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Link Schedulers for Green Wireless Networks with Energy Sharing by Luyao Wang, Kwan Wu Chin et al

Link Schedulers for Green Wireless Networks with Energy Sharing by Luyao Wang, Kwan Wu Chin et al
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A Novel Distributed Resource Allocation Scheme for Wireless Powered Co by Tengjiao He, Kwan Wu Chin et al

Abstract This paper considers a novel Internet of Things (IoT) network comprising of sensor devices and Power Beacons (PBs); both types of nodes are equipped with a Cognitive Radio (CR). In addition, these sensor devices are powered by Radio Frequency (RF) signals from PBs. Our aim is to maximize the minimum rate of devices acting as sources. We outline the first Mixed Integer Linear Program (MILP) that jointly optimizes the channel assignment of PBs and devices, beamforming vector of PBs, data routing over multiple hops and link activation schedule of devices. We also design a distributed protocol called Distributed Max-min Rate with Cognitive Radio (D-MRCR) for use by devices and PBs. Devices set their operation mode using local information and use a game theory based approach to iteratively adjust their transmit power. On the other hand, each PB employs a Linear Program (LP) to determine its beamforming vector. Our results show that the max-min rate of D-MRCR is within 51.84% tha

Maximizing Sampling Data Upload in Ambient Backscatter Assisted Wirele by Ying Liu, Kwan Wu Chin et al

This paper studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless powered Internet of Things (IoTs) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a Mixed Integer Linear Program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively – both of which exploit AmBC.

On Maximizing Min Source Rate in Power Beacon Assisted IoTs Networks by Tengjiao He, Kwan-Wu Chin et al

Publication Details T. He, K. Chin, S. Soh, C. Yang & J. Wen, On Maximizing Min Source Rate in Power Beacon Assisted IoTs Networks, IEEE Transactions on Vehicular Technology, vol. 69, (10) pp. 11880-11892, 2020. Abstract © 1967-2012 IEEE. Future Internet of Things (IoTs) networks will have Radio Frequency (RF)-energy harvesting devices powered by a Power Beacon (PB). These devices will be tasked with collection and transmission of data over multiple hops to a fusion center. In this paper, our aim is to maximize the minimum data gathering rate of devices acting as sources. We formulate a Mixed Integer Linear Program (MILP) to compute the optimal max-min rate of sources by deciding for each slot the devices to be charged, a PB s transmission power, the amount of data routed over each link, and the active time of transmitting links. We also present a novel distributed protocol called Distributed Charging and Fairness Gathering (D-CFG) that is used by IoT devices to request chargin

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