PROJECT TITLE :

Solar Power Prediction Assisted Intra-task Scheduling for Nonvolatile Sensor Nodes

ABSTRACT:

With the advent of the time of trillion sensors, solar-powered sensor nodes are widely used as they are doing not need battery charging or replacement. But, the limited and intermittent solar energy offer seriously affects deadline miss rate (DMR) of tasks. Furthermore, ancient solar-powered sensor nodes conjointly suffer from energy loss of battery charging and voltage conversion. Recently, a storage-less and converter-less power supply design has been proposed to achieve higher energy efficiency by removing the leaky energy storage and dc voltage conversion. Without energy storages, a node using inter-task scheduling is a lot of sensitive to solar variations, which ends up in high DMRs. This paper proposes an intra-task scheduling theme for the storage-less and converter-less solar-powered sensor nodes, whose options include power prediction based on classified solar profiles, a trigger mechanism to select scheduling points, a man-made neural network to calculate task priorities and a fine-grained task selection algorithm. Experimental results show that the proposed algorithm reduces DMR by up to thirty% and improves energy utilization potency by 20% with trivial energy overheads.