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In this paper, we validate that the deterministic distance-based analytical model can be used to estimate the reliability of one-dimensional (1-D) 802.11 broadcast wireless networks compared with the interference-based analytical model. Therefore, we propose a deterministic distance-based reliability analytical framework for such networks in d-dimensional (d-D, $$d \ge 1$$ ) scenarios. This framework takes into account the fading channel and hidden terminal problem and makes three commonly used reliability metrics able to be resolved, including point-to-point packet reception probability (NRP), packet delivery ratio (PDR), and packet reception ratio (PRR). There are two key factors involved in deducing the effect of hidden terminals. One is to measure the hidden terminal transmission probability during the vulnerable period, which can be calculated based on the approximate solution of the semi-Markov process model capturing the channel contention and the back-off behavior. Another is the challenge to determine the size of the area to which the hidden terminals belong. First, we give a general mathematical expression on the size of the hidden terminal coverage affecting NRP which is an important part of the closed-form solution of NRP/PRR. Second, we adopt the Monte-Carlo method to solve the size of general hidden terminal coverage affecting PDR, making it possible to approximate PDR, as well as control the efficiency and accuracy by constraining the relative error. Finally, we adopt a multi-parameter optimization scheme to find the optimum settings for the network to ensure the quality of service and maximize channel utilization. A series of experimental results show that the framework can be used to access the reliability of 802.11 based d-D broadcast wireless network and pave the way for further optimization.

Safety applications would be one of the most important on-board implementations in Vehicular Ad-hoc Network (VANET). Broadcasting the Basic Safety Message (BSM) periodically could help drivers increase their awareness range for preventing casualties of traffic accidents or reducing the number of traffic accidents. Usually, the safety applications have more stringent performance requirements (e.g. time delay and reliability) than other kinds of applications. However, because the vehicle density and speed are both changed frequently, the optimal network transmission parameters should not remain the same. So, setting constant transmission parameters in all conditions may cause problems, such as channel congestion, packet collisions and so on, which could degrade Quality of Service (QoS) of safety applications. For the purpose of maximizing the transmission capacity and keeping the application-level QoS of safety applications meeting their requirements, this paper proposes an optimization scheme with standard Particle Swarm Optimization (PSO) to adjust transmission parameters dynamically. The experimental results show that the optimized transmission parameters can get better results on transmission capacity and awareness probability compared with the transmission parameters which are used in real testbeds.

Safety-related applications in Vehicular Ad-hoc Networks (VANETs) can help to reduce the number of traffic accidents by periodically broadcasting Basic Safety Messages (BSMs). But considering that the density and topology of VANET change frequently, the Quality of Service (QoS) of safety applications with the fixed transmission parameters would not always meet the requirements of safety-related applications. In this paper, we firstly propose an analytical model to evaluate the performance of the BSM broadcast in VANET at the intersections. The effect of the traffic light is also taken into account by introducing the non-homogeneous Poisson process (NHPP) vehicle distribution, which is simulated by microscopic traffic simulator SUMO and validated by Kolmogorov-Smirnov (K-S) test. Secondly, the number of vehicles in the hidden terminal area and the concurrent transmission area need to be computed by complex integral computations for the proposed analytical model for evaluating the QoS of the safety applications, while the vehicle BSMs provide the vehicle location, speed, and transmission parameters, and these BSMs could be measured and collected in a timely manner to integrate into the analytical model to save the complex integral calculations. Based on the cross-validation QoS metrics between the NS2 (Network Simulator-ns-2) simulation and the analytical model, we employ the vehicle entity of the NS2 simulation model to represent the actual vehicle BSMs, to obtain the number of vehicles in the hidden terminal area and the concurrent transmission area. Finally, to maximize the transmission capacity and minimize the delay under the constraint of maintaining a high application-level QoS of safety applications, a multi-objective optimization scheme with Bare Bones Particle Swarm Optimization (BBPSO) is proposed to dynamically adjust multi transmission parameters. The accuracy of the proposed analytical model is validated by the NS2 simulation. The experimental results also show that the optimized ones could get better results compared with the real test-bed used transmission parameters. Furthermore, the comparisons with slotted-p(or 1)-persistent protocol and CSMA/CA with retransmission strategy show that the proposed solution could make VANET work with better performance at various vehicle densities.