TY - GEN
T1 - Jammer Localization in the Internet of Vehicles: Scenarios, Experiments, and Evaluation.
AU - Hussain, Ahmed
AU - Abughanam, Nada
AU - Sciancalepore, Savio
AU - Yaacoub, Elias
AU - Mohamed, Amr
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/11/7
Y1 - 2022/11/7
N2 - The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions.
AB - The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions.
KW - Internet of Vehicles
KW - Jammer Localization
KW - Jamming
KW - Physical-Layer Security
KW - Vehicular Communications
KW - Wireless Communications
UR - http://www.scopus.com/inward/record.url?scp=85146567524&partnerID=8YFLogxK
U2 - 10.1145/3567445.3567463
DO - 10.1145/3567445.3567463
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 73
EP - 80
BT - IoT 2022 - Proceedings of the 12th International Conference on the Internet of Things 2022
ER -