The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV
Abstract
:1. Introduction
2. Background
2.1. VANET
2.2. FANET
2.3. IoV
2.4. IoFV
3. Building Blocks of Vehicular Networks
3.1. Sensors
3.2. Onboard Units
3.3. Base Stations
3.4. Clouds
4. Layered Models
4.1. Sensory Layer
4.2. Communication Layer
4.3. Cognitive Layer
4.4. Application Layer
5. Communication in Vehicular Networks
5.1. Communication Models
5.2. Communication Types
- Direct communication: The usage of direct communication is possible in cases when vehicles are intended to communicate with base stations. In this scenario, vehicles are not able to communicate directly with each other due to the centralization of a network [66]. This type uses base stations as central nodes, thereby simplifying network architectures. A significant disadvantage is the possible failure of a central node, which would result in a failure of the whole network. Furthermore, due to limiting the communication only to a central node, the network may be prone to performance issues such as bottlenecks and is not suitable for dynamic environments [67].
- Satellite and cellular networks: Communication via satellite and cellular networks plays a vital role in interconnecting multiple vehicles. While satellite networks are suitable for communication in geographically distant nodes and find their use especially in flying vehicle networks, their use is also viable for ground vehicles in areas with sparse infrastructure and outside of cities, where there may not be other means of communication either directly to other vehicles or to an underlying infrastructure in the case that a vehicle is located outside of the coverage of cellular or other wireless networks [68]. On the other hand, cellular network coverage can provide fast and seamless communication in areas with sufficient coverage and is widely used in both ground and aerial vehicles [69]. Compared to satellite networks, cellular networks offer greater data transfer speed, lower latency—which is especially true in new generation networks such as 5G or beyond-5G networks—and a lower cost of operation [70].
- Ad hoc networks: In order to mitigate the disadvantages of the previous methods of communication, vehicular networks implement ad hoc networks. This approach was the primordial method of communication in earlier vehicular networks. Building on the MANET, nodes in the network with this type of communication enable nodes to communicate directly with each other without a need for centralization of the network’s components [71]. The usage of such a method is convenient, especially with more vehicles, with each viewed as an end system in proximity, thereby allowing them to remain connected even in the case that a vehicle reaches a point without a coverage of centralized components [72]. This method finds applications in both ground and aerial vehicle networks. Even though this approach to communication has several benefits in comparison to the previously discussed methods, its usage may be hindered by the rapidly changing dynamic topology of such a network. To alleviate those drawbacks, some works are focusing on designing routing protocols for those networks with the goal of fast and reliable delivery of data while lowering the cost of operation and energy consumption [51].
5.3. Clustering in Communication
- Discovery: In this stage, vehicles joining the network periodically broadcast messages reporting their state as unclustered along with the data required to assign the vehicle to a cluster.
- CH selection: Upon receiving broadcast messages from neighboring nodes, a vehicle will elect an appropriate node based on the gathered data.
- Notification: After a CH is elected, it announces its state to the other vehicles by broadcasting to the unclustered nodes.
- Association: Subsequently, other nodes request to join the cluster and change their state to clustered.
- Maintenance: Both the CH and CM monitor the communication state with each other. If the link is lost, a CM will change its state to unclustered and attempt to join a different cluster.
5.4. Routing
Algorithm | WoS | Scopus | Year | Citations WoS | Citations Scopus | Reference |
---|---|---|---|---|---|---|
PSO | Yes | Yes | 2019 | 1 | 1 | [91] |
PSO | Yes | Yes | 2021 | 1 | 1 | [92] |
PSO | Yes | Yes | 2021 | 2 | 3 | [93] |
PSO | Yes | Yes | 2020 | 2 | 3 | [94] |
PSO | Yes | Yes | 2021 | 6 | 8 | [95] |
PSO | Yes | Yes | 2020 | 6 | 8 | [96] |
F-Ant | Yes | Yes | 2018 | 29 | 39 | [97] |
Glow-Worm Swarm | Yes | Yes | 2022 | 1 | 0 | [98] |
Glow-Worm Swarm | Yes | Yes | 2018 | 5 | 4 | [99] |
ACO | Yes | Yes | 2022 | 18 | 27 | [100] |
Firefly | Yes | Yes | 2019 | 6 | 5 | [101] |
6. Computing in Vehicular Networks
6.1. Onboard
6.2. Edge and Fog
6.3. Cloud
7. Security
- Authentication is the verification of a user’s identity so that the person or system trying to access resources is actually who they say they are.
- Authorization is the granting of permission to access resources. If a person is authenticated, the authorization process determines which parts of the system and resources are accessible to that person and what actions he or she can perform.
- Auditing is the process of recording information about who logged into the system and when, which resources were used, and what actions were performed. It is therefore about monitoring user activity and preventing illegal activities.
7.1. Countermeasures
- Wi-Fi jamming: This is the most common method used, and it operates at a frequency of 2.4 GHz. It is the jamming of wireless transmission signals sent between devices connected to a given Wi-Fi network [142]. It is also used, for example, to protect the network from unauthorized access. The disadvantage is that such interference is visible and can be easily detected. Also, the interference is quite limited, and nearby frequencies are interfered with in addition to the target frequency [143].
- Replay: This attack consists of repeated data transmission between two devices without the data being modified in any way. The attacker intercepts the original communication and later retransmits it to gain unauthorized access [144]. For connected vehicles, it can also be used to break the encryption key while replaying the ARP protocol. It is mainly used when the connection between the vehicle and other devices is secure [145].
- Buffer overflow: This is an attack intended to fill the vehicle’s memory buffer with more data than it can process. Such data overflow can cause the vehicle to malfunction and execute arbitrary code, which is exploited to gain control of the device [146].
- DoS: This is performed either by deauthentication or Wi-Fi jamming, which causes devices in the vehicular networks to crash [147].
- ARP cache poisoning (ARP spoofing): An attacker sends spoofed ARP messages into the network in order to deceive other devices trying to communicate with the device. These spoofed ARP messages contain a physical MAC address to which other devices will send their messages. However, this MAC address is spoofed and it is the attacker’s device, which gives him access to the data being sent and allows him to modify it and send it on. In vehicular networks, it is used to send malicious scripts repeatedly [148].
7.2. Threats
- GPS signal manipulation: The GPS signal is crucial for navigation applications. It is transmitted by satellites orbiting the Earth. However, there is a vulnerability on Earth known as“GPS spoofing.” This refers to the act of generating a GPS signal to manipulate the GPS receiver in a target device. The person behind the spoofing has access to information about the GPS signal from satellites. The person uses it to create a manipulated version, thus giving them control over the target device [149]. This poses a risk for vehicles (UAVs) and unmanned ground vehicles that follow predetermined routes [150]. GPS spoofing could potentially lead to the theft of assets or result in unsafe cargo delivery, such as biological weapons or explosives.
- Malware and data interception: The wireless and remote control methods used for piloting UAVs and unmanned ground vehicles are not completely secure. Hackers can secretly implant a message into the vehicle’s memory by installing malware without detection in the system that controls the ground station [151]. Additionally, since these vehicles often monitor objects, their unsecured wireless transmission of sensor data makes them susceptible to hackers inserting malware.
- Wi-Fi interference: When deauthentication processes occur between an access point and a vehicle controller, it opens up opportunities for hijacking UAVs and unmanned ground vehicles. When hackers disrupt the Wi-Fi frequency of a drone, they manipulate it to connect to a network without authorization [152].
- Technical issues and nature: There are factors that can lead to crashes of UAVs and unmanned ground vehicles, such as the loss of connectivity, inexperienced piloting, or unfavorable weather conditions. These incidents can cause damage to property. Ref. [153] studied how these risks can even result in injuries. Additionally, technical problems like motors overheating or batteries exploding in temperatures can pose risks. Inadequate security measures in the design of these vehicles may also contribute to the loss of control, thus further escalating harm [154].
- Privacy concerns: As the popularity of UAVs and unmanned ground vehicles increases, so does the concern surrounding privacy [155]. Equipped with cameras and sensors, these vehicles have the capability to capture high-quality images and collect data that may intrude upon an individual’s privacy without their consent. Privacy concerns extend further when considering the risk of hackers gaining control over a vehicle’s cameras and gaining access to data from military zones or private residences for purposes such as identity theft, blackmailing, or illegal activities [156].
8. Use Cases and Applications
8.1. Agriculture
8.2. Warehouses and Logistics
8.3. Healthcare
8.4. Space Exploration
8.5. Search and Rescue
8.6. Other Applications of Drones and Autonomous Ground Vehicles
- Tourism, commerce, and cinematography: Drones play a pivotal role in the realms of tourism [181], commerce, and cinematography [182]. They provide high-quality images, thereby offering new perspectives to attract tourists and promote destinations at a whole new level. In cinematography, drones offer a cost-effective means to capture special shots that might be physically and financially challenging with alternative equipment.
- Underwater operations: Both aerial drones and unmanned underwater vehicles (UOVs) contribute significantly to monitoring marine life. Aerial drones can be used to survey coastlines and assess environmental conditions, while underwater drones are employed in surveys, checking underwater infrastructure, and searching for oil and gas. The advantage of underwater drones lies in their ability to withstand the dangerous and harsh conditions beneath the water’s surface. Advancements in this technology are anticipated, thereby enabling more sophisticated research and exploration of deep sea locations [183].
- Traffic monitoring: Drones and autonomous ground vehicles have proven effective in traffic and accident monitoring, thus quickly providing necessary footage through high-quality cameras [184]. They find applications in areas with a high frequency of accidents, thereby assisting in assigning blame or facilitating decision making. Beyond traffic, these autonomous systems are utilized to monitor crowds at protests or refugee movements, therbey contributing to public safety [185].
- Military applications: In military contexts, both drones and autonomous ground vehicles serve various purposes, from simple surveillance and reconnaissance to more complex tasks. They play a role in targeted killing through artificial intelligence, thereby reducing the risk to personnel. However, concerns about the misuse of such technologies for personal gain, potentially leading to property damage or harm to innocent lives, have been raised.
9. Challenges
9.1. Regulations
9.2. Integration
10. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AU | Application Unit |
UAV | Unmanned Aerial Vehicle |
VANET | Vehicular Ad Hoc Network |
MANET | Mobile Ad Hoc Network |
FANET | Flying Ad Hoc Network |
WLAN | Wireless Local Area Network |
IoT | Internet of Things |
DSRC | Dedicated Short-Range Communication |
IoV | Internet of Vehicles |
IoFV | Internet of Flying Vehicles |
5G | Fifth-Generation Network |
V2 | Vehicle-to-Vehicle |
V2I | Vehicle-to-Infrastructure |
V2X | Vehicle-to-Everything |
CCAS | Cooperative Collision Avoidance System |
CAS | Collision Avoidance System |
C-ITS | Cooperative Intelligent Transportation Systems |
OBU | Onboard Unit |
RSU | Roadside Unit |
CH | Cluster Head |
CM | Cluster Member |
CG | Cluster Gateway |
ITS | Intelligent Transportation System |
GPS | Global Positioning System |
LiDAR | Light Detection and Ranging |
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Feature | VANET | FANET | IoV | IoFV |
---|---|---|---|---|
Type of network | Vehicular | Flying | Vehicular | Flying |
Communication range | Short | Medium to Long | Short to Medium | Medium to Long |
Mobility | Road-based | Air-based | Road-based | Air-based |
Infrastructure | Roadside units | Limited ground infrastructure | Roadside units, Cloud | Ground stations, Cloud |
Challenges | Signal obstruction, | Limited energy, | Signal interference, | Limited energy, |
Interference, | Collision avoidance | Dynamic topology changes, | Communication security concerns | |
Dynamic topology changes | Security concerns | |||
Applications | Intelligent Transportation Systems, | Surveillance, | Traffic management, | Surveillance, |
Emergency services | Environmental monitoring | Fleet management, | Environmental monitoring, | |
Safety applications | Disaster response |
Reference | Layers | Applications |
---|---|---|
[44] | Data, Virtualization, Control, Application | See-Through, Collision Warning |
[45] | Sensing, Communication, Cognition, Control, Application | Safety, Transportation Management |
[46] | Perception, Communication, Application | Vehicle Collaboration |
[47] | User, Data Acquisition, Filtering, Communication, Control, Processing | Traffic Efficiency, Safety |
System | Algorithm | WoS | Scopus | Year | Citations WoS | Citations Scopus | Reference |
---|---|---|---|---|---|---|---|
EC-MOPSO | PSO | Yes | Yes | 2022 | 2 | 1 | [74] |
MADCR | MOA | Yes | Yes | 2021 | 7 | 11 | [75] |
AFS Clustering | AFS | No | Yes | 2020 | - | 3 | [76] |
CACOIOV | ACO | Yes | Yes | 2019 | 13 | 19 | [77] |
MFCA-IoV | MFO | Yes | Yes | 2019 | 31 | 38 | [78] |
CAVDO | DFO | Yes | Yes | 2018 | 52 | 64 | [79] |
Parameter | EC-MOPSO | MADCR | AFS Clustering | CACOIOV | MFCA-IoV | CAVDO |
---|---|---|---|---|---|---|
Grid size (km × km) | 1 | 1, 2, 3, 4 | 1 | 4 | 1, 2, 3, 4 | 1, 2, 3, 4 |
Vehicles (count) | 50–100 | 30–60 | 30–60 | 40–200 | 30–60 | 30–60 |
Speed (km/h) | 30–100 | 72–108 | 30–50 | 79.2–108 | 79.2–108 | 79.2–108 |
Communication Range (m) | 200 | Dynamic | 100–600 | Dynamic | Dynamic | Dynamic |
Mobility Model | Urban | Freeway | Urban | Freeway | Freeway | Freeway |
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Herich, D.; Vaščák, J. The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV. Drones 2024, 8, 34. https://doi.org/10.3390/drones8020034
Herich D, Vaščák J. The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV. Drones. 2024; 8(2):34. https://doi.org/10.3390/drones8020034
Chicago/Turabian StyleHerich, Dušan, and Ján Vaščák. 2024. "The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV" Drones 8, no. 2: 34. https://doi.org/10.3390/drones8020034
APA StyleHerich, D., & Vaščák, J. (2024). The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV. Drones, 8(2), 34. https://doi.org/10.3390/drones8020034