A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing

Area of Science:

• Computer Science
• Network Security
• Transportation Systems

Background:

• The Internet of Vehicles (IoV) paradigm, built on Vehicular Ad-hoc Networks (VANETs), faces significant security and privacy challenges, notably location tracking via safety messages.
• Existing location privacy schemes often use silent periods but compromise safety by not optimizing their timing.

Purpose of the Study:

• To propose a novel location privacy scheme, OVR, that enhances privacy without sacrificing safety in IoV environments.
• To leverage public transport and designated overseer vehicles to manage silent periods effectively.

Main Methods:

• The OVR scheme designates certain vehicles (overseers) to maintain communication and ensure safety while allowing other vehicles to enter silent periods for enhanced privacy.
• The scheme integrates real-time road congestion estimation using On-Board Units as mobile edge servers, communicating with static edge servers.

Main Results:

• OVR effectively enhances location privacy by strategically employing silent periods managed by overseer vehicles.
• The integrated road congestion estimation improves connectivity and reduces network latency.
• Comparative analysis in urban and highway models demonstrates the beneficial performance of OVR over existing schemes.

Conclusions:

• The OVR scheme offers a balanced solution for location privacy and safety in the IoV.
• It presents a practical approach to mitigating location tracking risks while supporting essential network functions like congestion estimation.