Snapshot for IoT: Adaptive Measurement for Multidimensional QoS Resources

Abstract

With the increasing and extensive use of intelligent Internet of things (IoT) devices, its operational aspect in the network has become a significant dependent data for network QoS management and scheduling. For the resilient intelligent IoT cluster with flexible increase and decrease of devices and heterogeneous operating systems, this paper proposes an adaptive measurement method MRAM, which can snapshot the multidimensional QoS resources view (MRV) of the IoT devices in cluster. MRAM uses the measurement offloading architecture based on extensible gateway platform and cloud computing to liberate the local resources of monitored IoT devices. Based on the improved LSTM algorithm, the MRV’s mutations detection method ELSTM is designed. Newly collected QoS resource can be judged whether mutations have occurred and adaptive measurement state machine is enabled by ELSTM. According to the state machine which ensures that the MRV is updated timely and reflected the current status of the cluster, MRAM adjusts the measurement granularity in real time. This method provides a high time efficiency global profile for the upper QoS services and reduces the impact of measurement on the IoT devices. A real environment is built to test the performance of this method. MRAM has high measurement accuracy and the precision of mutations detection is 98.29%. It converges the update MRV of second level under the condition of IoT devices’ low consumption of storage and CPU utilization.