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Joint Channel Access and Sampling Rate Control in Energy Harvesting Cognitive Radio Sensor Networks

机译:能量收集认知无线电传感器网络中的联合通道访问和采样率控制

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In this paper, we investigate the network utility maximization problem in energy harvesting cognitive radio sensor networks (CRSNs). Different from traditional sensor networks, sensor nodes in CRSNs are embedded cognitive radio modules, enabling them to dynamically access the licensed channels. Since the dynamic channel access is critical to guarantee the network capacity for CRSNs, existing solutions without considering the dynamic channel access cannot be directly applied into CRSNs. To this end, we aim at maximizing the network utility by jointly controlling the sampling rates and channel access of sensor nodes, under the energy consumption, channel capacity and interference constraints. With the consideration of fluctuated energy harvesting rates and channel switching costs, we formulate the network utility maximization as a mix-integer non-linear programming problem and solve it in an efficient and decoupled way by means of dual decomposition. A joint channel access and sampling rate control scheme, named JASC, is then proposed considering the real-time channel sensing results and energy harvesting rates. Extensive simulation results demonstrate that JASC can efficiently improve the network utility in CRSNs based on a realistic energy harvesting dataset.
机译:在本文中,我们研究了能量收集认知无线电传感器网络(CRSN)中的网络效用最大化问题。与传统的传感器网络不同,CRSN中的传感器节点是嵌入式认知无线电模块,使它们能够动态访问许可的信道。由于动态信道访问对于保证CRSN的网络容量至关重要,因此不考虑动态信道访问的现有解决方案不能直接应用于CRSN。为此,我们旨在通过在能耗,信道容量和干扰约束下共同控制传感器节点的采样率和信道访问来最大化网络实用性。考虑到能量采集速率和通道切换成本的波动,我们将网络效用最大化公式化为混合整数非线性规划问题,并通过对偶分解以有效且解耦的方式解决该问题。然后考虑实时信道感知结果和能量收集率,提出了一种联合信道访问和采样率控制方案,称为JASC。大量的仿真结果表明,JASC可以基于现实的能量收集数据集有效地改善CRSN中的网络实用性。

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