Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Liu:2019:10.1109/JIOT.2019.2895417,
author = {Liu, X and Qin, Z and Gao, Y and McCann, JA},
doi = {10.1109/JIOT.2019.2895417},
journal = {IEEE Internet of Things Journal},
pages = {4935--4945},
title = {Resource allocation in wireless powered IoT networks},
url = {http://dx.doi.org/10.1109/JIOT.2019.2895417},
volume = {6},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - In this paper, the efficient resource allocation for the uplink transmission of wireless powered Internet of Things (IoT) networks is investigated. We adopt LoRa technology as an example in the IoT network, but this paper is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov decision process is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.
AU - Liu,X
AU - Qin,Z
AU - Gao,Y
AU - McCann,JA
DO - 10.1109/JIOT.2019.2895417
EP - 4945
PY - 2019///
SN - 2327-4662
SP - 4935
TI - Resource allocation in wireless powered IoT networks
T2 - IEEE Internet of Things Journal
UR - http://dx.doi.org/10.1109/JIOT.2019.2895417
UR - http://hdl.handle.net/10044/1/72071
VL - 6
ER -