An Energy-aware Facilitation Framework for Scalable Social Internet of Vehicles

• Published in: International journal of advanced computer science & applications 2021, Vol.12 (9)
• Main Authors: Almazroi, Abdulwahab Ali, Qureshi, Muhammad Ahsan
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Data acquisition; Data processing; Energy consumption; Energy efficiency; Energy management; Internet of Things; Internet of Vehicles; Mobile ad hoc networks; Provisioning; Traveling salesman problem
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2021.0120915

The Internet of Things (IoT) has eventually evolved into a more promising service provisioning paradigm, namely, Social Internet of Things (SIoT). Social Internet of Vehicles (SIoV) symbolizes a multitude of components from the existing Vehicular Ad-Hoc Networks (VANETs) such as OBUs, RSUs, and cloud devices that necessitate energy for proper functioning. It is speculated that the connected devices will surpass the 40 billion mark in the year 2022 in which the devices related to ITS will constitute a significant part. Therefore, the ever-increasing number of components increases the communication hopping that results in the immense escalation of energy consumption. However, the energy consumption at the object level increases due to individual communication, storage, and processing capabilities. The existing research in SIoV is focused on providing state-of-the-art services and applications; however, a significant goal of energy efficiency is largely ignored. Therefore, extensive research needs to be performed to come up with an energy-efficient framework for a scalable SIoV system to meet the future requirements of ITS. Consequently, this study proposed, simulated, and evaluated an energy-aware efficient deployment of RSUs scheme. The proposed scheme is based on network energy, data acquisition energy, and data processing energy. To achieve efficiency in terms of energy, traveling salesman problem with ant colony optimization algorithm are utilized. The experiments are performed in an urban scenario with different numbers of RSUs. The outcomes of the experiments exhibited promising results in energy gain and energy consumption having implications for society and consumers at large.