Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey <s> Fig. 7: Congestion detection and beacon adaptation schemes <s> Freeway traffic is an extremely complex spatiotemporal nonlinear dynamic process. This book introduces a new traffic flow theory called "three-phase traffic theory," which can explain empirical spatiotemporal traffic patterns. The main focus of this book is a consideration of these empirical spatiotemporal traffic pattern features, their engineering applications, and explanations based on the three-phase traffic theory. The book consists of four parts. In Part I, empirical studies of traffic flow patterns, earlier traffic flow theories, and mathematical models are briefly reviewed, followed by a consideration of three-phase traffic theory. Main ideas and results of the three-phase traffic flow theory are introduced and explained without complex mathematical models. In Part II, empirical spatiotemporal traffic pattern features are considered. A microscopic three-phase traffic theory of these patterns and results of an application of the pattern features to engineering applications are presented in Part III and Part IV, respectively. Appendix A contains terms and definitions and Appendix B considers some extended formulations of the ASDA and FOTO models for spatiotemporal pattern recognition, tracking, and prediction, which have been applied at the traffic control center of the German Federal State of Hessen. An index is provided. <s> BIB001 </s> Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey <s> Fig. 7: Congestion detection and beacon adaptation schemes <s> In this paper, we present a new scheme for dynamic adaptation of transmission power and contention window (CW) size to enhance performance of information dissemination in Vehicular Ad-hoc Networks (VANETs). The proposed scheme incorporates the Enhanced Distributed Channel Access (EDCA) mechanism of 802.11e and uses a joint approach to adapt transmission power at the physical (PHY) layer and quality-of-service (QoS) parameters at the medium access control (MAC) layer. In our scheme, transmission power is adapted based on the estimated local vehicle density to change the transmission range dynamically, while the CW size is adapted according to the instantaneous collision rate to enable service differentiation. In the interest of promoting timely propagation of information, VANET advisories are prioritized according to their urgency and the EDCA mechanism is employed for their dissemination. The performance of the proposed joint adaptation scheme was evaluated using the ns-2 simulator with added EDCA support. Extensive simulations have demonstrated that our scheme features significantly better throughput and lower average end-to-end delay compared with a similar scheme with static parameters. <s> BIB002 </s> Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey <s> Fig. 7: Congestion detection and beacon adaptation schemes <s> In many active safety applications, each vehicle must periodically disseminate a beacon message including the status information, such as position, speed, steering, etc., so that a neighbor vehicle can better perceive and predict the kinematics of the vehicle sending this message. However, a simple broadcasting of beacon message may lead to a low message reception as well as an excessive delay. To resolve this problem, we consider the requirements of an active safety application, a channel load and a vehicle mobility in a highway scenario of vehicular networks. Based on these requirements, we analyze the impact of the following three key parameters of the beacon dissemination on the performance of vehicular networks: beacon period, beacon transmission power, and contention window (CW) size. We first derive a beacon period which is inversely proportional to the vehicle speed. Next, we mathematically formulate the maximum beacon load to demonstrate the necessity of the transmission power control. We finally present an approximate closed-form solution of the optimal CW size that leads to the maximum throughput of beacon messages in vehicular networks. <s> BIB003
propose a transmission control algorithm that controls beacon and transmission power rate based on the tracking accuracy and high speed topology variation of vehicular networks. The proposed algorithm considers closed loop control and error-prone and instability of wireless channels between vehicles. As can be observed in Fig.8 , each vehicle embedded with communication control and position estimators track vehicles in the vicinity and plant measurements (to generate a vehicles state information). The proposed algorithm is implemented in the communication control section in each vehicle. To control beacon transmission rate, the communication control logic of each vehicle tracks the error e j (t) difference between immediate vehicles toward its own position. Each vehicle computes the transmission probability p j (t) at time step t from the value of tracking error. The algorithm first defines an error threshold e th to control the transmission of beacon message, i.e., if the tracking error is greater than the error threshold, vehicle j do not transmit a beacon message. In the other words, the value of p j (t) is equal to 0 and the control channel can be used by nearby vehicles. If this condition Fig. 8 : Communication control and position estimator in each vehicle is not satisfied (i.e., e j (t) ≥ e th ), vehicle j calculates the probability of transmission by the following equ.6: where α denotes a sensitivity of the measured tracking error and its value is greater than 0. The authors also uses transmission power control to reduce beaconing load. In their mechanism, they used channel occupancy U j t as measure to tune transmission range of a vehicle. The value of U j t is between 0 to 1 and can be determined by Clear Channel Assessment (CCA) indicator of 802.11 MAC layer. This report acts as an indication by the PHY to the local MAC entity of the current state of the medium through PHY-CCA.indication (STATE) signal. If the wireless medium is busy of vehicle j, the PHY-CCA.indication (busy) returns to MAC layer otherwise the PHY-CCA.indication (idle) will be retuned to the MAC layer. Thus, the adapted transmission range at the time window t can be defined in equ.7 where L min and L max are minimum and maximum transmission ranges, the values of these parameters depends on the application. Moreover, U max and U min are denotes minimum and maximum channel occupancy which are determined empirically. The proposed mechanism is evaluated using OPNET [48] network simulator and realistic microscopic traffic simulator. Simulation results proves the robustness and less tracking error of the proposed algorithm in comparison with existing solutions (such as static beaconing interval of 100 ms and 500 ms). In another attempt, Nguyen et al. in BIB003 thoroughly analysed the impact of beacon rate, beacon transmission power and contention window on reducing beacon load in the wireless control channel. They first calculated the optimal beacon period to provide active safety applications without control channel congestion. Thus, the beacon period is defined as follows: where D th is position error between two vehicles and v is the velocity of a vehicle. For the beacon transmit power control, the authors derived the vehicle density which is inversely proportional with the vehicle speed, i.e., where D IV is the distance between two consecutive vehicles in the same road, D v is the vehicle length, τ r v is the displacement distance due to vehicle perception-reaction, v 2 2a b is the distance during braking-deceleration. As can be seen, the vehicle density decreases with the increase of vehicle speed. Thus, the velocity of vehicles and traffic density are implicitly interrelated to one another. This relationship is clearly known in traffic flow theory. In BIB001 , Kerner states that the vehicular traffic density decreases as a result of increasing vehicle's average speed. Each vehicle broadcast its won beacon in the Carrier Sensing Range (CSR). In the CSR of a vehicle the channel load due to beaconing can be computed as a multiplication of beacon load ( L BP ) of each vehicle with the traffic density within the CSR (2D CSR Kλ ). Thus, beacon channel load within the CSR: where L is the beacon size, BP is the beacon period and D CSR is the CSR. Thus, since the beacon load is proportional to the CSR, the beacon transmission power can be adjusted to reduce the CSR of the beacon. The active safety messages should be disseminated reliably and within acceptable time delay in the control channel. However, each vehicle uses control channel to disseminate beacon messages to the vehicles within its radio range. This beacon frequent broadcasting might occupy the control channel and thereby leads to congestion. In order to alleviate this situation, the occupation of control channel should be restricted to fraction (0 ≥ α ≤ 1) of the total wireless control channel (C) as shown in equ.11: The maximum CSR (D CSmax ) to meet the channel requirement can be computed in equ.12 by combining equ.11 and equ.10 Following the trend, the authors in BIB002 proposed a joint dynamic adjustment of transmission power and contention window size. The adaptation of transmission power by first computing communication range and then using that range to determine the power level. The transmission range is calculated based on the vehicle's local traffic density as follows: where α is the constant depends on the traffic flow, L is the length of each road segment, CR is the communication range, K is the local traffic density (i.e., is calculated as a ratio of actual number of vehicles over total number of vehicles within the transmission range of a vehicle). Consider vehicles with 300 meters of transmission range, which are travelling in two lanes highway (two lanes in each direction) in a regular road length of 700 meter. The safety distance between two consequent vehicles is 16 meters. For this highway configuration, the total number of vehicles is computed as 700 16 × 2 × 2 = 175 and we assume that the actual number of vehicles is 100. From these values we can calculate K as 100 175 = 0.571. Based upon this value and using equ.13, vehicles adjust their transmission range accordingly. After obtaining the value of transmission range, the proposed algorithm established a lookup table that maps the transmission power value to its equivalent transmission range. In order to classify traffic-related messages, their proposed algorithm uses IEEE 802.11e EDCA mechanism in VANET. IEEE 802.11e EDCA consists of four access categories for prioritize incoming messages: voice traffic, video traffic, best effort traffic and background traffic. These Access categories (AC(0) to AC(3)) have different channel access parameters. For instance, AC(3) has higher priority of message transmission in comparison to other access categories as it has lower minimum Contend Window (CW min ). In such way, the VANET related messages are prioritized in order to assign highest priority to critical emergency messages. The proposed algorithm is simulated in network simulator ns-2 and compared with the standard IEEE 802.11e EDCA protocol in terms of message delay and network throughput. Results show that the proposed algorithm reduces the end-to-end delay and increases average throughput as compared to the default IEEE 802.11e EDCA protocol.