01573nas a2200133   4500000000100000000000100001008004100002260003800043100001500081700001700096245009100113856003500204520120000239       2021                            d  bIEEEaNew Orleans, Louisiana, USA1 aMert Nakip1 aErol Gelenbe00aRandomization of Data Generation Times Improves Performance of Predictive IoT Networks  uhttps://wfiot2021.iot.ieee.org3 a<p>Input traffic from Internet of Things (IoT) devices is often both periodic and requires to be received by a given deadline. This can create congestion at instants of time when traffic flowing from multiple devices arrives at a shared input port or gateway, resulting in missed deadlines at the receiver. As a consequence, scheduling techniques such as the “Earliest Deadline First” (EDF) and “Priority based on Average Load” (PAL) are used to schedule the flow from different devices so as to try to satisfy the needs of the largest number of traffic flows in a timely fashion. In this paper, we propose the Randomization of flow Genaration Times (RGT) in order to smooth the total incoming traffic to the input port or gateway, on top of the use of EDF and PAL. We then evaluate the performance of RGT together with PAL and EDP, for traffic load with a varying number of up to 6400 IoT devices. Our simulation results show that RGT provides significantly better performance when added to EDF and PAL. Also, the additional computation required by RGT at each device can be quite small, suggesting that RGT is a very useful addition for improving the performance of IoT networks.</p>