Autonomous vehicles' forensics in smart cities

Xiaohua Feng; Edward Dawam; Dayou Li

doi:10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301

Autonomous vehicles' forensics in smart cities

Xiaohua Feng
, Edward Dawam
, Dayou Li

Institute for Research in Applicable Computing (IRAC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

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.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1688-1694
ISBN (Print)	9781728140346
DOIs	https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301
Publication status	Published - 2 Apr 2020
Event	IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation - Leicester Duration: 19 Aug 2019 → 23 Aug 2019

Conference

Conference	IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
City	Leicester
Period	19/08/19 → 23/08/19
Other	IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (19/08/2019-23/08/2019, Leicester)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Application programming Interface Keys (API Keys)
Autonomous Vehicles (AV)
Cyber security
Internet of Things (IoT)
Smart Cities (SC)
Smart City Infrastructure (SCI)
sensors

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10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301

https://ieeexplore.ieee.org/document/9060127

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Cite this

@inproceedings{f8000bbb17f64f42b6e162c1ef774406,

title = "Autonomous vehicles' forensics in smart cities",

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.",

keywords = "Application programming Interface Keys (API Keys), Autonomous Vehicles (AV), Cyber security, Internet of Things (IoT), Smart Cities (SC), Smart City Infrastructure (SCI), sensors",

author = "Xiaohua Feng and Edward Dawam and Dayou Li",

year = "2020",

month = apr,

day = "2",

doi = "10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301",

language = "English",

isbn = "9781728140346",

pages = "1688--1694",

booktitle = "nan",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE SmartWorld, Ubiquitous Intelligence \& Computing, Advanced \& Trusted Computing, Scalable Computing \& Communications, Cloud \& Big Data Computing, Internet of People and Smart City Innovation ; Conference date: 19-08-2019 Through 23-08-2019",

}

Feng, X, Dawam, E & Li, D 2020, Autonomous vehicles' forensics in smart cities. in nan. Institute of Electrical and Electronics Engineers Inc., pp. 1688-1694, IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, Leicester, 19/08/19. https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301

TY - GEN

T1 - Autonomous vehicles' forensics in smart cities

AU - Feng, Xiaohua

AU - Dawam, Edward

AU - Li, Dayou

PY - 2020/4/2

Y1 - 2020/4/2

N2 - 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.

AB - 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.

KW - Application programming Interface Keys (API Keys)

KW - Autonomous Vehicles (AV)

KW - Cyber security

KW - Internet of Things (IoT)

KW - Smart Cities (SC)

KW - Smart City Infrastructure (SCI)

KW - sensors

U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301

DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00301

M3 - Conference contribution

SN - 9781728140346

SP - 1688

EP - 1694

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation

Y2 - 19 August 2019 through 23 August 2019

ER -

Autonomous vehicles' forensics in smart cities

Abstract

Conference

UN SDGs

Keywords

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