Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, so on. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel Smart Traffic Light Controller (STLC)-assisted Software-Defined Vehicular Networking (SDNV)-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing SDVN architecture in vehicle-to-vehicle and vehicle-to-infrastructure communication. The SDVN module is used to offer two essential services: (1) SIoV-based road-lane prioritization and (2) congestion prevention signal generation for the STLC. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization are done: (1) SIoV and (2) the road lane with SIoV. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5–55.2%, 1.2–82.7%, 1.6–38.4%, and 1.8–12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

Towards achieving the goal of green transportation, the usage of battery powered electric vehicles (BEVs) has been continuously growing across the globe. However, considering the limited number of Charging Stations (CSs) in the cities, electric vehicle charging problem has become a challenging task, especially, due to the constraints of longer waiting time and dynamic pricing at the CHs. This issue has led to the degradation in Quality of Experience (QoE) for BEV drivers. Moreover, Charging Point (CP) service providers in the cities also suffer from lack of space which causes higher congestion at the CSs. In this context, we propose ChaseMe, a heuristic scheme for optimizing CS management by scheduling BEVs based on availability and type (fast/ultra-fast) of CPs by considering delay and charging time for CPs reservation. The proposed heuristic scheme consists of two soft computing techniques i) Harris Hawk Optimization (HHO) and ii) Fuzzy Inference System (FIS). Former technique is used to map the CP reservation requests to the best-suited CS by considering Quality of Service (QoS) parameters and acting as a global optimizer. FIS locally manages CPs at a particular CS in coordination with proposed meta-heuristic technique. The experimental results prove the benefits of the proposed ChaseMe framework as compared to the state-of-the-art techniques considering various charging metrics for BEVs.

Social Internet of Vehicles (SIoV) is an evolving vehicular networking framework integrating the next generation smart devices with vehicular communications. Green computing and communication under disruptive vehicular environment is one of the challenging tasks for enabling SIoV. In this context, green traffic data dissemination in SIoV environments is modelled as an NP-hard problem focusing on heterogeneous traffic data, transmission distance from next generation smart devices and probabilistic delay in transmissions due to disruptive vehicular environment. An adopted meta-heuristic solution namely Two-Way Particle Swarm Optimization (TWPSO) is developed for the green traffic data dissemination problem in SIoV considering software defined vehicular network architecture. Extensive simulation experiments were performed to assess the performance of TWPSO as compared to the state-of-the-art techniques. The critical analysis of the comparative results attest the green computing oriented benefits of TWPSO under real SIoV environments.