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Maximum Network Throughput Based on Distributed Algorithm for Rechargeable Wireless Sensor Networks

机译:基于分布式算法的可充电无线传感器网络最大网络吞吐量

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As the most important features, energy can be replenished continually, and its storage capacity is limited for each sensor in rechargeable wireless sensor networks, which causes a node cannot be always beneficial to conserve energy when a network can harvest excessive energy from the environment. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving network throughput. In this work, we propose a distributed algorithm to compute an optimal data generation rate that maximizes the network throughput, which is formulated as a linear programming problem. Considering it is NP-hard, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal data generation rate. The algorithms are illustrated by an example in which an optimum flow is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize network throughput in rechargeable wireless sensor networks.
机译:作为最重要的功能,能量可以连续补充,并且可充电无线传感器网络中每个传感器的能量存储容量都受到限制,这导致当网络可以从环境中收集过多能量时,节点不一定总能节省能量。因此,可以将节点的剩余能量用于增强分组传递效率并提高网络吞吐量。在这项工作中,我们提出了一种分布式算法来计算使网络吞吐量最大化的最佳数据生成速率,该算法被公式化为线性规划问题。考虑到它是NP难的,通过引入拉格朗日乘法器构造了对偶问题,并使用次梯度算法以分布式方式对其进行求解。所得的算法具有较低的计算复杂度,并且可以保证收敛到最佳数据生成速率。通过示例说明算法,其中针对随机分布节点的网络计算最佳流。通过广泛的仿真和实验,我们证明了我们的算法可有效提高可充电无线传感器网络中的网络吞吐量。

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