# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 3 Network Model
### Problem Statement  
A vehicle that offers an incorrect message is denoted as a bad node. Bad nodes usually supply high or low moving speed compared with normal automobiles [9]. Hence, most research [8; 9; 10; 21] detects bad nodes according to the vehicles' moving speeds. However, the detection performance of the relevant studies that are based on RSUs relies heavily on the density of deployed RSUs. In fact, it takes a lot of money to build infrastructure.  
Moreover, these strategies cannot work if the RSUs become failed due to disasters. In study [10], the authors let moving vehicles on the road form a fog network, in which fogs nodes assist the guard node to detect bad nodes. However, the stability of the fog network formed by the moving vehicles is difficult to maintain. When the traffic density is very low, it is difficult to even form the fog network.  
The scheme we propose is based on the parking resources on the roadside. Hence, the distribution and stability of the roadside parked vehicles are the keys to the realization of the proposed scheme. Research [22] conducted an investigation on the utilization of parking spaces in the Puget Sound of the United States and found that parking spaces are occupied for a long time. The average utilization rate of the ground parking lot is about 78%. In addition, the occupancy rate of more than half of the ground parking lots is close to 100%. The authors in [23] gave a survey of the parked vehicles in a 5500-square-kilometer area in Montreal, Canada, which found that the number of parked vehicles in this area is about 30,000 per day and the average parking time of the vehicle is nearly 6.95 h every day. Report [24], which investigated the road conditions in Shenzhen, China, showed that the density of parking spaces in this city is very high, reaching 2100 parking spaces per square kilometer. In reference [25], the situation of parking lots in Harbin, China was investigated. It was found that the utilization rate of parking spaces during the day was maintained at more than 80% and was relatively stable. Although the parking reports provided above are only for a few cities, large-scale parking, long-term parking and high-density ground parking are common in most cities. Moreover, research [26] proved the occupancy ratios of roadside parking can be maintained. Research [27] further verified the stability of the parking cluster through theoretically analysis.  
Traffic conditions in modern cities are highly variable, with moving vehicles on the streets changing their positions rapidly, especially during rush hours of the day. On the contrary, most cars are parked for much longer time than they are driven. Based on the above analysis, we conclude that, compared with moving vehicles, the formed cluster is much more stable. Further, fog nodes are carefully selected from stable parked vehicle members in the cluster. Hence, the formed fog network, which has high stability, is able to support the detection of bad nodes in complex traffic environments.  
For the power problem of the vehicle, we let parked vehicles with low power not take part in rogue node detection. This is because the driver cannot drive the parked car away if the remaining power of the parked vehicle is low. To avoid this problem, when selecting the fog computing nodes, if the remaining battery level of a stable parked vehicle is lower than a predefined threshold, it cannot be selected as the member of the fog network. Moreover, if the battery level of the parked car is lower than the predefined threshold, it is deleted from the fog network. Further, the proposed parked vehicle stability prediction model can be executed periodically. The structure of the fog network is maintained and updated by constantly adding newly-detected parked vehicles with sufficient battery energy into the fog network. Thus, the power of the fog nodes can support the rogue node computation.