## 2 Cooperative V2X Traffic Surveillance: Background and Related Work
### Related Work  
Even if VANET is a very active research area, the majority of the attention so far has been focused on new and optimized routing protocols to overcome the numerous challenges posed by these mobile networks. In this paper, we focus only on traffic surveillance solutions, an area in which most literature has concentrated on estimating very basic indicators such as the number of mobile vehicles inside a targeted area, usually called density, thus gathering only a general concise overview of the traffic state. The methods used to collect the information can make use of a clustering approach to group the nodes together if they are moving in the same general direction [13] or if they are close to a fixed point [14]; or it can make use of direct communications between the participating nodes and, when present, the infrastructure support points (e.g., RSUs), such as in [15; 16; 17]. While the goal of the present paper is the presentation of a quantitative comparison and benchmark of notable existing proposals, for a qualitative and more exhaustive review of existing efforts in the field, we refer interested users to the survey [18]. Among the traffic surveillance solutions available in the literature, we have decided to consider three relevant protocols that have become relatively well-known in the community in the field and that are sufficiently well described in the corresponding papers to allow us to implement them in our simulation work (reproducibility of their performance results). In addition, as it will be clarified in the following sections, these three protocols are fairly comparable with our original proposal because they share some similar characteristics and features. The first one is the MobSampling [16] protocol. MobSampling has been designed to estimate vehicle density over a targeted area: a source node (either fixed or mobile) sends a message to the targeted area to start the sampling process; the first vehicle receiving this message becomes the sampler and has the task of periodically estimating the density in the area. The protocol also considers the possibility that the sampler node leaves the targeted area, by including a role switching procedure to transfer the sampler role. The density estimation process in MobSampling works as follows: the sampler randomly selects when to trigger the sampling, with a uniform distribution with its average value equal to T seconds; in each sampling period, the sampler broadcasts a POLL message that includes the location of the center and the radius of the targeted area; if a vehicle inside the targeted area receives the POLL message, it will answer with a REPLY message after a random delay (to avoid broadcast storms towards the receiver); finally, the sampler node computes the instantaneous density as the ratio between the number of received REPLAYs to the last POLL message and the road length in the targeted area. The next two selected protocols, discussed in [17], were initially developed for ecology-oriented applications to estimate wild animal population and then adapted to traffic surveillance applications. Both produce an estimation of the number of vehicles inside the targeted area, by assuming that every vehicle periodically sends status messages, such as the safety messages used in DSRC. In particular, the Capture-Recapture protocol estimates the total population by considering only a small number (at least two) of independent samples of \"captured\" vehicles. Considering the method with only two samplings, as in [17] and in this paper, the estimation of the population \(N\) is obtained with the formula \(N=mn/r\), where \(m\) is the number of distinguished nodes counted in the first sampling,\(n\) is the count of the second sampling, and \(r\) is the number of vehicles counted in both samplings. In vehicle-oriented traffic surveillance, a vehicle is counted if one of its messages is successfully received by a neighbor vehicle.