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"On Virtualizing Targets Coverage in Energy Harvesting IoT Systems" by Longji Zhang, Kwan Wu Chin et al.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and the set of nodes that serve a request. We also propose a heuristic, called energy harvesting aware request placement (EHARP), to determine requests to be deployed in each time slot based on energy harvesting conditions and the resource state of sensor nodes. Furthermore, we propose two model predictive control (MPC) approaches, called MPC-MILP and MPC-EHARP, respectively, which deploy requests based on energy arrival at devices over a given time window as predicted by a Gaussian mixture model (GM ....

Energy Harvesting , Nternet Of Things , Mathematical Optimization , Receding Horizon Control , Soft Sensors , Task Analysis , Task Assignment , Virtual Network Function , Wireless Sensor Networks ,

"Learning Based Channel Access, Data Collection and Computation Methods" by Hang Yu

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 ....

Time Division Multiple Access , Radio Frequency , Hybrid Access Point , Dynamic Framed Slotted Aloha , Sequential Monte Carlo , Reinforcement Learning , Wireless Power Transfer , Receding Horizon Control , Edge Computing , Data Collection ,

"Learning to Charge RF-Energy Harvesting Devices in WiFi Networks" by Yizhou Luo and Kwan Wu Chin

Future WiFi networks will be powered by renewable sources. They will also have radio frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point (AP) will be tasked with supporting both nonenergy harvesting or legacy data users such as laptops, and RF-energy harvesting sensor devices. A key issue is ensuring the AP uses its harvested energy efficiently. To this end, this article contributes two novel solutions that allow the AP to control its transmit power to meet the data rate requirement of legacy users and also to ensure RF-energy devices harvest sufficient energy to transmit their sensed data. Advantageously, these solutions can be deployed in current wireless networks, and they do not require perfect channel gain information to sensor devices or noncausal energy arrivals at an AP. The first solution uses a deep Q-network (DQN) whilst the second solution uses model predictive control (MPC) to manage the AP’s transmit power subject to its available e ....

Receding Horizon Control , Einforcement Learning Rl , Signal To Noise Ratio , Task Analysis , Wireless Fidelity , கேளுங்கள் பகுப்பாய்வு ,

"Data Collection in Radio Frequency (RF) Charging Internet of Things Ne" by Hang Yu and Kwan Wu Chin

Abstract
This paper considers minimizing the time required to collect L bits from each Radio Frequency (RF)-energy harvesting device serviced by a multi-antenna Hybrid Access Point (HAP). We outline a Mixed Integer Non-Linear Program (MINLP) to determine the transmit power allocation of the HAP and devices over multiple time slots. We also outline a Receding Horizon Control (RHC) approach coupled with a Gaussian Mixture Model (GMM) to optimize the transmit power allocation of the HAP. Our results show that the performance of our approach is within 5% of the optimal solution.
Open Access Status ....

Linear Program , Radio Frequency , Hybrid Access Point , Mixed Integer Non Linear Program , Receding Horizon Control , Gaussian Mixture Model , F Charging , Wireless Power Transfer , நேரியல் ப்ரோக்ர்யாம் , வானொலி அதிர்வெண் , கலப்பு நுழைவு பாயஂட் , கலப்பு முழு அல்லாத நேரியல் ப்ரோக்ர்யாம் ,