# Road-Side Unit Anomaly Detection
## 2 State of the Art
### Anomaly Detection  
In [8], an anomaly detection approach is the process of defining a region of normal behavior within a set of data and declare whichever data points that do not belong to this region as anomalies. Moreover, anomalies are classified into three categories: (a) point anomalies, which is when a data point is considered aberrant from the rest of the data, (b) contextual anomalies, which is when a data instance is anomalous in a specific context, and (c) collective anomalies, which is when a collection of related data instances is anomalous while the individual data instances may not be anomalous themselves. Vehicular communication applications are known for their high mobility, which qualifies them as critical applications that require a reliable infrastructure and communication scheme. RSUs are one of the key components of a C-ITS; they mainly ensure the V2I  
Figure 1: A General Scheme for Vehicle-to-Vehicle Communication. Figure 2: A General Scheme for Vehicle-to-Infrastructure Communication. communication type. An omnidirectional antenna of an RSU is assumed to have equal radio propagation in all directions, but the presence of obstacles such as buildings, tunnels, rivers, or ground elevations can cause signal attenuation in certain directions. Such an RSU is not technically defective. However, a high signal attenuation in some areas of effective coverage could indicate failure. In [9], antenna failure detection was implemented using a probabilistic model. The model was constructed using real data from field tests, whereas the health assessment of RSUs was performed through a comparison between their behaviors in terms of radio propagation. In [10], a failure detector of VANET systems is proposed, which has some notions of signal attenuation. In [11], numerical simulations were performed in various radio-propagation scenarios, where it was shown that various factors affect the attenuation of a signal. A signal tends to attenuate over large distances in line-of-sight (LOS) contexts, whereas in non-line-of-sight (NLOS) contexts, the presence of a building or obstacle can greatly affect signal intensity (in dBm). Thus, the behavior of radio propagation is highly dependent on the geographical context, be it interurban or urban. In terms of range, we have observed in our work that, in the interurban context, and with the absence of obstacles, the in-distance (ID) of communication is greater than the out-distance (OD), which are, respectively, the range of the first point where the RSU signal is detected and the range of the last point. This was also observed in [12], where the range of communication between a vehicle and an RSU tends to be greater when the vehicle is moving toward the RSU. Moreover, the authors observed a relationship between vehicle velocity and range and they explain it as a consequence of the Doppler effect. This can also be observed in [13]. The inverse case is, however, observed in [14], where the OD is greater than the ID; however, the authors explain that this is due to the time needed for the mechanism of signal validation in the Wi-Fi access points during a first connection. DeepADV [15] is an anomaly detection framework for VANETs based on deep neural networks. A threshold is calculated based on the difference between a genuine and an anomalous message and then used to classify them. The algorithm is to be deployed on RSUs and its aim is to detect faulty messages. In [16], an anomaly detection scheme on VANETs using edge computing was proposed. The faults concerned transmission omission and were detected using RSU-based edge network and vehicular edge computing (VEC). These RSUs guard information about a number of vehicles and a number of collected packets, which are then used to determine whether an anomaly (a change in the numbers) has occurred. They tested their anomaly detection strategy on a simulation and found that the strategy was highly effective at high rates of fault ratio (25%).