Energy-Aware Task Scheduling in Contiki

Applications for the Internet of Things often run on devices that have very limited energy capacity. Energy harvesting can offset this inherent weakness of these devices by extracting energy from the environment. Energy harvesting increases the total energy available to a device, but efficient energy consumption is still important to maximize the availability of the device. Energy-aware task scheduling is a way to efficiently consume energy in an energy constrained device with energy harvesting capabilities to extend the device's availability. In this thesis, prediction of future incoming harvest energy is combined with hard real-time and reward-based weakly-hard real-time task scheduling schemes to achieve efficient energy usage. Linear regression and artificial neural networks are evaluated individually on their ability to predict future energy harvests. The artificial neural network used contains a single hidden layer and is evaluated with ReLU, Leaky ReLU ,and sine as activation functions. The performance of linear regression and the artificial neural network with varying activation functions and number of hidden nodes are tested and compared. Linear regression is shown to be a sufficient means of predicting future energy harvests. A hard real-time and a reward-based weakly-hard real-time task scheduling scheme are also presented and compared. The experimental results show that the hard real-time scheme can extend the life of the device compared to a non-energy-aware scheduler, but the weakly-hard real-time scheme will allow the device to function indefinitely