Considering that sometimes it is needed to recover from a congested channel situation, this means that reactive approaches expose safety-related applications to the risk of not being able to fulfill their design goal, due to the poor temporary performance of the underlying radio channel. Another disadvantage of reactive approaches is that important design goals such as fairness and packet prioritization are more difficult to achieve than in a proactive approach.
We remark that fairness is important in vehicular networks to ensure that all vehicles in the network have similar opportunities to communicate with nearby nodes. In fact, if congestion control were obtained by sacrificing; say, a specific node in the network is forced to set its transmission power to a very low value, this node would not have a chance to communicate with nodes in its surrounding which will consequently impair application-level performance.
Most importantly, in safety-related applications, every vehicle in the network should be able to receive fresh information about the status of the other vehicles in its surrounding, along with communicating its own status to the surrounding vehicles. For this reason, fairness becomes a major design goal in safety-related applications. As for prioritization, providing a strict prioritization of different classes of packets is an important requirement for vehicular networking, which is partly addressed in the drafted IEEE The main objective of this paper is fine-tuning the D-FPAV algorithm, which is a proactive congestion control algorithm, and it will be described in details in next section.
Proactive, reactive, hybrid congestion control approaches which are proposed for VANETs will be summarized in Table 1. We have done this taxonomy based on the above definitions and information which was available in the literature.
The references mentioned in the table can be used for more information. The following designs goals are reached through this algorithm which employs transmit power control. Congestion control: through periodic beacon exchange the load on the medium produced is limited by congestion control. Fairness: Maximize the smallest amount of transmit power value over all transmission power levels which are allocated to nodes.
This shapes the vehicular network under Constraint 1. Prioritization: assign higher priority to event-driven emergency messages when compared to the priority of periodic beacons. Solving the problem in the following lines in a fully distributed environment is the purpose of DFPAV [18].
The following elements builds the D-FPAV: 1 implementing the algorithm of FPAV [28] at every node with the collected information from the beacons which was received; 2 swapping transmit power control values which are locally computed among vehicles in the surroundings; and 3 choosing the lowest power level among all those computer locally and by surrounding vehicles. The implementation is based on the combination of transmitting power control and message generation rate.
Using Dynamic MBL value makes the algorithm adjustable based on traffic or non-traffic and event-driven or non-event-driven message conditions. The conditions on the streets and highways can be classified into two main categories; when there is traffic and when there is no traffic. Heavy traffic in the streets and highways can be detected from beacons information and based on vehicles speed.
Based on above mentioned conditions, four different states are generated, namely, non-traffic and event-driven, non-traffic and non event-driven, traffic and event-driven, traffic and non- event-driven.
However, the last state may not be generated due to the fact that the event- driven message is issued in the case of abnormal conditions. Vehicles topology location will change slowly due to heavy traffic in the streets. In this situation, using the proposed approach can decrease the number of piggybacked beacons and consequently, the control channel overhead, which is already mentioned, can be reduced. Moreover, needless to say that traffic happens when there is an abnormal condition in a street.
Therefore, in this situation, event-driven messages should have higher priority than beacon messages. Through the proposed methodology, more bandwidth will be reserved for transmitting event-driven messages in the case of traffic.
As a result, the probability of receiving event-driven messages will be raised as well as their reception range. In the next paragraphs, the procedures for detection of traffic in the streets and assigning the MBL value dynamically are explained in details.
Therefore in next section, its effect will be investigated via simulation. Experiment In the evaluation of the wireless communications, the aspect of the using suitable models and their accurate configuration plays a significant role. Since NS-2 [29] is an extensively used network simulator is employed as the simulator in our paper.
To consider a real-life scenario as well as a dynamic network topology, a scenario has been used which has 7km long with relatively considerable traffic density.
The highway has three lanes and the average vehicle density for each kilometer is This scenario is illustrated in Figure 1. Figure 1. Vehicles Scenario for 1 km There are several other parameters which are designed to perform the scenarios in the simulation.
The packet generation rate which is selected for beacons is 10 packets per second which is seen as a suitable value in order to provide accurate data for the safety system [13]. The sizes of packets for all beacons are sat at Bytes. In the CS range, rather than the number of nodes, the MBL threshold is vented in relation to megabits per second.
Nevertheless, both measures that are the packet size and the packet generation rate assumed to be the same for all the nodes are equal and recognized.
Finally, communication range CR is set to meter at maximum transmit power. The maximum CS range, considering these configuration parameters, is meter. Table 2 shows the most important configuration parameters which used in these simulations. The event-driven messages are transmitted through one particular node situated around the middle of the 7km long road.
They resend at the highest transmit power. The metric used for analyzing MBL effect on D-FPAV is the probability of the event-driven and beacon messages effective reception when considering the distance. The reception probability is employed to evaluate D-FPAVs efficiency in reaching a suitable prioritization for safety-related messages, which is attained via rising the correctly reception probability of event-driven messages simultaneously as not lowering the correctly reception probability of beacons near to the sender.
Simulation Results 6. The distance between the sender and on the CR probably depends on the volume of the increase in reception. The trade off for obtaining a higher probability of receiving event-driven messages is a decreased probability of receiving beacon messages. For example, the probability of receiving of beacon message correctly at a distance of 50m from the sender is about 0.
For example, the probability of correctly receiving of beacon message when the distance is 25m from the sender is around 0. This lower probability for beacon reception is because of the lower transmitting power employed in sending beacons.
This can be considered essential in order to lessen the beaconing load lower than the MBL threshold. A MBL value which is smaller, limits the transmission power used for beacons, i. Facing smaller MBL, the average beacons transmission range is decreased and event-driven messages benefit the lesser load which will result in the higher probability of successfully being received from a distance.
The obtained results with D- FPAV-On lower probability of reception for beacons at medium-long distances, while higher reception probability at all distances for event-driven messages satisfied the safety related messages requirement. Beacons are sent periodically and the data which they carried is mainly significant for the nodes in the vicinity to the sender. For this reason, in terms of safety messages, the lower reception probability of beacons in close distance is not vital.
However, to avoid accidents and other hazards, the event-driven messages need an immediate reaction after being issues by the near and far distant vehicles. This will be fulfilled via the evidently greater probability of the reception of event-driven messages with D-FPAV-On with any MBL value, which is tested in our simulation, at all distances close and far from the sender.
Based on our findings, the MBL values assigned in each scenario have proven the effectiveness of algorithm to control the messages load in vehicular networks. As a result, the MBL values for different conditions, which are mentioned in our algorithm, are as follows: the MBL equals to 1Mbps when there is traffic in street condition 1 , 3Mbps when no traffic occurred and event-driven message to send condition 2 , 2Mbps when there is no traffic at least one event-driven message has to be sent condition 3.
According to the results which are illustrated in Figure 4 and Figure 5, dynamic D-FPAV shows better throughput and reception probability in the case of beacon and event driven messages.
Throughput is a metric which is described as the total number of received packets at destination out of total transmitted packets. This service is more advanced with JavaScript available.
Luan Qiaorong Liu Rui Xing. One of the the first books to systematically expound the next generation vehicular networks with a wide range from the novel model and algorithms to the practical applications One of the first books that integrates the enabling technologies and vehicular networks to provide a comprehensive coverage of the network architectures and the framework design principles Discusses a state-of-the-art review of key enabling technologies and their potential development prospects in vehicular networks.
Front Matter Pages i-xiv. Luan, Qiaorong Liu, Rui Xing. Pages Conclusions and Future Directions. About this book Introduction This book proposes the novel network envisions and framework design principles, in order to systematically expound the next generation vehicular networks, including the modelling, algorithms and practical applications. It focuses on the key enabling technologies to design the next generation vehicular networks with various vehicular services to realize the safe, convenient and comfortable driving.
The next generation vehicular networks has emerged to provide services with a high quality of experience QoE to vehicles, where both better network maintainability and sustainability can be obtained than before. The framework design principles and related network architecture are also covered in this book.
Vergados Angelos Michalas Dept. Vergados Dept. MPEG Dynamic Adaptive in rapid succession; b the movement at high speeds causes Streaming over HTTP DASH is a widely used standard, that Doppler effects that may hinder the wireless transmission; allows the clients to select the resolution to download based c as the speed of the vehicles increases, the handovers must be on their own estimations.
The algorithm for determining the performed quicker to prevent the interruption of the service. To next segment in a DASH stream is not part of the standard, guarantee the reliability of services in vehicular environment, but it is an important factor in the resulting playback qual- ity. Nowadays vehicles are increasingly equipped with mobile specific algorithms should be applied to adjust the video rate communication devices, and in-vehicle multimedia entertainment delivered to each user in accordance to the current network systems.
In this paper, we evaluate the performance of various conditions. We expect adaptation algorithms that work well in DASH adaptation algorithms over a vehicular network. However, the VANETs are expected to provide modern services with standard does not define how the user could adapt to time different Quality of Service QoS characteristics.
To support varying bandwidth in order to achieve better quality. The clients femtocells as well as IEEE The current paper extends our previous work by transmission of multimedia data, occurring on a highway, comparing FDASH with other adaptation algorithms in the including video clips of an accident or a critical situation context of a vehicular network over LTE.
In particular, we e. We evaluate the performance in terms of throughput, congestion level shift-ups. Moreover, it uses a rate margin to playback interruption time, and number of interruptions. A reduce slightly the video rate to limit video rate adjustments. The fetch times of last two video segments are used the literature.
In Section II we present and the client. This bandwidth estimation is then used to select a review of the relevant literature. Section IV provides simulation results the highest rate that is smaller than the estimated bandwidth. Finally, we In [11], the authors applied a Markov chain to analyse the conclude the paper and discuss ongoing and future research QoE metrics for the user, namely the probability of the video directions in Section V.
We focus on adaptation mechanisms that are performed moving average in order to capture the related information entirely at the client side, using measured network conditions between near-term past values and current values. Caching enables storage balancing the following goals: a to prevent video playback of popular videos in the network edge, close to the users, interruptions when possible; b to maintain a high average however, caching cannot be applied to all videos.
Reference [7] presents an adaptation scheme called Rate III. Switch up is done using a step-wise process, whereas ously prevents the video playback from being disrupted due to switch down is done in a single step aggressive.
There is also buffer underflow and maintains the highest video bit rate. The a mechanism to limit the maximum buffering time. In order to detect the network avail- an HTTP video server. Each client time. The expected segment fetch time takes into account the uses a fuzzy controller rate adaptation algorithm to estimate media segment duration and the buffering time at the client. The proposed algorithm tries to achieve the following: rate adaptation metric, a step wise switch-up and a multi- a distribute the best possible resolution of video segments; step switch-down strategies are applied.
In addition, priority b deliver undisrupted video playback as a result of buffer segment fetch times are assigned to new clients to improve underflows at the client; c avoid unnecessary changes of fairness. The definition of function f Aaash-conf Fdash-conf encompasses the logic of the controller. The average playback bit rate that is achieved for each algorithm and and the time when the whole segment has been received at speed.
The final step of the algorithm tries to avoid unnecessary 70 Aaash-conf bit rate fluctuations. In all other cases, the bit rate of the next segment is set to bn. The average number of interruptions per video stream for each algorithm and speed.
For each simulation run, each user downloads a video over LTE, where all users use the same DASH adaptation algorithm, 35 Aaash-conf which is a parameter of the simulation. The other simulation Fdash-conf 30 Osmp-conf parameter is the maximum speed for the vehicular users.
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