A 5G-VANET model with the integration of SDN, Cloud-RAN, and edge computing technologies is designed. This model provided better throughput and minimized delay.
Proposed a scheduling scheme established on queue length and response time. They also formulated a design for a vehicular cloud, based on a compositional approach (PEPA).
The PV system supports a heuristic insertion algorithm and a cooperative strategy between vehicle nodes, edge, and the cloud for sending requests as well as schedules routes for PVs.
Presented an SDN based (MPSO-CO) centralized load balancing algorithm. This optimized the workload between the edge/fog networks so the latency can be efficiently minimized.
Presented a computational offloading infrastructure, which stresses upon the computational effectiveness of the transfer frameworks of V2I and V2V modes of communication.
Presented CVFH scheme in which, before entering the coverage area of the target AP, a vehicle takes relevant information from the qualified vehicle through its neighboring vehicle.
Proposed an FLRM scheme, which determines each resource’s survival time by using the collected information with the help of fuzzy logic, which is based on popularity evaluation algorithm.
They addressed the local resource management in the FeRAN. To support this, FRR and FRL schemes were introduced. The on-hop probability for real-time vehicular services is enhanced.
Proposed FOX, which detects and manages traffic congestion in VANETS. Through this, the time of the trip, emission, and fuel consumption could also be reduced.
Proposed a data-sharing scheme, which analyzes a multiauthority CP-ABE by effective decryption, while protecting a CP-ABE system safe against the collusion attack.
Proposed a fuzzy trust model, which detects the defective nodes and unauthorized attackers and handles the uncertainty of data in the vehicular networks.
Analyzed DREAMS where edge server executes local management tasks by ensuring a trusted reputation. It optimizes resource allocation and detects and enhances the recognition rate of misbehaving vehicles.