Abstract:Vehicular ad-hoc networks (VANETs) are promising research areas which mainly include three communication modes: vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and hybrid vehicle communication (HVC). But most of the current research on HVC mode in which vehicle nodes and infrastructures coexist only focuses on the analysis of the optimal single-type relay selection schemes. Inspired by this, in order to design an optimal multi-relay selection scheme which can select different types of relays simultan… Show more
“…Queuing model can describe the network model and analyze the system performance [10]. Therefore, it is an effective method to establish a queuing model to evaluate the performance of the secondary system in CRNs.…”
Section: B Related Workmentioning
confidence: 99%
“…, where I is the identity matrix. After finite iterations (4) (3 15] is updated to [0, 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. Finally, the matrix after layer updating is the steady-state distribution of matrix Q with size1 32 .…”
Section: Calculation Of the Steady-state Distributionmentioning
The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.
INDEX TERMSCognitive radio networks, burst traffic, network congestion, queuing model, twodimensional Markov chain, performance evaluation.
“…Queuing model can describe the network model and analyze the system performance [10]. Therefore, it is an effective method to establish a queuing model to evaluate the performance of the secondary system in CRNs.…”
Section: B Related Workmentioning
confidence: 99%
“…, where I is the identity matrix. After finite iterations (4) (3 15] is updated to [0, 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. Finally, the matrix after layer updating is the steady-state distribution of matrix Q with size1 32 .…”
Section: Calculation Of the Steady-state Distributionmentioning
The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.
INDEX TERMSCognitive radio networks, burst traffic, network congestion, queuing model, twodimensional Markov chain, performance evaluation.
“…Coincidentally, this idea coincides with us. In our finished work [26], an important conclusion that under a specific communication scenario (such as the communication distance between source nodes and destination nodes), there always exists an optimal number of relays in the system which can maximize the energy efficiency has been drawn. Based on this, is it possible to design an optimal relay number selection algorithm which can dynamically adjust the number of relays according to the changing communication distance?…”
Section: Timetable: the Development And Research Status Of Fanets 2020mentioning
confidence: 99%
“…On the other hand, because the very high mobility is the most distinct characteristic between FANETs and traditional terrestrial networks, we further select the short-term static feature to characterize the complex FANETs time-varying channel. It is worth noting that in our finished work for typical terrestrial networks such as VANETs [26] and WSNs [31], the longterm static Rayleigh fading is chosen as the channel model. The difference between long-term and short-term static feature lies in the changing frequency of channel fading coefficient.…”
Section: System Model and Optimal Relay Number Selection Algorithmmentioning
“…They apply real time messaging between vehicles (nodes) using radio wave transmission over specific distance (range). VANETS consist of vehicular mobile nodes, established without a centralized infrastructure, thus, each node will perform the functions of transmitter, receiver, and data router [22], [23], [24], [25], [26], [27], [28].…”
New approach to manage congestion using vehicular communication is presented in this work. The research work using MATLAB simulation, tracked communicating vehicles travelling on roads with constant registration of changes in routes, number of hops, and energy consumed as a function of travelled distances. The area of travel and simulation is divided into blocks or zones to enable sufficient allocation and distribution of Road Side Units (RSUs) that are used to relay communication signals and transmission of Basic Safety Messages (BSMs). The successfully concluded simulation is based on the assumption that as congestion occurs, the number of hops per route and associated energy consumption per transmitted packets will change patterns in terms of hops, routes and consumed energy as traffic passes from low to smooth (optimal) to high density (congestion) states, where at the start of congestion, vehicles start to slow down and become closer to each other in a two dimensional space. The output is used as input to traffic status pattern characterization algorithm (management system) that uses the data to indicate the start of traffic accumulation, thus pre-emptive measures can be taken to avoid congestion and reduction in mobility. The presented analysis proved that it is possible to predict congestion as a function of both hops sequences and consumed energy, depending on the hops pattern which is shown to be symmetric in the case of optimum traffic that flows smoothly. The analysis also showed that when congestion starts to occur, asymmetric hops pattern occurs with hops sequences elements switch and swap places within the identified pattern. Further analysis and polynomial curve fitting proved that congestion control and smooth traffic management using the proposed approach is achievable.
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