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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Achieving Congestion Mitigation Using Distributed Power Control for Spectrum Sensor Nodes in Sensor Network-Aided Cognitive Radio Ad Hoc Networks
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Achieving Congestion Mitigation Using Distributed Power Control for Spectrum Sensor Nodes in Sensor Network-Aided Cognitive Radio Ad Hoc Networks

机译：在传感器网络辅助认知无线电自组织网络中使用频谱传感器的分布式功率控制来实现拥塞缓解

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摘要

The data sequence of spectrum sensing results injected from dedicated spectrum sensor nodes (SSNs) and the data traffic from upstream secondary users (SUs) lead to unpredictable data loads in a sensor network-aided cognitive radio ad hoc network (SN-CRN). As a result, network congestion may occur at a SU acting as fusion center when the offered data load exceeds its available capacity, which degrades network performance. In this paper, we present an effective approach to mitigate congestion of bottlenecked SUs via a proposed distributed power control framework for SSNs over a rectangular grid based SN-CRN, aiming to balance resource load and avoid excessive congestion. To achieve this goal, a distributed power control framework for SSNs from interior tier (IT) and middle tier (MT) is proposed to achieve the tradeoff between channel capacity and energy consumption. In particular, we firstly devise two pricing factors by considering stability of local spectrum sensing and spectrum sensing quality for SSNs. By the aid of pricing factors, the utility function of this power control problem is formulated by jointly taking into account the revenue of power reduction and the cost of energy consumption for IT or MT SSN. By bearing in mind the utility function maximization and linear differential equation constraint of energy consumption, we further formulate the power control problem as a differential game model under a cooperation or noncooperation scenario, and rigorously obtain the optimal solutions to this game model by employing dynamic programming. Then the congestion mitigation for bottlenecked SUs is derived by alleviating the buffer load over their internal buffers. Simulation results are presented to show the effectiveness of the proposed approach under the rectangular grid based SN-CRN scenario.
机译：从专用频谱传感器节点（SSN）注入的频谱感测结果的数据序列和来自上游辅助用户（SU）的数据流量导致传感器网络辅助的认知无线电自组织网络（SN-CRN）中的数据负载无法预测。结果，当提供的数据负载超过其可用容量时，在充当融合中心的SU上可能发生网络拥塞，这会降低网络性能。在本文中，我们提出了一种有效的方法，通过基于矩形网格的SN-CRN上的SSN分布式电源控制框架，来缓解瓶颈SU的拥塞，旨在平衡资源负载并避免过度拥塞。为了实现此目标，提出了一种用于内部层（IT）和中间层（MT）的SSN的分布式功率控制框架，以实现信道容量和能耗之间的权衡。特别是，我们首先考虑本地频谱感知的稳定性和SSN的频谱感知质量来设计两个定价因素。借助定价因素，可以结合考虑IT或MT SSN的功耗降低和能耗成本来制定此电源控制问题的效用函数。考虑到效用函数最大化和能量消耗的线性微分方程约束，我们进一步将功率控制问题表述为合作或不合作情况下的微分博弈模型，并通过动态规划严格地获得该博弈模型的最优解。 。然后，通过减轻其内部缓冲区上的缓冲区负载来获得瓶颈SU的拥塞缓解。仿真结果表明了该方法在基于矩形网格的SN-CRN场景下的有效性。

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