes in kinetic energy rate, respectively.
- EV Energy Efficiency Optimization: Energy-efficiency optimization adjusts vehicle speed, driving behavior, route, and power management according to vehicle type and current traffic conditions.For EVs and HEVs, velocity and route are primary variables; PHEV strategies also emphasize engine-power management.
- EV Energy Efficiency Optimization: Dynamic programming can globally optimize EV energy management, but its heavy computation burden and need for future driving information motivate approximate online alternatives such as ADP and HDP.HDP combines nonlinear-function fitting with reinforcement learning to approach dynamic-programming solutions online.
- EV Charging Management: Charging-management research addresses station deployment, coordination, energy transfer, matching, user satisfaction, social welfare, and energy utilization across EV-to-grid and EV-to-EV systems.EV-to-EV studies use matching and cooperation, while wireless power transfer can charge vehicles while moving.
- EV Charging Management: EV energy-sharing strategies can improve renewable-resource utilization, reduce transmission losses or energy consumption, and increase network social welfare under evaluated scenarios.Greedy placement used fewer charging stations to cover a bus system, while diffusion-based placement produced lower energy-transmission losses; matching strategies reduced EV energy consumption and improved social welfare.
- EV Charging Management: EV-to-EV charging remains constrained by power-transmission efficiency, establishment cost, vehicular mobility, and seamless switching among charging facilities.These challenges limit the practical development of efficient EV charging management.
7 ENERGY HARVESTING AND SHARING
The paper surveys renewable and RF energy harvesting for green IoV, covering battery dynamics, energy sharing, infrastructure deployment, and resource management. It identifies coordinated harvesting, storage, transfer, and traffic scheduling as continuing design challenges.
- 7.2 Renewable Energy Sources: Energy harvesting supports IoV devices and infrastructure through renewable sources such as solar and wind, and RF conversion of electromagnetic signals into electricity.These approaches address energy constraints in wireless and IoT devices.
- 7.1 Energy Harvesting Model: The harvesting model represents energy as stochastic packet arrivals, with an i.i.d. assumption capturing the intermittent nature of renewable energy.Harvested energy is stored for computation or transmission in later time slots.
- 7.1 Energy Harvesting Model: Energy-harvesting offloading decisions must account for varying battery levels and coupled harvesting and consumption across successive time slots.Energy efficiency is evaluated over the whole scheduling period rather than independently at each slot.
- 7.5 Research Challenges: Future green IoV systems need coordinated energy management across harvesting sources, storage and distribution, energy transfer, and cooperative traffic scheduling.Real-time service requests, traffic lights, and road congestion complicate charging and discharging decisions.
- 7.3 Renewable Energy Management: Renewable-powered RSUs and EVs require strategies for energy storage, distribution, and transfer, including RF sales of surplus RSU energy to passing EVs.Solar- and wind-powered RSUs can also reduce deployment costs when energy-harvesting rates and RSU density are optimized.
- 7.4 Energy Cooperation and RF Harvesting: Adaptive traffic management and energy cooperation can adjust served traffic, base-station states, cell size, and user association according to renewable-energy availability.RF harvesting is less dependent on environmental conditions but requires energy-efficient allocation because dedicated transfer signals consume additional energy.
8 FUTURE RESEARCH TRENDS
Future green IoV research must address the energy and coordination challenges created by dense 6G infrastructure, heterogeneous networks, realistic EV conditions, and growing security requirements. The paper highlights cross-layer optimization, prediction, AI, and new protocols as key directions.
- 8.1 Green IoV Infrastructure: Dense deployment of cameras, radars, RSUs, MEC servers, UAVs, and base stations for autonomous driving will substantially increase IoV infrastructure energy consumption.The 6G era is expected to have more base stations than 5G networks.
- 8.2 Green Space-Aerial-Terrestrial-Sea Communication: SAGIN research needs cross-layer and end-to-end energy optimization across satellites, MEC-enabled HABs, UAVs, and vehicles.Heterogeneous hardware, time-varying channels, and time-sensitive requirements complicate efficient deployment and operation.
- 8.3 Joint Optimization for EV Charging: EV charging research must jointly consider charging-station and vehicle availability, fuel capacities, renewable-energy states, driving routes, traffic lights, and road congestion.AI-based real-time charging-path and charging-decision optimization remains subject to deployment, convergence, and privacy challenges.
- 8.4 Intelligent Energy Harvesting: Renewable-resource systems require prediction of future harvesting states and nearby devices’ real-time energy demands to optimize charging and discharging decisions.The paper also calls for joint design of intelligent harvesting frameworks and resource-utilization strategies.
- 8.5 Green Heterogeneous V2X Communication: Dense 6G base-station deployment will cause frequent user switching and increase energy consumption during communication and computation switching processes.Integrated interfaces and new protocols are proposed to reduce handover cost and improve energy efficiency.
- 8.6 AI-Enabled Green IoV: AI techniques can support adaptive traffic-light control and edge-server active/sleep switching by predicting traffic volumes, resource requirements, and workload arrivals.Federated learning can reduce data-transmission energy while enabling decentralized model training and knowledge sharing.
9 CONCLUSION
The paper surveys green IoV development across five scenarios and reviews energy-efficiency approaches, models, challenges, and open issues for 6G-enabled systems.
- 9 Conclusion: The survey covers V2X communication, vehicular edge computing, intelligent traffic management, EV energy management, and energy harvesting management.It reviews state-of-the-art approaches and challenges for improving energy efficiency in each scenario.
- 9 Conclusion: The paper discusses general energy-consumption models for V2X communication, vehicular edge computing, vehicle driving, EV charging, and energy harvesting.It also outlines open issues in developing 6G-enabled green IoV systems.