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Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks
International Journal of Machine Learning and Cybernetics ( IF 3.1 ) Pub Date : 2020-06-17 , DOI: 10.1007/s13042-020-01154-y
Thompson Stephan , Fadi Al-Turjman , Suresh Joseph K , Balamurugan Balusamy

The problem of energy efficiency in cognitive radio sensor networks (CRSN) is mainly caused by the limited energy of sensor nodes and other channel-related operations for data transmission. The unequal clustering method should be considered for balancing the energy consumption among the cluster heads (CHs) for prolonging the network lifetime. The CH selection should consider the number of accessible free channels for efficient channel assignment. To improve fairness, the channel assignment problem should consider energy consumption among the cluster members. Furthermore, the relay metric for the selection of the best next-hop should consider the stability of the link for improving the transmission time. The CH rotation for cluster maintenance should be energy and spectrum aware. With regard to the above objectives, this paper proposes an energy and spectrum aware unequal clustering (ESAUC) protocol that jointly overcomes the limitations of energy and spectrum for maximizing the lifetime of CRSN. Our proposed ESAUC protocol improves fairness by achieving residual energy balance among the sensor nodes and enhances the network lifetime by reducing the overall energy consumption. Deep Belief Networks algorithm is exploited to predict the spectrum holes. ESAUC improves the stability of the cluster by optimally adjusting the number of common channels. ESAUC uses a CogAODV based routing mechanism to perform inter-cluster forwarding. Simulation results show that the proposed scheme outperforms the existing CRSN clustering algorithms in terms of residual energy, Network Lifetime, secondary user–primary user Interference Ratio, Route Discovery Frequency, throughput, Packet Delivery Ratio, and end-to-end delay.



中文翻译:

认知无线电传感器网络中基于能量和频谱的不平等聚类与基于深度学习的主要用户分类

认知无线电传感器网络(CRSN)中的能量效率问题主要是由于传感器节点的能量有限以及其他与数据传输相关的与通道有关的操作所致。应该考虑采用不均等的群集方法来平衡群集头(CH)之间的能耗,从而延长网络寿命。CH选择应考虑可访问的空闲信道的数量,以进行有效的信道分配。为了提高公平性,信道分配问题应考虑集群成员之间的能耗。此外,用于选择最佳下一跳的中继度量应考虑链路的稳定性,以改善传输时间。机群维护的CH旋转应具有能量和频谱意识。关于上述目标,本文提出了一种能量和频谱感知不平等聚类(ESAUC)协议,该协议共同克服了能量和频谱的局限性,以最大化CRSN的寿命。我们提出的ESAUC协议通过实现传感器节点之间的剩余能量平衡来提高公平性,并通过减少总能耗来延长网络寿命。利用深信度网络算法来预测频谱漏洞。ESAUC通过最佳地调整公共通道数来提高群集的稳定性。ESAUC使用基于CogAODV的路由机制来执行集群间转发。仿真结果表明,该方案在剩余能量,网络生存时间,次要用户-主要用户干扰比,路由发现频率,吞吐量,

更新日期:2020-06-17
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