无信标支持的IEEE 802.15.4网络重传和ACK模式的性能分析
Performance analysis of a non-beacon enabled IEEE 802.15.4 network with retransmission and ACK mode
摘要：
我们的工作的目的是评估在一个更精细的时间单位的确认（ACK）模式和重传限制的非信标IEEE 802.15.4网络的性能。此外，我们预测网络的性能参数，使用反向传播人工神经网络（BP-ANN）相对于一个给定的服务质量为现实世界的应用。因此，我们提出的方法可以帮助部署一个星型网络，其性能指定在一个更实际的方式。首先，采用离散时间马尔可夫链模型和M/M/1/k排队描述了无信标网络中的完全非时隙载波侦听多址接入冲突避免（CSMA/CA）算法。考虑到MAC缓冲区大小，不饱和流量和饱和流量，我们建立了三个分析模型，以得出八个重要的性能指标，例如，吞吐量、延迟和可靠性。此外，大量的仿真结果表明，在吞吐量和可靠性的分析模型的准确性。最后，我们使用分析数据来训练BP-ANN模型，以预测节点数和延迟等关键参数。所有的仿真数据用于测试的BP神经网络的结果表明，这些模型的准确性。因此，这些方法和结果可以用于部署应用环境中的星型网络。
英文摘要：
The aim of our work is to evaluate the performance of non-beacon IEEE 802.15.4 networks with acknowledgement (ACK) mode and retransmission limits in a finer time unit. Moreover, we predict network performance parameters using backpropagation artificial neural networks (BP-ANNs) with respect to a given quality of service for real-world applications. Therefore, our proposed methods can assist the deployment of a star network with its performance specified in a more practical way. First, the discrete time Markov chain model and M/M/1/k queue are used to describe the full unslotted carrier sense multiple access with collision avoidance (CSMA/CA) algorithm in a non-beacon network. Considering MAC buffer size, unsaturated traffic, and saturated traffic, we build three analytical models to derive eight important performance metrics, e.g., throughput, delay, and reliability. In addition, extensive simulation results show the accuracy of the analytical models in throughput and reliability. Finally, we use the analytical data to train the BP-ANNs models to predict the key parameters such as node number and delay. All results from the simulation data used to test the BP-ANNs show the accuracy these models. Thus, these methods and results can be used to deploy star networks in application environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
