A REVIEW OF ENERGY-EFFICIENT TASK SCHEDULING IN IOT CLOUD, FOG, AND EDGE SYSTEMS

Abstract

The resulting fast growth in the number of IoT ecosystems has made the demand of sophisticated, power-efficient task-scheduling algorithms that can handle dynamic workloads and hetero-geneous devices and provide strict requirements on latency. The classic scheduling techniques are usually based on fixed settings or cloud-based processing where it consumes too much energy and hampers the performance of the network edge. To solve the energy-latency trade-off in IoT-edge-cloud systems, it is suggested in this paper to dynamically and cross-layer schedule an application, considering real-time system monitoring, a lightweight neural prediction module, and decision optimization with the help of DVFS. The neural predictor which has been trained on skip-layer connections and an entropy-based fitting has a good feature separation as seen through the sorted weight-magnitude analysis and SSR of 335 which means that the predictor is stable when it comes to predicting computation and communication needs. With iFogSim2, EdgeCloudSim and Google Collaboratory, the system demonstrates an up to 27 percent decrease in overall energy use as well as the 95th-percentile latency with different mobility and workload situations. The findings affirm that the suggested approach provides a high-quality, scalable, and energy-conscious scheduling solution that can be utilized in the website of current IoT applications.