| Title |
Autonomous Vehicles' Forensics in Smart Cities |
| ID_Doc |
42594 |
| Authors |
Dawam, ES; Feng, XH; Li, DY |
| Title |
Autonomous Vehicles' Forensics in Smart Cities |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301 |
| Abstract |
Autonomous vehicles (AVs) are capable of sensing their environment and navigating without any human inputs. When accidents occur between AVs, road infrastructures, or human subjects, liability is decided based on accident forensics. This accident forensics is carried out by the acquisition of sensor data generated within the AVs and through its communication between vehicles to a vehicle (V2V) and vehicle to infrastructure (V2I) with a centralised data hub in smart cities that collects and stores this data thereby aiding the relevant authorities in informed decision making. However, practices mostly employed in extracting this information are unprofessional when compared to other areas of digital forensics. In this paper, we designed and implemented a non-invasive mechanism for the collection and storage of forensic data from AVs within smart cities. This mechanism is efficient, secure, and preserves the privacy of data generated by the AV. |
| Author Keywords |
Autonomous Vehicles (AVs); Smart City (SC); Smart City Infrastructure (SCI); Internet of Things (IoT); Sensors; Application Programming Interface keys (API Keys); Cyber Security |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000936421900250 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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