TY - JOUR
T1 - Energy efficient resource allocation in EH-Enabled CR networks for IoT
AU - Shahini, Ali
AU - Kiani, Abbas
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received May 18, 2018; revised August 22, 2018 and October 14, 2018; accepted October 29, 2018. Date of publication November 8, 2018; date of current version May 8, 2019. This work was supported by the NSF under Grant CNS-1320468. (Corresponding author: Nirwan Ansari.) The authors are with the Advanced Networking Laboratory, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: nirwan.ansari@njit.edu). Digital Object Identifier 10.1109/JIOT.2018.2880190
Funding Information:
This work was supported by the NSF under Grant CNS-1320468.
Publisher Copyright:
© 2014 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Cognitive radio (CR) can be leveraged to mitigate the spectrum scarcity problem of Internet of Things (IoT) applications while wireless energy harvesting (WEH) can help reduce recharging/replacing batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an access point. More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency of the network. In particular, we formulate a mixed integer nonlinear programming problem that maximizes EE while taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints, and interference constraints. We further prove that the proposed optimization framework is an NP-hard problem. Thus, we propose a low complexity heuristic algorithm, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.
AB - Cognitive radio (CR) can be leveraged to mitigate the spectrum scarcity problem of Internet of Things (IoT) applications while wireless energy harvesting (WEH) can help reduce recharging/replacing batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an access point. More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency of the network. In particular, we formulate a mixed integer nonlinear programming problem that maximizes EE while taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints, and interference constraints. We further prove that the proposed optimization framework is an NP-hard problem. Thus, we propose a low complexity heuristic algorithm, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.
KW - Energy efficiency (EE)
KW - network optimization
KW - resource allocation
KW - wireless energy harvesting (WEH)
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U2 - 10.1109/JIOT.2018.2880190
DO - 10.1109/JIOT.2018.2880190
M3 - Article
AN - SCOPUS:85056337879
SN - 2327-4662
VL - 6
SP - 3186
EP - 3193
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
M1 - 8527537
ER -