Reaching spatial or networking saturation in VANET

Serkan ztrk 0 Jelena Mii 1 Vojislav B Mii 1 0 Erciyes University , Kayseri, Turkey 1 Ryerson University , Toronto, ON, Canada In this article, we investigate the network transition between non-saturation and saturation regimes for a Vehicular Ad hoc Network (VANET) which is composed of mobile nodes. We combine vehicular traffic theory, queuing model, and Markov chain to evaluate the performance of the network under spatial or networking saturation for multiple data classes over control channel and service channel. Our results indicate that the vehicle density growth can result in saturation of wireless medium around the roadside unit (RSU), further resulting in buffer overflows at on board units (OBUs). We also investigate the network saturation points for different transmission ranges of a RSU. Our results show that RSU's transmission coverage has to be chosen with respect to data patterns of OBUs, minimal distance between vehicles, and number of lanes in order to avoid network saturation condition. 1 Introduction Vehicular Ad hoc Network (VANET) is a special type of Mobile Ad hoc Network (MANET) based on short-range communications among moving vehicles and between vehicles and roadside units (RSUs). IEEE 802.11p is referred to as dedicated short-range communications (DSRC) standard for wireless access in vehicular environment (WAVE). For DSRC, 75MHz of licensed spectrum at 5.9 GHz has been allocated. This 75MHz band is divided into one central control channel (CCH) and six service channels (SCHs) as shown in Figure 1. CCH is dedicated for transmission of traffic safety messages, while SCHs are dedicated to transfer of various application data. Both CCH and SCH support four data classes with aggressively differentiated priorities as shown in Tables 1 and 2. Each data class has its own MAC resources. With IEEE 802.11 technologies, often a single shared wireless channel is used for both uplink (from vehicles to the RSU) and downlink (from the RSU to vehicles). Because of the distributed nature of contention, the capacity actually depends on the behavior of contending vehicles [1]. The number of contending vehicles covered by an RSU depends on the vehicle mobility and density. Furthermore, as shown in Figure 2, vehicles have different payload transmission rates according to their distance to the RSU [2]. The sojourn time of a vehicle for each of the different ranges is dependent on its speed. A VANET is unstable when a queue of any on board unit (OBU) in the network is saturated. A queue is saturated if it always has at least one frame waiting to be served. VANET cannot operate under saturation conditions because the OBUs buffer will overflow and frames queuing delay will grow unacceptably. Since all the data classes need to operate in stable conditions, the network performance must be investigated under non-saturation regime. However, network performance in non-saturation regime has received much less attention because of its complexity. In [3], the authors investigate the performance of an IEEE 802.11p-based network in non-saturation regime with static nodes. On the other hand, spatial saturation occurs when the distance between vehicles reaches minimal (jamming) value because of the vehicular traffic congestion. While spatial saturation of vehicles during rush hours or accidents cannot be avoided, networking saturation can be avoided by proper dimensioning of resources. In this study, we combine vehicular traffic theory, M/G/1 queuing analysis, and Markov chain analysis in order to investigate the transition between non-saturation and saturation regimes for an IEEE 802.11p-based network which is composed of mobile nodes with multiple data Control Channel Service Channels Service Channels High Power Public Safety Critical Safety of Life Figure 1 WAVE channels. combinations and multiple data classes per combination. We consider the neighbourhood of a single RSU operating in non-saturation regime deployed on a bidirectional road segment. The number of vehicles in each direction (lane) under free-flow model [4] is considered as a Poisson distribution. Assuming error-prone channel conditions, we derive probability distributions for frame backoff time, waiting time in queue, collision probability of a transmission, and normalized throughput for each channel and each data class with different transmission rates depending on the vehicles distance from the RSU. The remainder of the article is organized as follows: in Section 2 we discuss related work and in Section 3 we develop analytical model. Section 4 presents the numerical results. Finally, Section 5 concludes the article. 2 Related work Vehicular traffic flow models are classified as microscopic, macroscopic, and mesoscopic [5]. Microscopic traffic flow models describe each vehicle separately. In macroscopic models, all individual vehicles are aggregated and described as flows. The speed-flow-density relationships are used in these models [4,6]. Mesoscopic models combine microscopic and macroscopic elements in a unified approach. In [7] the authors investigate the connectivity of VANETs operating in free-flow regime. They use a common model [4] in vehicular traffic theory in which any observer in space sees cars passing it that are separated by exponentially distributed times. Current state of the art in this area is a combination of saturated IEEE 802.11 model with free-flow vehicular traffic regime and spatial Poisson arrangement of vehicles [1,8-12]. In [1], authors have developed an analytical framework to evaluate the upload performance for Drivethru Internet as a function of vehicle density. In [8], authors have derived an analytical model to quantify the impact of parameters such as road traffic density and vehicle speed on the download performance of moving vehicles in Drive-thru Internet systems. Authors in [9] have considered heterogeneous vehicular environments where vehicles may have different mobility characteristics. A model to estimate the collision probability in VANETs has been proposed in [10]. This model integrated the characteristics of VANETs (vehicle density and speed) into the traditional collision probability model. In [11], authors have proposed a model to improve the efficiency of communication between vehicles and RSUs. In this model, every vehicle can individually calculate its own priority of communication based on its speed and location. Authors in [12] have proposed an analytical model to evaluate the MAC throughput under different node speeds in Drivethru Internet system. All the proposed models have deployed IEEE 802.11b as the wireless communication standard for VANETs instead of IEEE 802.11p. None of the proposed models have considered non-saturation regime, so far. 3 Analytical model Let us consider the neighbourhood of a single RSU operating in non-saturation regime deployed on a bidirectional road segment as shown in Figure 3. According to the location to RSU, th (...truncated)