Utility-Optimal Resource Management and Allocation Algorithm for Energy Harvesting Cognitive Radio Sensor Networks

In this paper, we study resource management and allocation for energy harvesting cognitive radio sensor networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate the self-sustainability of the power-limited sensors. Furthermore, cog...

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Veröffentlicht in:IEEE journal on selected areas in communications Jg. 34; H. 12; S. 3552 - 3565
Hauptverfasser: Zhang, Deyu, Chen, Zhigang, Awad, Mohamad Khattar, Zhang, Ning, Zhou, Haibo, Shen, Xuemin Sherman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithms
Batteries
channel allocation
Cognitive radio
Collision rates
Design optimization
Energy consumption
Energy efficiency
Energy harvesting
Energy management
Energy sources
Lyapunov optimization
Networks
Optimization
Queues
Resource allocation
Resource management
Sensors
Stochastic processes
Sustainable development
Wireless sensor network
ISSN:0733-8716, 1558-0008
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Zusammenfassung:In this paper, we study resource management and allocation for energy harvesting cognitive radio sensor networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate the self-sustainability of the power-limited sensors. Furthermore, cognitive radio enables access to the underutilized licensed spectrum to mitigate the spectrum-scarcity problem in the unlicensed band. We develop an aggregate network utility optimization framework for the design of an online energy management, spectrum management, and resource allocation algorithm based on Lyapunov optimization. The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading. However, a priori knowledge of any of these processes statistics is not required. Based on the framework, we propose an online algorithm to achieve two major goals: first, balancing sensors' energy consumption and energy harvesting while stabilizing their data and energy queues; second, optimizing the utilization of the licensed spectrum while maintaining a tolerable collision rate between the licensed subscriber and unlicensed sensors. The performance analysis shows that the proposed algorithm achieves a close-to-optimal aggregate network utility while guaranteeing bounded data and energy queue occupancy. The extensive simulations are conducted to verify the effectiveness of the proposed algorithm and the impact of various network parameters on its performance.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2016.2611960