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Efficient Resource Allocation Algorithm for Underwater Wireless Sensor Networks Based on Improved Stochastic Gradient Descent Method

机译:基于改进的随机梯度下降方法的水下无线传感器网络有效资源分配算法

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Due to the poor wireless communication performance of underwater wireless sensor networks (UWSNs) nodes in underwater environment, acoustic transmission is usually used for communication. Resource allocation efficiency and lifetime of nodes are very important in UWSNs. In this paper, multi homing technology is used in UWSNs. Considering the actual network rate constraint, power constraint and energy return constraint, the multi homing technology is adopted for UWSNs. Then, an optimization model of network resource allocation is established to maximize the throughput of communication system under the mechanism of energy borrowing and energy recovery. Based on the advantages of gradient descent method, such as simplicity, fast convergence speed and reliable effect, an improved stochastic gradient descent algorithm is proposed. In each iteration process of the algorithm, it is not necessary to traverse all the data, only a random sample is selected to calculate the gradient, and the weight vector is updated iteratively. The momentum factor ensures the optimal step size and greatly reduces the computational complexity of the algorithm. The convergence and numerical simulation results show that the algorithm can effectively reduce node energy consumption and improve the throughput of UWSNs.
机译:由于水下环境中的水下无线传感器网络(UWSNS)节点的无线通信性能差,声传输通常用于通信。节点的资源分配效率和生命周期在UWSN中非常重要。在本文中,UWSNS中使用多归巢技术。考虑到实际的网络速率约束,功率约束和能量返回约束,UWSN采用多归归机技术。然后,建立了网络资源分配的优化模型,以最大限度地提高能量借用和能量恢复机制下通信系统的吞吐量。基于梯度下降方法的优点,如简单,快速收敛速度和可靠效果,提出了一种改进的随机梯度下降算法。在算法的每个迭代过程中,不需要遍历所有数据,仅选择随机样本来计算梯度,并且迭代地更新权重向量。动量因数确保了最佳步长,大大降低了算法的计算复杂性。收敛和数值模拟结果表明,该算法可以有效地降低节点能量消耗并提高UWSN的吞吐量。

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