High Vehicular Density Research Articles

The Shared Experience Actor–Critic (SEAC) Approach for Allocating Radio Resources and Mitigating Resource Collisions in 5G-NR-V2X Mode 2 Under Aperiodic Traffic Conditions

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns.

“Characterization of residential proximity to sources of environmental carcinogens in clusters of Acute Lymphoblastic Leukemia in San Luis Potosi, Mexico”

BackgroundAcute Lymphoblastic Leukemia (ALL) is the most prevalent neoplasia in children and teenagers in Mexico. Although epidemiological data supports that children's residence close to emissions from vehicular traffic or industrial processes increases the risk of ALL; and the IARC states that benzene, PAHs, and PM 2.5 are well-known environmental carcinogens, there is a gap in linking these carcinogenic hazards with the sources and their distribution from scenario perspective. AimTo identify ALL clusters in the population under 19 years of age and characterize the environment at the neighborhood level by integrating information on sources of carcinogenic exposure using spatial analysis techniques in the Metropolitan Area of San Luis Potosi, Mexico. MethodsUsing the Kernel Density test, we designed an ecological study to identify ALL clusters from incident cases in the population under 19 years of age. A multicriteria analysis was conducted to characterize the risk at the community level from carcinogenic sources. A hierarchical cluster analysis was performed to characterize risk at the individual level based on carcinogenic source count within 1 km for each ALL case. ResultsEight clusters of carcinogenic sources were located within the five identified ALL clusters. The multicriteria analysis showed high-risk areas (by density of carcinogenic source) within ALL clusters. ConclusionsThis study has a limited source and amount of available data on ALL cases, so selection bias is present as well as the inability to rule out residual confounding factors, since covariates were not included. However, in this study, children living in environments with high vehicular density, gas stations, brick kilns, incinerators, commercial establishments burning biomass, or near industrial zones may be at higher risk for ALL.

FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET

In a continuous data stream vehicular network environment, a Traffic Congestion Detection Service (TCDS) receives many periodic information to update and discover road segments with low speeds and high vehicular density. The use of Cloud computing to support the massive amounts of traffic data received from multiple vehicles significantly increases network traffic. Therefore, we propose FogJam, a Fog service to detect traffic congestion directly at the edge of the vehicular network. In the network edge, FogJam leverages sampling and clustering-based methods to reduce the traffic data stream transmitted by all vehicles on the network links to the Cloud. In the Cloud, FogJam is used as a macro-control of all vehicles geographic positions, acquiring traffic flow data to detect traffic congestion. We evaluate FogJam using OMNeT++, Veins and SUMO simulators. The results suggest that FogJam is highly accurate in detecting traffic congestion at a lower cost, even in a high vehicular density scenario. Furthermore, using clustering-based methods, FogJam is able to, on average, reduce the impact on network usage by approximately 70% compared to the sampling methods, while maintaining an acceptable level of congestion detection accuracy.

Design and Implementation of Intelligent Traffic Control System using Programmable Logic Controller

Generally, traffic light systems are regarded as open loop control systems, that is, they are in continuous operation without any form of feedback that can help the system take a decision based on certain conditions. And so, this is specifically part of the reason why there exist high vehicular densities at roundabouts. Having considered in providing a solution to this problem, this project integrated a 4I/O Programmable Logic Controller (PLC) into the traffic light system circuitry which provided feedback based on signals received from the input sensors connected to the PLC. When these sensors get energized due to the presence of a vehicle, it sends an ON-bit signal to the PLC through the input port terminating the sensor and the PLC. This signal in turn triggers the output port mapped in the microprocessor so that it energizes the Light Emitting Diodes (LEDs). During simulation, two forms of control were used: Primary (default) and Secondary control systems. On the secondary control, the PLC receives signal from the ultrasonic sensor on road tracks A and B then allows long queued vehicles to pass while it stops vehicular movement on road track C and D and vice versa. It is identified that this method of traffic light control has helped to ensure orderliness and evenly distributed vehicular flow at case study roundabout.

Stochastic analysis of urban V2X communications: Orthogonality versus non-orthogonality

In this paper, we study the broadcasting performance of urban Vehicle-to-everything (V2X) communications at road intersections. In such environments, dense buildings obstruct the radio channel between vehicles in adjacent road segments and Non-Line-Of-Sight (NLOS) signal propagation reduces the received power level. Therefore, messages that originate from adjacent roads traveling in a non-parallel direction usually have weaker signals than those from the road segment traveling in parallel opposite direction. Besides, the high vehicular density leads to insufficient orthogonal channel resources and high level of interference. We propose to apply Non-Orthogonal Multiple Access (NOMA) to V2X communications at urban road intersections to enhance the spectrum efficiency and improve the Package Delivery Ratio (PDR) performance. With NOMA, signals from multiple transmitters are decoded together and the interference from undesired users can be canceled if these messages are retrieved. Specifically, we study two NOMA-based V2X communication schemes, namely, NOMA-V2X decoded by Successive Interference Cancellation (SIC-V2X) and NOMA-V2X decoded by Joint Decoding (JD-V2X). Based on the tools developed in stochastic geometry, we derive and compare the PDR expressions for both NOMA schemes and the Orthogonal Multiple Access (OMA) scheme. Our results indicate that 1) both NOMA schemes outperform the conventional OMA scheme and the PDR of LOS/NLOS communications with two-user access increases by 51%/369%; 2) for four-user access, the proposed NOMA scheme shows 375% goodput enhancement as compared with the OMA scheme; and 3) JD-V2X provides significant PDR enhancement compared with SIC-V2X in the high data rate regime. Finally, we also demonstrate that the random access based NOMA schemes can achieve lower transmission latency than collision-free scheduling schemes when the road is over 56% jammed.

Performance Assessment of DTN and VANET Protocols for Transmitting Periodic Warning Messages in High Vehicular Density Networks

Performance Assessment of DTN and VANET Protocols for Transmitting Periodic Warning Messages in High Vehicular Density Networks

Evolution of size and composition of fine particulate matter in the Delhi megacity during later winter

This study presents the first multi-site evolution analysis of fine Organic Aerosols (OA) and inorganics in the Delhi megacity (National Capital Region (NCR)) using high-resolution-particle time of flight (HR-PToF) size distribution obtained from Aerodyne HR-ToF-AMS. It provides a comprehensive view of the atmospheric dynamic processes responsible for the aerosol size and composition transformation. The measurements were performed at two different sampling sites (1) Indian Institute of Technology, Delhi (IITD), and (2) Manav Rachna International University, Faridabad (MRIU) during a late winter period (February to March 2018). Among organics, primary OA (POA) showed particle growth, whereas it was absent in secondary OA proxies. IITD was observed to have a distinct growth pattern as compared to MRIU because of high vehicular density and heterogeneity, and the presence of growth-promoting factors such as acidity and RH conditions. IITD aerosols were observed to be more acidic (ANR∼ 0.75) compared to MRIU (ANR∼1) and showed a distinct diurnal bin-wise increase during the photochemically active period (PAP). Such high acidic conditions are responsible for promoting acid-catalyzed SOA productions over the broader size bins, especially during the PAPs. Primary OA (POA) such as HOA and BBOA proxies show a diurnal growth from ∼440 to 970 nm and from ∼370 to 550 nm, at IITD and from ∼270 to 400 nm and ∼430–470 nm, at MRIU respectively. Further, OA families (amines and hydrocarbons), and inorganics such as chloride and nitrate also showed distinct concurrent diurnal growth patterns at IITD (from an MMDs of ∼380–520 nm to 650–960 nm). We have also observed a positive correlation between OA and inorganics growth and the mass fraction (MF = SOA/OA) of SOA at both sites of NCR. For 0.1 (MF) increase in SOA, the sizes of HOA, chloride, primary CH family and amines (CHN family), and secondary amines (CHON family) are increased by ∼93, 80, 64, 66, and 47 nm at IITD and ∼14, 35, 8, 11, and 16 nm at MRIU respectively. The concurrent growth of these species with the increase in the SOA concentration indicates the existence of a similar size evolution mechanism at both NCR sites, further promoting the formation of internally mixed aerosols during some phase of their evolution. This suggests that condensation is significantly governing the growth mechanism in NCR. Our study indicates the inclusion of such dynamic growth patterns arising from local prevailing conditions into the models for a better understanding of atmospheric aerosol evolution and estimation of fine particulate matter budget.

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle’s speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter’s help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle’s high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China’s Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput.

Extreme events in Nagel–Schreckenberg model of traffic flow on complex networks

The Nagel–Schreckenberg vehicular traffic model is extended to analyse a system of vehicles moving on a scale-free network to study extreme events in them. The dependence of the free-flow and congestion states on the density of vehicles is determined. In particular, a free-flow state at low vehicular density is observed and it gradually transitions to a congested state at higher density. Using detrended fluctuation analysis, it is shown that the flux of vehicles are long range correlated in a regime of low density. At higher densities, the flux becomes uncorrelated. We study the recurrence interval distribution for extreme events and the probability for its occurrence at low and high vehicular density regimes. It is shown that the occurrence probability for extreme events is independent of the degree of the node or the threshold used for defining extreme events

Spectrum Sensing on High Density Cognitive Radio Vehicular Ad Hoc Network

Intelligent Transport System (ITS) has emerged as the most probable technology for improved transport experience more so in environments with high vehicular density. Effective vehicular communication is however hindered by spectrum scarcity due to the already crowded licensed spectrum. This has led to the emergence of Cognitive Radio (CR) systems as a solution to the spectrum scarcity problem. A crucial component in CR is spectrum sensing. Various spectrum sensing techniques including Cyclostationary, Matched Filter, and Energy detection have been proposed and applied with varied outcomes. In this paper, the above mentioned detection techniques are discussed and an improved energy detection based cooperative spectrum sensing scheme is proposed for improved communication in vehicular ad hoc networks (VANET). The proposed scheme showed an improvement in the performance of a network which in turn could lead to more efficient utilization of spectrum.

Health Risk Assessment, Composition, and Distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in Drinking Water of Southern Jharkhand, East India.

The studies on polycyclic aromatic hydrocarbons (PAHs) occurrence, distribution, health risk, and composition in drinking water are limited in India and worldwide. The main objective of this study was to find the contaminant sources, composition, health risk, and distribution of USEPA's 16 priority pollutant PAHs in the drinking water samples collected between July 2019 to September 2019 from six districts of Southern Jharkhand. The Σ16PAHs mean ± standard deviation [SD] concentration values were ordered as East Singhbhum (ES) (21.5 ± 14.8ng L-1) > West Singhbhum (WS) (16.57 ± 13.21ng L-1) > Saraikela Kharsawan (SK) (11.48 ± 9.92ng L-1) > Khunti (KH) (10.32 ± 9.09ng L-1) > Simdega (SM) (9.96 ± 7.85ng L-1) > Gumla (GU) (9.41 ± 8.63ng L-1). The results show that ES and WS districts' groundwater samples were more contaminated by the PAHs, which may be attributed to the presence of many small-, medium-, and large-scale industries and high vehicular density in these districts. The concentrations of lower molecular weight ring (3-rings) and middle molecular weight ring (4-rings) PAHs were dominant throughout all drinking samples. The concentration of the 3-ring PAH Anthracene and 4-ring PAH Fluoranthene were dominant in all districts. The molecular ratios suggested that the potential sources of PAHs are fuel combustion and coal, grass, and wood burning. Risk assessment shows that the incremental lifetime cancer risk and risk index (RI) were ranged from 0.02 × 10-10 to 4.93 × 10-10 for children and 0.01 × 10-10 to 2.98 × 10-10 for adults. The RI values for seven carcinogenic PAHs were 8.83 × 10-10 for children and 7.38 × 10-10 for adults. Although the carcinogenic risks were within the permissible values, chronic exposure to PAHs through the ingestion of drinking water could still be a human health concern.

Mitigating message dissemination issues in safety applications for vehicular ad hoc networks

SummaryIn a vehicular ad hoc network (VANET) each node is required to transmit periodic and event‐driven messages. The periodic messages are also known as beacons and consist of the basic vehicle status, such as position, speed, direction, and any additional information required by a safety application. One of the main concerns faced with this technology is channel congestion, which often happens in scenarios with a high vehicular density like traffic jams. When the channel is congested, there are no guarantees for a reliable message delivery. For this purpose, several protocols have been proposed to resolve this issue. In this work we propose neighbor congestion avoidance protocol (NCAP) as a novel solution employing a proactive strategy with vehicle position prediction using Kalman filter and an efficient event‐driven message delivery scheme. When compared with other strategies in congested scenarios, NCAP was able to significantly decrease the number of beacons by 49%, while also reducing the number of event‐driven message retransmissions by approximately 81%. NCAP also presented better performance in regard to the number of collisions between messages and channel busy ratio.

RSU-Assisted Traffic-Aware Routing Based on Reinforcement Learning for Urban Vanets

In urban vehicular ad hoc networks (VANETs), the high mobility of vehicles along street roads poses daunting challenges to routing protocols and has a great impact on network performance. In addition, the frequent network partition caused by an uneven distribution of vehicles in an urban environment further places higher requirements on the routing protocols in VANETs. More importantly, the high vehicle density during the traffic peak hours and a variety of natural obstacles, such as tall buildings, other vehicles and trees, greatly increase the difficulty of protocol design for high quality communications. Considering these issues, in this paper, we introduce a novel routing protocol for urban VANETs called RSU-assisted Q-learning-based Traffic-Aware Routing (QTAR). Combining the advantages of geographic routing with the static road map information, QTAR learns the road segment traffic information based on the Q-learning algorithm. In QTAR, a routing path consists of multiple dynamically selected high reliability connection road segments that enable packets to reach their destination effectively. For packet forwarding within a road segment, distributed V2V Q-learning (Q-learning occurs between vehicles) integrated with QGGF (Q-greedy geographical forwarding) is adopted to reduce delivery delay and the effect of fast vehicle movements on path sensitivity, while distributed R2R Q-learning (Q-learning occurs between RSU units) is designed for packet forwarding at each intermediate intersection. In the case of a local optimum occurring in QGGF, SCF (store-carry-forward) is used to reduce the possibility of packet loss. Detailed simulation experimental results demonstrate that QTAR outperforms the existing traffic-aware routing protocols, in terms of 7.9% and 16.38% higher average packet delivery ratios than those of reliable traffic-aware routing (RTAR) and greedy traffic-aware routing (GyTAR) in high vehicular density scenarios and 30.96% and 46.19% lower average end-to-end delays with respect to RTAR and GyTAR in low vehicular density scenarios, respectively.

An effective handover management based on SINR and software‐defined network over urban vehicular ad hoc networks

AbstractUrban vehicular ad hoc network (UVANET) is a modality of high‐topology variation MANET where vehicles travel across constrained layout of the roads and tall buildings, which eventually results in shadowing effects. One of the critical challenges in UVANET is the handover process that happens when a vehicle changes its association with the roadside unit. Urban area suffers from high vehicular density at traffic signals, and vehicle slowdowns caused by speed limits, frequent link disconnection caused by signal attenuation due to obstacles. Thus, it is difficult for UVANET to disseminate the packets to their destinations located in a specified area. This paper presents an effective handover management that calculates the signal‐to‐interference‐plus‐noise ratio (SINR) to ultimately determine the optimal cluster head for seamless handover in UVANET. It also utilizes the software‐defined network to resolve the problem of handover failure in UVANET. The finding in the experimental results shows that the developed association clustering mechanism based on SINR significantly outperforms the baseline association mechanism. More importantly, it can resolve different types of handover problems that improved the quality of service in terms of throughput, handover latency, packet loss ratio, and handover failure ratio, when compared to the baseline handover mechanisms.

Modeling and performance analysis of VI‐CRA: A congestion control algorithm for vehicular networks

SummaryThis study proposes a Vehicle ID‐based CAM Rate Adaptation (VI‐CRA) algorithm for beacon messages in the vehicular network. Foremost, an improved vehicle ID–based analytical model is proposed at the MAC layer of vehicular network. The model weighs the random back‐off number chosen by vehicles participating in the back‐off process, with the vehicle ID incorporated in their respective CAMs. This eventually leads to the selection of a vehicle ID–based random back‐off number, minimizing the probability of collision due to same back‐off number selection. It is worth noting that the improved analytical model outperforms the existing works in terms of average packet delay since only one fourth of the contention window size is used throughout the simulation. To enhance the performance of the analytical model, the paper incorporates a congestion control algorithm, by adapting the rate of CAM broadcast over the control channel. The algorithm is designed considering a wide range of scenarios, ranging from nonsaturated to extremely saturated network (in terms of collision probability) and sparsely distributed to teemed network (in terms of vehicular density). For better analyses of simulation results, the algorithm is applied over different vehicle ID–based back‐off numbers. Simulation results for all the back‐off numbers show that vehicle ID–based CAM rate adaptation algorithm performs better than the traditional fixed CAM rate IEEE 802.11p, even at high vehicular density.

Influences of boundary layer phenomena and meteorology on ambient air quality status of an urban area in eastern India

Monitoring and analyses of various air pollutants like particulate matter (PM 10 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), carbon monoxide (CO) and ammonia (NH 3 ), as well as meteorological parameters (temperature, humidity, wind speed and direction) have been performed for the period between April and May, 2014 at three different sites (an area with high vehicular density, a residential area and an industrial area) of an urban area in eastern India. The hourly average concentration of surface ozone is found maximum at the industrial site (70.085 µg/m 3 ) followed by the residential site (39.067 µg/m 3 ) and the site with high vehicular density (25.936 µg/m 3 ). The site with high vehicular density shows the maximum 24 h average concentration of NO 2 (86.112 µg/m 3 ) and NH 3 (109.673 µg/m 3 ). Maximum daily (24 h) average concentration of PM 10 (392.127 µg/m 3 ) and SO 2 (174.069 µg/m 3 ), and highest hourly average of CO (3.846 mg/m 3 ) at the industrial site can be attributed to emissions from the surrounding industries. Computation of air quality indices (AQI) show that both site I (a traffic congested area with AQI = 53.85) and site III (an industrial area with AQI = 71.68) fall under a moderately polluted category whereas site II (a residential area with AQI = 22.69) is found to be clean. This detailed study reveals that variations in the concentration of different air pollutants manifest the combined effect of emission sources and patterns, planetary boundary layer height and meteorology.

Spatio-Temporal Variation and Futuristic Emission Scenario of Ambient Nitrogen Dioxide over an Urban Area of Eastern India Using GIS and Coupled AERMOD-WRF Model.

The present study focuses on the spatio-temporal variation of nitrogen dioxide (NO2) during June 2013 to May 2015 and its futuristic emission scenario over an urban area (Durgapur) of eastern India. The concentration of ambient NO2 shows seasonal as well as site specific characteristics. The site with high vehicular density (Muchipara) shows highest NO2 concentration followed by industrial site (DVC- DTPS Colony) and the residential site (B Zone), respectively. The seasonal variation of ambient NO2 over the study area is portrayed by means of Geographical Information System based Digital Elevation Model. Out of the total urban area under consideration (114.982 km2), the concentration of NO2 exceeded the National Ambient Air Quality Standard (NAAQS) permissible limit over an area of 5.000 km2, 0.786 km2 and 0.653 km2 in post monsoon, winter and pre monsoon, respectively. Wind rose diagrams, correlation and regression analyses show that meteorology plays a crucial role in dilution and dispersion of NO2 near the earth’s surface. Principal component analysis identifies vehicular source as the major source of NO2 in all the seasons over the urban region. Coupled AMS/EPA Regulatory Model (AERMOD)–Weather Research and Forecasting (WRF) model is used for predicting the concentration of NO2. Comparison of the observed and simulated data shows that the model overestimates the concentration of NO2 in all the seasons (except winter). The results show that coupled AERMOD–WRF model can overcome the unavailability of hourly surface as well as upper air meteorological data required for predicting the pollutant concentration, but improvement of emission inventory along with better understanding of the sinks and sources of ambient NO2 is essential for capturing the more realistic scenario.

Adaptive Beaconing in Mobility Aware Clustering Based MAC Protocol for Safety Message Dissemination in VANET

Majority of research contributions in wireless access in vehicular environment (WAVE)/IEEE 802.11p standard focus on life critical safety-related applications. These applications require regular status update of vehicle’s position referred to as beaconing. Periodic beaconing in vehicle to vehicle communication leads to severe network congestion in the communication channel. The condition worsens under high vehicular density where it impacts reliability and upper bound latency of safety messages. In this paper, WAVE compliant enhancement to the existing IEEE 802.11p protocol is presented which targets prioritized delivery of safety messages while simultaneously provisioning the dissemination of nonsafety messages. Proposed scheme relies on dynamic generation of beacons to mitigate channel congestion and inefficient bandwidth utilization by reducing transmission frequency of beacons. Through the use of clustering mechanism, different beaconing frequencies and different data transmission rates are assigned to prioritize vehicular mobility. Through extensive simulation results, the performance of the proposed approach is evaluated in terms of a wide range of quality of service (QoS) parameters for two different transmission ranges. Results show that the proposed protocol provides significant enhancement and stability of the clustered topology in vehicular ad hoc network over existing standard and other protocols with similar applications.

Evaluating the Impact of Transmission Range on the Performance of VANET

Recently, interest in the field of Vehicular Ad-hoc Networks (VANETs) has grown among research community to improve traffic safety and efficiency on the roads. Despite the many advantages, the transmission range in vehicular network remains one of the major challenges due to the unique characteristics of VANETs such as various communication environments, highly dynamic topology, high node mobility and traffic density. The network would suffer from a broadcast-storm in high vehicular density when a fixed transmission range in VANET is used, while in sparse vehicular density the network could be disconnected frequently. In this paper, we evaluated the impact of different transmission ranges and number of flows formed between vehicles in a highway scenario using AODV as routing protocol. In order to validate the simulation of VANET, traffic and network simulators (SUMO & NS-2) have been used. The performance was evaluated in terms of packet delivery ratio and end-to-end delay. The simulation results have shown that better performance was achieved in term of higher PDR and lower end-to-end delay for less than 500 meters transmission range. On the contrary, the PDR started to decrease and end-to-end delay increased when the transmission range exceeded 500 meters. The performance degraded as the number of flows increased.

Bayesian game-theoretic approach based on 802.11p MAC protocol to alleviate beacon collision under urban VANETs

Vehicular Ad-hoc Networks (VANETs) facilitate the broadcasting of status information among vehicles. In the IEEE 802.11p/WAVE vehicle network environment, the strict periodic beacon broadcasting of safety messages requires status advertisement to assist drivers in maintaining safety. The beacon broadcasting is required for real-time communication, and for avoiding the degradation of communication channels in high vehicular density situations. However, a periodic safety beacon in the IEEE 802.11p/WAVE standard can only transmit packets on a single channel using the MAC protocol. In high vehicular density situations, the channel becomes overloaded, thereby increasing the probability of beacon collision, and hence reducing the influx of successfully received beacons, which increases the delay. Many studies have indicated that appropriate congestion control algorithms are essential to provide efficient operation of a network. In this paper, to avoid beacon congestion, we have considered game theoretic models of wireless medium access control (MAC) where each transmitter makes individual decisions regarding their power level or transmission probability. We have evaluated the equilibrium transmission strategies of both the selfish and the cooperative user. In such a game-theoretic study, the central question is whether Bayesian Nash equilibrium (BNE) exists, and if so, whether the network operates efficiently at the equilibrium point. We proved that there exists only one BNE point in our game and validated our result using simulation. The performance of the proposed scheme is illustrated with the help of simulation results.