A QoS Aware Uplink Scheduler for IoT in Emergency Over LTE/LTE-A Networks

A massive number of Internet-of-Things (IoT) devices are deployed to monitor and control a variety of physical objects as well as support a body of smart-world applications. How to efficiently allocate network resources becomes a challenging issue with the rapidly growing connected IoT devices. Depending on applications, the burst of IoT traffic could lead to the bandwidth deficiency within a short period of time and further deteriorates network performance. To tackle this issue, in this paper we first propose a Quality of Service (QoS) aware Normal Round Robin Uplink Scheduler (QNRR-US) over Long-Term Evolution (LTE)/LTE-Advance (LTE-A) networks. QNRR-US assigns a higher priority to IoT data that requires urgent treatment over normal IoT data, and then builds IoT devices’ scheduling queues based on priorities of data traffic. Thus, QNRR-US guarantees high priority data transmission. To provide fairness to IoT data, QNRR-US reserves some bandwidth for low priority data traffic. Based on QNRR-US, we then propose the QoS aware Bound Round Robin Uplink Scheduler (QBRR-US), which separates enormous IoT devices with burst data traffics and pushes them into service and waiting queue. The IoT devices in service queue take part in round robin resource allocation until the transmission of urgent data from the IoT device is complete and the new IoT device enters service queue from waiting queue for the next turn of resource allocation. Through simulations in NS-3, our experimental results show that QBRR-US outperforms the traditional proportional fair (PF) scheduler and QNRR-US with respect to throughput, packet loss ratio, and packet delay.