What Is the Internet of Vehicles?

The Internet of Vehicles (IoV) is a distributed network in which vehicles, roadside infrastructure, pedestrian devices, and cloud services exchange data continuously so that each participant can act on information gathered beyond its own sensors. The concept extends the Internet of Things to a population of nodes that move at highway speed, join and leave the network within seconds, and generate data with strict latency requirements. The term marks a shift away from earlier vehicular ad hoc networks, which connected vehicles to each other in isolation, toward an architecture that couples the vehicle to edge servers, cellular core networks, and traffic management systems.

The subject draws on wireless communication, distributed computing, control theory, and transportation engineering. A vehicle in an IoV deployment is simultaneously a sensor platform producing camera, radar, and telemetry streams, a consumer of maps and traffic state, and a relay for neighboring nodes. Managing that role under fast topology change, intermittent connectivity, and safety-critical timing constraints defines most of the technical work in the field.

Communication Layers and V2X

Connectivity is described by the vehicle-to-everything family of link types: vehicle to vehicle, vehicle to infrastructure, vehicle to pedestrian, and vehicle to network. Two radio access technologies compete and increasingly coexist. Dedicated short-range communication, built on IEEE 802.11p and its successor IEEE 802.11bd, provides direct low-latency broadcast without network infrastructure. Cellular V2X, standardized by 3GPP and carried forward into 5G New Radio sidelink, supports both direct device-to-device transmission and connections through base stations, with quality of service handling for safety messages. A survey of road safety and traffic efficiency applications built on C-V2X compares how these technologies serve collision warning, platooning, and signal phase notification, and work on 5G-based V2X ecosystems describes the network slicing and edge deployment needed to meet automotive latency budgets.

Edge Computing and Data Architecture

A connected vehicle generates far more data than it can send to a distant data center in useful time. IoV architectures therefore place computation at roadside units and base station edge servers, where cooperative perception, map updates, and trajectory prediction can be run within a few tens of milliseconds. Federated learning is widely studied as a way to train shared models across fleets without moving raw sensor data off the vehicle. Task offloading decisions, which balance onboard processing against edge capacity, energy use, and link quality, form a large optimization literature. Energy efficiency has become its own design axis, examined in research on green Internet of Vehicles for the 6G era.

Security, Privacy, and Trust

Because a false message can cause a physical collision, IoV security requirements exceed those of most consumer networks. Deployed systems use public key infrastructure with short-lived pseudonymous certificates, which authenticate a sender while preventing long-term tracking of a vehicle by its identifiers. Misbehavior detection adds a second layer, checking whether a claimed position and speed are physically plausible against sensor observations, and defends against Sybil attacks in which one node fabricates many identities. In-vehicle network security matters equally, since a compromised telematics unit can reach the controller area network bus that operates braking and steering.

Applications

Internet of Vehicles has applications across transportation and adjacent fields, including:

• Cooperative collision warning, intersection movement assist, and emergency braking alerts
• Truck platooning and cooperative adaptive cruise control
• Traffic signal coordination and dynamic route guidance
• Cooperative perception that extends a vehicle's view around occlusions
• Fleet telematics, usage-based insurance, and predictive maintenance
• Electric vehicle charging coordination and grid interaction