  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.
