A Block Prefetching Framework for Energy Harvesting IoT Devices

Abstract

The advancement of the Internet of Things has enabled numerous applications ranging from smart wearable to connected vehicles. However, the limited energy and memory resources of low-end IoT devices significantly impede their further flourish. In this article, we consider a system that consists of an IoT device and an edge server. The edge server stores code blocks for the IoT device and loads required blocks to the IoT device for execution thereby alleviates the latter from the limited memory resource. Furthermore, the IoT device can harvest energy from the ambient energy sources to achieve a sustainable operation. To deal with the dynamic energy harvesting process and block request process, we propose a stochastic block prefetching framework (BPF) to optimize the user experienced delay. The BPF assists the IoT device to intelligently prefetch blocks from the edge server according to the historical user behaviors. The BPF consists of three modules, i.e., estimation module, prefetching module, and dual learning module. The estimation module measures the probability of block being requested in the future. The prefetching module requests blocks from the edge server according to the available energy and memory. The dual learning module helps to accelerate the convergence of the framework. The numerous simulation results are provided to verify the effectiveness of the proposed framework.