Beacon Rate Research Articles

Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle's ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL-JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.

Distributed and Fair Beacon Power and Beaconing Rate Adaptation Based on Game Theoretic Approach for Connected Vehicles

In vehicular ad hoc networks, vehicles regularly transmit information through beacons to raise awareness among nearby vehicles about their presence. However, as the number of beacons increases, the wireless channel becomes congested, resulting in packet collisions and the loss of numerous beacons. This paper addresses the challenge of optimizing joint beaconing power and rate in VANETs. A joint utility-based beacon power and rate game is formulated, treated both as a non-cooperative and a cooperative game. To compute the desired equilibrium, three distributed and iterative algorithms (Best Response Algorithm, Cooperative Bargaining Algorithm) are introduced. These algorithms simultaneously update the optimal values of beaconing power and rate for each vehicle in each step. Extensive simulations showcase the convergence of the proposed algorithm to equilibrium and offer insights into how variations in game parameters may affect the game's outcome. The results demonstrate that the Cooperative Bargaining Algorithm is the most efficient in converging to equilibrium.

Performance Analysis and Optimization for Semi-Persistent Scheduling in C-V2X

For periodical beacon broadcasting in cellular vehicle-to-everything (C-V2X) networks, a distributed reservation media access control (MAC) protocol, the sensing-based semi-persistent scheduling (SPS), is adopted. However, how to quantify the communication reliability and latency is an open issue, which is critical for low-latency and high-reliability services. In this paper, an analytical model for SPS is presented, based on which the impacts of beacon rate, range settings and system configuration on access collision probability and delay outage probability are quantified. The analytical model provides important insights and guideline to adapt and optimize protocol parameters including the sensing range, transmit power and resource reservation. The enhanced MAC protocol can maintain high-reliability and low-latency services with a wide range of vehicle density. Simulations are conducted to validate the analysis and the results demonstrate that the proposed MAC enhancement solution can reduce collision probability while ensuring the delay outage probability based on service requirements.

Efficient Congestion Control With Information Loss Minimization for Vehicular Ad Hoc Networks

The reduction of beacon message rates is of great significance to network congestion control in Vehicular Ad Hoc Networks (VANETs). However, this may cause large traffic information loss and serious traffic accidents. In this paper, we propose an efficient congestion control protocol, which not only alleviates channel congestion but also minimizes information loss by adaptively adjusting the beacon rates. Unlike conventional methods, this protocol investigates beacon transmission from a perspective of information loss, and adopts different beacon rates for different vehicles. First, we design an information loss factor to quantify the information deviation between the original information generated by a vehicle and the actual information received by its neighbor vehicles. Then, based on the information loss factor, we transform the beacon rate adjustment problem into an information loss minimization problem, which is addressed by a greedy beacon-rate adjustment algorithm. In addition, based on the derived different beacon rates, we design a periodicity-based time-slot selection scheme to allocate idle time slots to different vehicles, in order to alleviate transmission collisions. Extensive simulation results show the advantages of our solution in terms of multiple metrics.

Towards Intelligent Control of Beaconing Power and Beaconing Rate in Vehicular Ad Hoc Networks

In this paper, to avoid congestion in the wireless channel of vehicular ad hoc networks (VANETs), a joint beaconing rate and beaconing power based on game theory are proposed in this paper. The game is formulated as a non-cooperative game, a Bayesian game, and a cooperative game. Three distributed and iterative algorithms (Best Response Algorithm, Fictitious Play Algorithm, and Cooperative Bargaining Algorithm) are proposed for computing the beaconing power and beaconing rate of each vehicle. Extensive simulations show the convergence of a proposed algorithm to the equilibrium beaconing power and beaconing rate and give some insights on how the game parameters may vary the game outcome.

Adaptive and Optimized Control Channel Utilization in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) are counted as a state-of-art technology which can be considered to provide reliable communication which is increase the safety and convenience of drivers in the road. Due to channel congestion occurrence in both highway and urban scenario, many VANET’s safety applications face degradation in their quality of service especially in a high-density environment. To improve the performance, reliability, and safety of VANETs, message overhead on the standard wireless channel dedicated short-range communications (DSRC) should be alleviated to guarantee safety-related applications. To this end, this paper proposes an approach to optimize the utilization of the control channel (CCH) and make the control channel interval (CCHI) adaptive to the emergency cases in the vehicular scenarios. More precisely, when there is an emergency cases, a part of service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery. When a vehicle intended to transmit the safety message to the surround vehicles, the proposed method granted that CCH is allocated to meet the traffic safety demands; then, the safety messages disseminate properly to the transmission range. A part of the service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery, Then, applying the laying chicken algorithm (LCA) to solve the derived CCH optimized utilization. Finally, extensive simulations are carried out to validate the proposed optimized CCH utilization. To validate the performance of the proposed adaptive congestion control approach (ACCA), we used the network simulator Ns-3 [35] to simulate the vehicle’s environment, after generating vehicles’ mobility traces of the nodes using the network simulator for urban mobility (SUMO) with OpenStreetMap (OSM). The simulation indicates that our congestion algorithm (ACCA) performs better service quality than other existing algorithms. We got a better network connection regarding safety messages, beacon rate with time, and packet length for different vehicles’ densities.

Reliability Evaluation of IEEE 802.11p Broadcast Ad Hoc Networks on the Highway

In a vehicular ad hoc network (VANET) based on IEEE 802.11p, the performance of the communication link is heavily influenced by interference. To quantify the impact of interference, analytical models usually explicitly or implicitly assumed the interference range beyond which interference is ignored. We find that the potential maximum interference range should be related to the minimum interference power that the device can perceive. However, most previous works on VANET performance modeling simply assumed a fixed interference range. The most recently proposed effective interference range dependent on the signal-to-interference-and-noise ratio (SINR) threshold may appear to exceed the potential maximum interference range, resulting in an overestimation of the impact of interference. This paper proposes the SINR related Effective Distance Constrained by the Maximum interference range-based (SED-CM) model for IEEE 802.11p VANET performance evaluation on the Highway. First, we give the potential maximum interference range setting by studying the empirical distribution of interference distance corresponding to the minimum interference power in NS2 simulation. The performance metrics are then derived. The proposed SED-CM model is further extended to the intersection scenario. We conducted extensive NS2 simulations to evaluate the proposed model and compare it with three typical models. Results show that the SED-CM model presents the best evaluation ability. Moreover, we experimentally analyzed the effect of vehicle speed on the performance of VANET by mapping the vehicle speed to the vehicle density. Results show that a faster beacon rate is required to meet the reliability requirement of safety applications with the increase in vehicle speed.

Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.

Joint Beacon Power and Beacon Rate Control Based on Game Theoretic Approach in Vehicular Ad Hoc Networks

In vehicular ad hoc networks (VANETs), each vehicle broadcasts its information periodically in its beacons to create awareness for surrounding vehicles aware of their presence. But, the wireless channel is congested by the increase beacons number, packet collision lost a lot of beacons. This paper tackles the problem of joint beaconing power and a beaconing rate in VANETs. A joint utilitybased beacon power and beacon rate game are formulated as a non-cooperative game and a cooperative game. A three distributed and iterative algorithm (Nash Seeking Algorithm, Best Response Algorithm, Cooperative Bargaining Algorithm) for computing the desired equilibrium is introduced, where the optimal values of each vehicle beaconing power and beaconing rate are simultaneously updated at the same step. Extensive simulations show the convergence of a proposed algorithm to the equilibrium and give some insights on how the game parameters may vary the game outcome. It is demonstrated that the Cooperative Bargaining Algorithm is a fast algorithm that converges the equilibrium.

MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETs

Vehicular ad-hoc communications rely on periodic broadcast beacons as the basis for most of their safety applications, allowing vehicles to be aware of their surroundings. However, an excessive beaconing load might compromise the proper operation of these crucial applications, especially regarding the exchange of emergency messages. Therefore, congestion control can play an important role. In this article, we propose joint beaconing rate and transmission power control based on policy evaluation. To this end, a Markov Decision Process (MDP) is modeled by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques. This MDP characterization, denoted as MDPRP (indicating Rate and Power), leverages the trade-off between beaconing rate and transmission power allocation. Moreover, MDPRP operates in a non-cooperative and distributed fashion, without requiring additional information from neighbors, which makes it suitable for use in infrastructureless (ad-hoc) networks. The results obtained reveal that MDPRP not only balances the channel load successfully but also provides positive outcomes in terms of packet delivery ratio. Finally, the robustness of the solution is shown since the algorithm works well even in those cases where none of the assumptions made to derive the MDP model apply.

Improving 802.11p for Delivery of Safety-Critical Navigation Information in Robot-to-Robot Communication Networks

Real-world robot networks create hostile radio environments due to their highly dynamic communication topologies and stringent real-time transmission requirements. In this article, we propose improvements to contention-based networks that utilize inter-robot navigation information for safer robot motion control in dense multi-robot settings. Toward this end, we introduce a Crash Risk Prioritization (CRP) index, which is employed as the core metric in a scheme of dynamic beacon rate control. Key to our approach is a coupled evaluation setup that allows us to evaluate the impact of network-level changes on the safety of robot navigation. Our results demonstrate that the co-design and simulation of robotic control algorithms and network protocols provide key insights and metrics into the network-level requirements for V2V systems. These insights allow us to make informed conclusions about how such networks can be best served by emerging standards.

An energy-efficient adaptive beaconing rate management for pedestrian safety: A fuzzy logic-based approach

Pedestrian road-safety applications require geolocation data at high refresh rate. This is achieved through high rate beaconing of the data from the safety devices owned by road users to servers. The servers perform threat analysis and send alerts to road users in case of risk of collisions. However, when low-battery devices like smartphones are used for such applications, they drain their energy rapidly as high beaconing rate entails quick energy consumption. In this paper, we propose an energy-efficient fuzzy logic-based adaptive beaconing rate management system while keeping traffic crashes on pedestrians as low as possible. The fuzzy inference system predicts road accident risk levels of pedestrians which are finally used to decide beaconing rates. The prediction relies on factors related to the pedestrians, drivers, vehicles, roads, environments, etc. The risk prediction accuracy of the fuzzy logic-based system is evaluated by comparing its output with the risk level prediction algorithm that relies on minimum distance for information exchange to predict the likelihood of accidents. Results show that collision risk level prediction of the two methods is the same for more than 90% of accident records investigated. Energy-efficiency evaluation of the adaptive beaconing rate management depicts that, the battery life of a mobile device can be doubled in comparison with a method that uses a mismanaged high rate beaconing frequency. Additional evaluations portray that energy consumption of adaptive beaconing rate is affected by vehicle arrival rates and factors that affect traffic safety. Comparative analysis with state-of-the-art related works indicates that despite considering several collision risk-pulling factors ignored by other researchers, the proposed solution has negligible energy overhead.

Towards Rear-End Collision Avoidance: Adaptive Beaconing for Connected Vehicles

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.

POSACC: Position-Accuracy Based Adaptive Beaconing Algorithm for Cooperative Vehicular Safety Systems

Cooperative vehicular safety systems are expected to revolutionize the driving experience by providing road safety applications based on incident detection. Two vital quality parameters for cooperative safety applications are the position accuracy and communication reliability of the status information. The receiver may take erroneous decisions if the received data does not correspond to the latest situation of the transmitter (e.g., position, velocity, and trajectory of the target vehicle). In this paper, we propose and evaluate a POSition-ACCuracy (POSACC) based adaptive beaconing algorithm for cooperative vehicular safety systems. POSACC integrates three different control mechanisms to guarantee specific performance metrics. It adopts the position accuracy and communication reliability as the highest priority metrics, due to their direct impact on the vehicle's systems capability to avoid potential traffic accidents in real-time. In addition, it guarantees the priority metrics, maintaining the vehicle's warning distance, channel load, and end-to-end latency into the operative range of cooperative safety applications. POSACC is compared with three different state-of-the-art adaptive beaconing algorithms; ETSI DMG, LIMERIC, and DC-BTR&P. Extensive evaluation results show that POSACC successfully controls the beacon rate, transmission power, and the size of the minimum contention window. Simulation results also demonstrate that POSACC is more effective than the benchmark algorithms by guaranteeing the operational requirements of cooperative safety applications in a wider range of traffic situations.

Fine-Grained TDMA MAC Design toward Ultra-Reliable Broadcast for Autonomous Driving

In the autonomous driving era, V2X communication is essential since it enables rapid message dissemination via periodical beacon exchange (broadcast communication), which contributes to better situation awareness and maneuvering cooperation. However, designing a MAC protocol for reliable V2X broadcasting is challenging, as minimal beacon delivery delay and collision avoidance should be achieved simultaneously. In this article, we design a fine-grained TDMA-based MAC protocol to support ultra-reliable broadcast for autonomous vehicles. Specifically, three critical issues are first identified: mobility-caused time slot collision, time slot shortage, and stiff beacon rate limitation. Accordingly, three fine-grained solutions are provided to tackle those issues: mobility-aware time slot assignment, beacon rate adaption with safety awareness, and flexible beacon rates support. Moreover, a case study on mobility-aware time slot assignment based on road topology and lane distribution is presented, with simulation results' verification. Finally, we elaborate the steps to implement the fine-grained MAC protocol in autonomous driving environments.

Social-Aware Peer Discovery for Energy Harvesting-Based Device-to-Device Communications.

In Device-to-Device (D2D) communications, the first step is to find all of the neighboring peers in the network by performing a peer discovery process. Most previous studies use the social behaviors of the users to adjust the sending rates of the peer discovery messages (i.e., beacons) under the constraint of consumed power for increasing the Peer Discovery Ratio (PDR). However, these studies do not consider the potential for energy harvesting, which allows for the User Equipments (UEs) to procure additional power within charging areas. Accordingly, this paper proposes an Energy-Ratio Rate Decision (ERRD) algorithm that comprises three steps, namely Social Ratio Allocation (SRA), Energy Ratio Allocation (ERA), and Beacon Rate Decision (BRD). The SRA step determines the allocated power quantum for each UE from the total budget power based on the social behavior of the UE. The ERA step then adjusts this allocated power quantum in accordance with the power that is harvested by the UE. Finally, the BRD step computes the beacon rate for the UE based on the adjusted power quantum. The simulation results show that ERRD outperforms the previously-reported Social-Based Grouping (SBG) algorithm by 190% on the PDR for a budget power of one watt and 8% for a budget power of 20 watts.

Identifying Transmission Opportunity through Transmission Power and Bit Rate for Improved VANET Efficiency

With the development of vehicular industry and wireless communication technology, vehicular ad hoc network (VANET) has become a popular research field. However, timely dissemination of safety related information in VANET is always challenging due to the highly dynamic vehicular environment. In this paper, we investigate an analytic approach in enhancing transmission efficiency of VANET by adapting transmission power and bit rate for IEEE 802.11p based VANET. The proposed algorithm, Joint Adaptation of Transmission power and Bit rate (JATB) algorithm uses modified Dynamic Programming (DP) to search for optimal value of transmission power and bit rate with the aim to increase packet success rate, minimize end-to-end delay and minimize busy time. JATB also uses vehicle density to improve vehicular environment awareness, and adapt transmission power and bit rate accordingly. Adaptability of JATB algorithm is tested with different beacon rates and simulation scenarios. Simulation results show an improvement in transmission efficiency as compared to standard protocol [original algorithm set in VANET standard which has fixed transmission power (20 mW) and fixed bit rate (6Mbps)] and reference journal’s Transmission Power Transmission Range (TPTR) [algorithm from reference journal that adapts transmission power according to transmission range] algorithms.

Time-to-Collision-Based Awareness and Congestion Control for Vehicular Communications

Vehicular wireless communications require both congestion control to guarantee the availability of a fraction of the bandwidth for safety-related event-driven messages in emergency cases, and awareness control to adapt the beaconing activity to the application needs and surrounding traffic situation. Most current approaches either ignore the traffic situation and only adapt the beaconing rate to the channel congestion state or override the congestion control limits, leading to questionable results in both cases. In this paper, we conceive and validate a novel approach, combining both aspects. Based on distributed Network Utility Maximization (NUM), our algorithm satisfies the constraints on channel availability, whereas the safety of the surrounding traffic situation is captured with a time-to-collision metric, used to assign priorities in the optimal allocation problem. The performance of the proposed approach is validated and compared to other popular algorithms. Results show that our proposal automatically anticipates a potential increase in rate due to a critical safety situation, but does not interfere with the reserved bandwidth for safety applications.

DBCC: Leveraging Link Perception for Distributed Beacon Congestion Control in VANETs

Under the IEEE 802.11p-based dedicated short range communication modules, vehicular safety applications rely on periodical broadcasts of safety beacons by each vehicle. However, the channel can be easily congested by high-frequency periodic beacons when the vehicle density becomes heavy. In this paper, through real-trace-based empirical study on vehicle-to-vehicle communication, we find that nonline-of-sight (NLoS) condition is the key factor on link performance degradation and blindly sending more packets in harsh NLoS conditions can hardly succeed but increase interferences to neighboring vehicles. Inspired by this, we propose a distributed beacon congestion control (DBCC) scheme to control beacon activities with considering link conditions, i.e., vehicles with more neighbors and better conditions of links with its neighbors, will be assigned with higher beacon rates. In DBCC, we first utilize two machine learning methods, i.e., naive Bayes and support vector machines, to train the features and output a classifier model which conducts online NLoS link condition prediction. With link status information, we then formulate a link-weighted safety benefit maximization (L-SBM) problem of the rate-adaptation under a TDMA broadcast MAC, which is proved to be NP-hard. A greedy heuristic algorithm for L-SBM is then proposed and the performance of the algorithm is evaluated. Extensive trace-driven simulations demonstrate the efficiency of DBCC design; particularly, the rate of beacon transmissions can be effectively controlled without exceeding the resource limit and the rate of transmission/reception collisions are greatly reduced.

A Self-decision-making Beacon Interval Adjusting Method for VANETs

In Vehicular Ad Hoc Network (VANET), rapid movement of vehicles leads to the neighbor information fast outdated. Fast exchange of beacons in high traffic density causes wireless channel congested and overload of VANET. In this article, a Self-Decision-Making Beaconing (SDMB) method is proposed to solve the above issues. In this method, the beaconing interval is adjusted based on node activity and the change rate of its neighbors. Results show that, the successful beaconing rate is closed to or larger than 90% and the normalized channel load is less than 20% in different traffic density by using SDMB.