A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks
Abstract
:1. Introduction
1.1. Motivation
1.2. Contribution
2. The Evolution from Cloud Computing to MEC
2.1. Cloud Computing
2.1.1. Deployment Models
2.1.2. Service Models
2.2. Fog Computing
2.2.1. Architecture
- Tier 1—IoT Devices: This level mainly comprises user equipment such as smartphones, intelligent cars, etc. We call these devices Terminal Nodes (TNs). TNs might have features such as a Global Positioning System (GPS).
- Tier 2—Fog (middle layer): There is a special plane in this architecture called the fog computing plane. In this plane, there are devices such as routers, switches, and Access Points (APs). These devices not only exchange data but they can also share their storage space and computing power.
- Tier 3—Cloud: This faraway layer is equipped with extensive storage capacity and powerful computing resources that can handle a lot of information and perform complex tasks.
2.2.2. Applications
Features | Cloud Computing | Fog Computing |
---|---|---|
Server hardware | Large-scale data centers (including a significant number of highly capable servers) | Small-scale data centers (moderate or low resources) |
Server location | Far from end users, installed in large premises, accessed via wired Internet [41] | Located near the end users, communication via Wi-Fi, LTE, 5G, etc. |
Deployment cost | High, requiring complicated configuration and planning | Low, requires ad hoc deployment with or without planning |
Computing method | Centralized | Distributed or centralized |
Operated by | Large companies | Small or large companies |
System management | Centralized control | Hierarchical control |
Applications | Cyber-domain, time-tolerant, and high-intensity computation applications | Supports both cyber-domain and cyber-physical applications, specifically latency-sensitive applications |
Backhaul usage | Frequent use | Lower use, avoiding traffic congestion |
Latency control | Low | High |
Reliability | High | Low |
Maintenance | By technical experts | Requiring little or no human intervention |
2.3. Edge Computing
2.3.1. Definition of Mobile Edge Computing
2.3.2. Mobile Edge Computing Architecture
- Mobile Micro Cell (MMC)
- Small Cell Cloud (SCC)
- MobiScud
- Follow Me Cloud (FMC)
- ETSI Multiaccess Edge Computing
3. MEC Implementation in Vehicular Networks
3.1. User-Side Layer
3.2. MEC Layer
3.3. Core Network Layer
4. Key Technologies for MEC-Assisted Vehicular Networks
4.1. Software-Defined Networking
4.2. Digital Twin
5. Using MEC to Augment the Computing Capabilities of Vehicles
5.1. Task Offloading
5.2. Resource Allocation
6. Using MEC as Enabler for Vehicular Applications
6.1. Collision Avoidance
6.2. Platooning
6.3. Tele-Operated Driving
6.4. Video Streaming
7. Using Vehicles to Augment MEC Capabilities
8. Open Challenges and Research Directions
8.1. Stability and Interconnectivity
8.2. Task Segmentation and Migration
8.3. Using Artificial Intelligence
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Reference | Year | Main Contribution |
---|---|---|
[7] | 2018 | Various applications of MEC in different network areas of IoT |
[8] | 2019 | Architectures of MEC, use cases, and challenges in IoT |
[9] | 2022 | MEC-enabled 5G use cases, security |
[10] | 2023 | Mobility of IoT devices |
[11] | 2022 | Use cases and challenges of MEC in VNs |
[12] | 2022 | Fundamentals, enablers, and challenges of MEC in IoT |
[13] | 2019 | Mobility issues of both content-caching nodes and end devices |
[14] | 2022 | MEC for V2X architectures and applications |
[15] | 2023 | Comparison of offloading strategies in MEC |
[16] | 2023 | Task-offloading algorithms and optimization approaches in MEC |
[17] | 2023 | A review of the research status of MIMO-MEC networks |
This work | End of 2023 | Using MEC for task offloading in V2X (Section 5); using MEC to enable V2X applications (Section 6); using vehicles to enhance MEC (Section 7) |
Reference | Methodology | Key Metrics | Computing Method | Focused Infrastructure | Mobility Consideration |
---|---|---|---|---|---|
[96] | IoV with imperfect CSI—using DRL | Computation delay and energy consumption | MEC server | V2I | No |
[97] | Network slicing | Delay | Local—MEC server | V2V-V2I | Yes |
[99] | Multiuser MEC assisted | Delay and energy consumption | Local—MEC server | V2V-V2I | No |
[100] | A heuristic task migration | Delay | Local—MEC server | V2I | Yes |
[101] | Distributed task-offloading framework—DDQN optimization | Delay and resource management | Local—MEC server | V2I | Yes |
[103] | NOMA and MEC merging | Delay and resource utilization | Local | V2V | Yes |
[104] | Task offloading using parked and moving vehicles | Processing delay | MEC server—cloud server | V2I-I2I | No |
[105] | Multihop task offloading | Service-delay prediction | MEC server—cloud server | V2I | No |
[106] | Decentralized DRL—task offloading with game theory algorithm | Latency and energy consumption | MEC server | V2I | No |
[107] | Generic MEC system—a hybrid DQN and optimization approach for task-offloading strategy | Resource consumption | Local—MEC server | V2V-V2I | Yes |
[108] | Probabilistic scheduling approach—two-dimensional Markov chain | Load balancing | MEC server | V2I | No |
[109] | Computation offloading | Latency and overhead | Local—MEC server | V2I | No |
Ref. | Type of Problem | Objective Function | Number of Variables | Number of Constraints | Algorithm Used |
---|---|---|---|---|---|
[96] | Convex | Minimizing the total overhead | DRL and Lagrangian multiplier | ||
[97] | Integer linear | Maximizing the accepted offloading tasks | Network slicing and load balancing, VECSlic-LB | ||
[99] | Non-convex | Minimizing response time of the task | Learning-based task-offloading framework based on the multiarm bandit | ||
[100] | Convex | Diminishing average delay | Task migration cooperative offloading | ||
[101] | Non-convex | Minimizing the total system delay | DRL-DDQN | ||
[105] | Mixed-integer linear | Minimizing execution time and computation cost | Quadratically Constrained Quadratic Programming (QCQP) problem and semidefinite relaxation | ||
[108] | Linear | Minimizing cost | Markov chain | ||
[109] | Non-convex | Minimizing the averageprocessing delay | DQN and RMSProp optimizer | ||
[110] | Integer linear | Maximizing success offloading tasks and minimizing service delay | Convolutional Neural Network (CNN) |
Reference | Methodology | Key Metrics | Focused Component |
---|---|---|---|
[115] | Real-time energy-aware offloading using MINLP | Energy consumption | Vehicles |
[116] | Resource management optimization with DRL | Network traffic and overhead | MEC server and vehicles |
[117] | Threshold-based computation resource configuration | Computation resource demand and processor power | MEC server |
[122] | Adaptive resource allocation | Processing capacity, radio resources | MEC server |
[123] | SDN-enabled IoV resource allocation with PSO algorithm | Processing capacity, radio resources | MEC server and vehicles |
[124] | A contract-Stackelberg approach | Idle resources of vehicles | Vehicles |
[125] | Dynamic resource management with MDP | Dynamic network conditions | MEC server |
[127] | Mobility-aware resource allocation based on matching theory | Mobility of users and constrained resources | MEC server |
[130] | Intelligent offloading and caching strategy | Energy efficiency | MEC server and vehicles |
[131] | Mobility-aware EECOC task scheduling with DRL | Task latency | MEC server |
Reference | Methodology | Computing Place | Focused Connection |
---|---|---|---|
[68] | Safety management in tele-operated driving | MEC server | V2I |
[135] | Detecting and solving road hazards | Vehicle, MEC server, cloud server | V2I |
[137] | Detecting car-to-pedestrian hazards | User cellphone | V2V-V2I |
[138] | Detecting vulnerable road users, avoiding car accidents | MEC server, cloud server | V2X |
[139] | Crash anticipation-assisted steering system | MEC server | V2I |
[144] | Detecting car-to-pedestrian hazards | MEC server, vehicles, user cellphone | V2X |
[146] | Assigning processing resource to collision detection system | MEC | V2I |
[147] | Platooning, cooperative adaptive cruise control | Vehicle, MEC server | V2V-V2I |
[148] | Platooning, optimizing task offloading and resource allocation | MEC server | V2V-V2I |
[149] | Platooning, conserving fuel, and increasing traffic effectiveness | MEC server | V2I |
[150] | Platooning, short delay, and high reliability | Vehicle, MEC server, cloud server | V2V-V2I |
[151] | Platooning resource allocation | MEC server | V2I |
[152] | Platooning, cooperative adapted driving | Vehicle, MEC server, cloud server | V2V-V2I |
[154] | Platooning, cooperative adaptive cruise control | Vehicle, MEC server | V2V-V2I |
[155] | Infrastructure-focused platoon management | Vehicle, MEC server | V2V-V2I-V2N |
[156] | Centralized control of a platoon | MEC server, cloud server | V2I |
[158] | Generating and controlling behaviors of the platooning vehicles | Vehicle, MEC server | V2V-V2I |
[159] | Migration of the platoon controller | MEC server | V2I |
[162] | Controlling power of task offloading for vehicular platooning | MEC server | V2I |
[168] | Tele-operated transport approach | MEC server | V2I |
[172] | Video streaming | MEC server | V2V-V2I |
[173] | Video streaming with caching mechanism | MEC server | V2I |
[175] | Video streaming with bandwidth saving and reduced data communication times | MEC server | V2I |
[176] | Video streaming with a buffering method | MEC server | V2I |
[177] | Video streaming clustered video caching | Vehicle, MEC server | V2V-V2I |
[179] | Video streaming with hierarchical cooperative cache architecture | Vehicle, MEC server | V2V-V2I |
[180] | MEC cache for 360-degree video streaming | MEC server | V2I |
Reference | Methodology | Mobility Consideration | Focused Connection |
---|---|---|---|
[101] | Cooperative vehicle-assisted task offloading | Yes | V2I |
[185] | Cooperative task offloading with vehicles, MEC, and cloudlet | Yes | V2V-V2I |
[186] | Offloading service with best-relaying vehicle to requesting vehicle | Yes | V2X |
[188] | Cooperative task offloading to VEC and MEC servers | Yes | V2I |
[189] | Collaborative EdgePV, parked vehicle resources | No | V2X |
[184] | Moving vehicles with a resource pool | Yes | V2I |
[190] | Collaborative EdgeGA, parked vehicle resources | No | V2V-V2I |
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Pashazadeh, A.; Nardini, G.; Stea, G. A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks. Future Internet 2023, 15, 391. https://doi.org/10.3390/fi15120391
Pashazadeh A, Nardini G, Stea G. A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks. Future Internet. 2023; 15(12):391. https://doi.org/10.3390/fi15120391
Chicago/Turabian StylePashazadeh, Ali, Giovanni Nardini, and Giovanni Stea. 2023. "A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks" Future Internet 15, no. 12: 391. https://doi.org/10.3390/fi15120391