# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 1 Introduction  
With the progress of wireless communication technology and information technology and the continuous emergence of various sensing devices, intelligent vehicles equipped with wireless communication units, computing equipment, electronic maps and a large number of sensors appear. With the help of On Board Units (OBUs), intelligent vehicles constitute the Internet of Vehicles (IoV). As the most potential industrial application in the fields of 5G, transportation and automobile, IoV is an important part of the future intelligent transportation system [1, 2]. However, rogue nodes (bad nodes), which spread false information or malicious information in the network, bring many problems to the entire network, such as high network delay, data inconsistency and data loss. These problems have a great impact on the actual traffic environment, resulting in traffic jams, vehicle collisions, etc., which bring social losses. Hence, rogue node discovery plays an important role in building a secure IoV environment. Previous research proposed machine learning-based methods to detect rogue nodes [3, 4, 5, 6]. In these works, Support Vector Machine classifiers (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) algorithm, etc., are used for rogue vehicle detection. These above methods can identify a certain number of rogue nodes. However, they rely too much on historical data. Further, these methods may lead to long network delay and slow detection speed [6]. Since using the computing power of roadside units (RSUs) can obtain more accurate detection results, studies [7, 8, 9] proposed to use the RSUs deployed on the road to discover the rogue node. However, both the number and the coverage of the RSUs in the urban area are limited. Therefore, it is difficult to obtain real-time detection when the traffic load is heavy. Moreover, RSU-based rogue vehicle detection strategies cannot workif the RSUs become failed due to disasters. To discover rogue vehicles, [PERSON] et al. [10] proposed to dynamically create a fog network composed of moving vehicles in the detection area. However, due to the fast speed of moving vehicles, the stability of the fog network is difficult to guarantee. Based on the widespread parked vehicles in cities, this paper proposes a rogue node detection scheme based on roadside parked vehicles. In this paper, parked vehicles on a road automatically form parking clusters at first. Then, a prediction model is built to predict the vehicles that will continue to stay in the cluster in the next few hours. A fog network composed of stable parked vehicles is dynamically established. Finally, a U-test method based on hypothesis testing is proposed in order to detect rogue vehicles. To achieve fast and efficient rogue vehicle detection, we let all nodes in the fog network perform rogue node detection in parallel. Simulation results show that the proposed scheme has higher detection accuracy and stability than other rogue node detection schemes. We summarize our contributions as follows:  
(1) We propose a parked vehicle stability prediction model to predict the probability that the parked vehicle will continue to stay in the cluster in the future period of time. Stable vehicles are then selected to form a fog network, in which fog nodes cooperate with each other to detect rogue nodes to reduce the delay of bad node discovery. Since the formed fog network is stable, the calculation migration overhead caused by the leaving of the fog node is avoided. To the best of our knowledge, this is the first attempt that tries to decrease the processing delay of bad nodes by using parked vehicles in cities. (2) To adapt to the dynamic changes of traffic conditions, we let the number of fog nodes correspond to the moving vehicle density on the road. To obtain the density of moving vehicle on the road, the Greenshield model is used to model the current road conditions.