Uncertainty-Aware Energy Management of Internet of Things (IoT) Devices: Novel Models and Algorithms

Nuzhat Yamin

doi:10.7273/000007109

Low-power Internet of Things (IoT) devices have the potential to transform multiple fields, including healthcare, environmental monitoring, and digital agriculture. However, the operating life of these devices is severely constrained by their small batteries that require frequent recharging. Harvesting energy from ambient sources has emerged as an effective approach to prolong the lifetime of these devices. Consequently, energy harvesting necessitates the development of energy management approaches to manage the harvested energy effectively. The harvested energy must be carefully managed to ensure sufficient energy is available when ambient energy is scarce. Prediction of the energy available in the future can aid energy management algorithms in making better decisions about the allocation of the available energy. Once the harvested energy is predicted, the predictions must be used in an energy management algorithm to maximize the operating lifetime and application quality of service. However, this is a challenging problem due to two key reasons: 1) In a multi-sensor and harvester scenario, energy harvesting approaches typically assume that the placement of the energy harvesting device and sensors required for health monitoring are the same. However, this assumption does not hold for several real-world applications. For example, motion energy harvesting using piezoelectric sensors is limited to the knees and elbows. In contrast, a sensor for heart rate monitoring must be placed on the chest for optimal perfor- mance. 2) Using dynamic optimization methods to obtain the energy consumption in each decision interval is not suitable due to the high overhead of continuously executing dynamic optimization on low-power IoT devices. This dissertation aims to address the above challenges by making the following contributions: 1) We provide a low-overhead algorithm to accurately provide future energy in outdoor solar energy harvesting as well as on-body en- ergy harvesting using light, and motion. 2) Ambient energy sources are highly stochastic in nature. Therefore, we describe a machine-learning-based approach to accurately predict the energy and uncertainty in energy for multiple future intervals. The approach performs an iterative algorithm to obtain near-optimal energy management decision using the predicted energy and uncertainty. 3) We present an efficient algorithm to dynamically transfer and manage the energy in a multi-sensor, multi-harvester scenario. 4) We present an imitation learning methodology to directly provide energy management decisions in IoT devices while eliminating the need to perform dynamic optimization, thus lowering the complexity of the energy management. 5) Finally, we present a two-step uncertainty-aware EH prediction and management framework with conformal prediction and rollout approach to construct energy harvest prediction intervals and near-optimal energy allocation bounds in the future. We perform experiments on real-world energy and activity datasets to demonstrate the performance of our energy prediction and management algorithms.

Uncertainty-Aware Energy Management of Internet of Things (IoT) Devices: Novel Models and Algorithms

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