## 3 Problem Statement
* When higher requirements are put forward for timely task processing, which involves a large number of computations, task offloading faces obstacles. * Even though the offloading is performed with ultra-low latency, the offloading quality (i.e., storage, deadline delay constraints, and energy efficiency) is not good and should be improved. * Lack of environmental factors leads to mobile edge server failure, so offloading accuracy is decreased. Research solutions: Physical and virtual fog deployment in IoCV environments can reduce response time and latency. The deployment of servers for the highest potential influence will increase the quality of offloading. A driver behavior model that analyzes the behavior of drivers was proposed in [42]. It measures information such as vehicle data, environmental data, and socio-demographic data. A Gaussian mixture model-universal background model (GMM-UBM) is used to construct an individual model for each drive to store individual behavior profiles. The drivers are given scores based on an analysis performed by the model. The scores are given with respect to the score of the safest driver, which is considered from the historical records of the drivers or by calculating the average normal distribution of all the drivers. Based on the scores, the drivers will get recommendations to mimic the behavior of the safest driver. Through this method, risky drivers can adopt better driving behavior, thereby reducing the risk of road accidents. However, this method has several problems, which are mentioned below. * A different level of connectivity for longer tests with more vehicles within other networks cannot be calculated. * The data library is not sufficient because it does not have more trajectory data in addition to other types of data describing driver situations, particularly weather conditions and driving behavior, and real-time updating of the data library should be implemented. * The health parameters of drivers are not considered, and they are important in emergencies. Research solutions: Driver behavior can be analyzed by considering driver health parameters, vehicle motion detection parameters, and surrounding factors. The driver analysis is performed by a refined asynchronous advantage actor critic (A3C) algorithm. An intelligent driver assistant was proposed that accompanies the driver by providing real-time audio-visual alerts in [43]. The proposed system uses both road accident risk map analysis and on-board telemetry, which are integrated based on fuzzy logic. The on-board telemetry system monitors the driver by collecting data such as speed, bad pedaling, and bad steering. The proposed system analyzes a certain amount of accident data through various parameters and estimates the road risk level, thereby identifying which road is riskier than the other. Through this proposed method, the efficiency of the driver gets improved. The drawbacks of this approach are below. The assistance given by the car is very limited as it does not give any real-time warnings. * Regarding drivers not only having the risk of an accident for a particular road section but also sub-section or intersection, such details are not given. * Environment characteristics such as weather conditions are not observed in the road accident risk map analysis but do have an impact on road accidents  
Research solutions: Personalized assistance can be provided in real-time on the basis of the current state of the driver. Assistance is provided one or more times through a multi-attribute utility model. This model is accurate and reduces the latency in giving assistance. A beaconless approach for data communication between two vehicles was proposed in [44] to overcome the wastage of bandwidth caused by beacon packets. The packets to be transmitted are rescheduled on the basis of the forwarding probability, which is decided by factors such as distance, angular orientation, moving direction, and buffer delay. Among the rescheduled packets, the highest priority packet is transmitted first.