Research Area:  Wireless Sensor Networks
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≥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.
Author(s) Name:  Jing Zhao, Zhijuan Li, Yanbin Wang, Zhuofei Wu, Xiaomin Ma & Yue Zhao
Journal name:  Wireless Networks
Publisher name:  Springer
Volume Information:  volume 26, pages 3373–3394 (2020)
Paper Link:   https://link.springer.com/article/10.1007/s11276-020-02268-5