Link Optimization in Software Defined IoV driven Autonomous Transportation SystemShow others and affiliations
2021 (English)In: IEEE Transactions on Intelligent Transportation Systems, Vol. 22, no 6, p. 3511-3520Article in journal (Refereed) Published
Abstract [en]
Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).
Place, publisher, year, edition, pages
IEEE, 2021. Vol. 22, no 6, p. 3511-3520
Keywords [en]
Software-defined IoV, link optimization, vehicular networks, SSLO, autonomous, ITS
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-42871DOI: 10.1109/TITS.2020.2973878OAI: oai:DiVA.org:miun-42871DiVA, id: diva2:1587695
2021-08-252021-08-252021-09-07Bibliographically approved