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首页> 外文期刊>Networking, IEEE/ACM Transactions on >A Simple Asymptotically Optimal Joint Energy Allocation and Routing Scheme in Rechargeable Sensor Networks
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A Simple Asymptotically Optimal Joint Energy Allocation and Routing Scheme in Rechargeable Sensor Networks

机译:可充电传感器网络中一种简单的渐近最优联合能量分配和路由方案

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In this paper, we investigate the utility maximization problem for a sensor network with energy replenishment. Each sensor node consumes energy in its battery to generate and deliver data to its destination via multihop communications. Although the battery can be replenished from renewable energy sources, the energy allocation should be carefully designed in order to maximize system performance, especially when the replenishment profile is unknown in advance. In this paper, we address the joint problem of energy allocation and routing to maximize the total system utility, without prior knowledge of the replenishment profile. We first characterize optimal throughput of a single node under general replenishment profile and extend our idea to the multihop network case. After characterizing the optimal network utility with an upper bound, we develop a low-complexity online solution that achieves asymptotic optimality. Focusing on long-term system performance, we can greatly simplify computational complexity while maintaining high performance. We also show that our solution can be approximated by a distributed algorithm using standard optimization techniques. In addition, we show that the required battery size is $O(ln(1/xi))$ to constrain the performance of our scheme within $xi$-neighborhood of the optimum. Through simulations with replenishment profile traces for solar and wind energy, we numerically evaluate our solution, which outperforms a state-of-the-art scheme that is developed based on the Lyapunov optimization technique.
机译:在本文中,我们研究了带有能量补充的传感器网络的效用最大化问题。每个传感器节点都消耗其电池中的能量,以通过多跳通信生成数据并将数据传输到目的地。尽管可以从可再生能源中补充电池,但是应该仔细设计能量分配,以最大程度地提高系统性能,尤其是在事先未知补充特性时。在本文中,我们解决了能量分配和路由的联合问题,以最大化整个系统的效用,而无需事先了解补给配置文件。我们首先在一般补货配置文件下表征单个节点的最佳吞吐量,然后将我们的思想扩展到多跳网络情况。在确定最佳网络效用的上限后,我们开发了一种实现渐近最优的低复杂度在线解决方案。着眼于长期系统性能,我们可以在保持高性能的同时极大地简化计算复杂性。我们还表明,可以使用标准优化技术通过分布式算法来近似我们的解决方案。此外,我们显示所需的电池大小为 $ O(ln(1 / xi))$ 来限制性能最优方案的 $ xi $ 邻域内的方案的结果。通过对太阳能和风能的补给曲线进行仿真,我们对解决方案进行了数值评估,其性能优于基于Lyapunov优化技术开发的最新方案。

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