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Joint Trajectory and Link Scheduling Optimization in UAV Networks by Yawen Zheng and Kwan Wu Chin
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Link Schedulers for Green Wireless Networks with Energy Sharing by Luyao Wang, Kwan Wu Chin et al
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Green Multi-Stage Upgrade for Bundled-Link SDNs with Budget and Delay by Lely Hiryanto, Sieteng Soh et al
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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
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.