Snapshot for Power Grids IoT: Adaptive Measurement for Resilience Intelligent Internet of Things

With the wide application of Internet of Things (IoT) devices in the power grids, the sophisticated feedback on their operating status is of great significance for improving efficiency and reducing accidents. For exquisite management of the resilient intelligent IoT with flexible increase and decrease of devices and heterogeneous operating systems, this article proposes an adaptive measurement method “MRAM,” which can snapshot the multidimensional resource view (MRV) of all devices in the jurisdiction. Extensible gateway platform based on CPU, field-programmable gate array, and cloud computing is applied in MRAM, which liberates the local resources of monitored IoT devices. MRAM improves the long short-term memory algorithm called ELSTM. ELSTM can accommodate the current IoT devices’ state for detecting the mutation of MRV. The newly collected resources determined by ELSTM whether MRAM enters an abnormal state to drive the adaptive measurement state machine. According to the state machine which endeavors that the MRV is updated timely, MRAM adjusts the measurement granularity in real time. Simulations and experiments have tested the convergence time and occupied bandwidth of MRAM deployed in power grids. These evaluations confirmed MRAM’s practicality and robustness, as well as the MRV is genuine management data for the upper layer power grids applications. A real environment is built to test the performance of this method as well. MRAM has high measurement accuracy and the precision of mutation detection is 98.41%. It converges the update MRV of second level under the condition of IoT devices and the cloud’s low consumption of memory and CPU utilization.