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Resource Management for Energy and Spectrum Harvesting Sensor NetworksJoint Energy and Spectrum Management in ESHSNs

Resource Management for Energy and Spectrum Harvesting Sensor Networks: Joint Energy and Spectrum... [In this chapter, we develop an aggregate network utility optimization framework for energy and spectrum management in energy and spectrum harvesting sensor networks (ESHSNs). The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading, and does not require a priori statistics of these processes. Based on the framework, we propose an online algorithm to balance energy consumption and energy harvesting, and optimize the spectrum utilization while considering PU protection. Performance analysis shows that the proposed algorithm achieves a close-to-optimal network utility while guaranteeing network stability. Extensive simulations demonstrate the effectiveness of the proposed algorithm and the impact of network parameters on its performance.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Resource Management for Energy and Spectrum Harvesting Sensor NetworksJoint Energy and Spectrum Management in ESHSNs

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2017
ISBN
978-3-319-53770-2
Pages
49 –75
DOI
10.1007/978-3-319-53771-9_4
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we develop an aggregate network utility optimization framework for energy and spectrum management in energy and spectrum harvesting sensor networks (ESHSNs). The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading, and does not require a priori statistics of these processes. Based on the framework, we propose an online algorithm to balance energy consumption and energy harvesting, and optimize the spectrum utilization while considering PU protection. Performance analysis shows that the proposed algorithm achieves a close-to-optimal network utility while guaranteeing network stability. Extensive simulations demonstrate the effectiveness of the proposed algorithm and the impact of network parameters on its performance.]

Published: Mar 9, 2017

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