TY - JOUR
T1 - An Enhanced Information Sharing Roadside Unit Allocation Scheme for Vehicular Networks
AU - Magsino, Elmer R.
AU - Ho, Ivan Wang Hei
N1 - Funding Information:
The work of Ivan Wang-Hei Ho was supported in part by the General Research Fund established under the University Grant Committee (UGC) of the Hong Kong Special Administrative Region (HKSAR), China, under Project 15201118. The work of Elmer R. Magsino was supported in part by the Research Impact Fund established under the University Grant Committee (UGC) of the Hong Kong Special Administrative Region (HKSAR), China, under Project R5007-
Publisher Copyright:
© 2022 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA-RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA-RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area's average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA-RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.
AB - Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA-RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA-RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area's average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA-RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.
KW - Roadside unit allocation
KW - efficiency and effectiveness
KW - fairness
KW - information sharing
KW - spatiotemporal coverage
KW - starvation
KW - vehicular networks
UR - http://www.scopus.com/inward/record.url?scp=85123357167&partnerID=8YFLogxK
U2 - 10.1109/TITS.2022.3140801
DO - 10.1109/TITS.2022.3140801
M3 - Journal article
AN - SCOPUS:85123357167
SN - 1524-9050
VL - 23
SP - 15462
EP - 15475
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 9
M1 - Article number 9681261
ER -