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Internet of Things (IoT) networks consist of sensing devices and gateways. Specifically, these devices monitor an environment to obtain measurements of a physical quantity such as temperature or the location of a target. A gateway or server is then required to collect and compute sensory data from devices. A critical issue in an IoT network is that devices have limited operation time due to the lack of energy. This affects the amount of data collected by devices and data processed by a gateway. To this end, prior works have considered powering devices using energy sources such as solar or wirelessly via Radio Frequency (RF) signals. Another issue is ensuring sensed data is processed quickly to infer any events. To address this issue, many works have considered installing computational resources at the edge of an IoT network.
Given the above issues, this thesis first considers a Hybrid Access Point (HAP) that charges one or more energy harvesting devices via RF signals. These devices th
Data collection is a fundamental operation in energy harvesting industrial Internet of Things networks. To this end, we consider a hybrid access point (HAP) or controller that is responsible for charging and collecting L bits from sensor devices. The problem at hand is to optimize the transmit power allocation of the HAP over multiple time frames. The main challenge is that the HAP has causal channel state information to devices. In this article, we outline a novel two-step reinforcement learning with Gibbs sampling (TSRL-Gibbs) strategy, where the first step uses Q-learning and an action space comprising transmit power allocation sampled from a multidimensional simplex. The second step applies Gibbs sampling to further refine the action space. Our results show that TSRL-Gibbs requires up to 28.5% fewer frames than competing approaches.