Most of the networks deployed for massive IoT communications use Aloha-based algorithms for channel access. However, those algorithms are known to be unstable and inefficient when the network size is high. Since recently, a Distributed Queuing (DQ) algorithm is being proposed as a solution to mitigate several of the Aloha issues in IoT networks. In this paper, a statistical performance analysis of the DQ algorithm without any prior consideration of any physical layer is presented. We evaluate the DQ algorithm in a massive communication environment and give the average values for these performance metrics: collision resolution time, access delay per sensor, channel throughput, number of attempts required by a sensor to complete the contention process, number of nodes contending per frame and the distribution of contention slots into idle, successful, and collided. The goal of this paper is to provide a statistical baseline performance evaluation of the DQ algorithm in general.
Networks such as LTE-M, NB-IoT, LoRa and SigFox are being deployed for massive IoT communications. However, the Aloha protocols used for the media access are inefficient when the network size is high. As a result, a Distributed Queuing (DQ) algorithm is being proposed to replace the conventional access schemes because of its superior network performance. In this paper, we study the algorithm with no prior consideration of any underlying technology at the physical layer. We perform a steady-state analysis of the algorithm in a network where the traffic is composed of periodic and urgent packets. It was found that for high traffic from the network all the nodes were taking part in each contention. Nevertheless, for better performance in terms of access delay and packet drop rate, it was preferable to operate the algorithm in the small traffic interval. Moreover, the downlink traffic has been observed to have a significant impact on the stability of the algorithm.
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