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arXiv 1503.04964cs.NIcs.LG
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具有有限缓冲区的多传感器节点能量共享

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

Indian Institute of Science(印度科学理工学院)

Sindhu Padakandla, Prabuchandran K. J, Shalabh Bhatnagar

摘要:

我们考虑的问题,找到最佳的能量共享策略,最大限度地提高网络性能的系统,包括多个传感器节点和一个单一的能量收集(EH)源。传感器节点周期性地感知随机场并产生数据,这些数据存储在相应的数据队列中。EH源利用环境能源的能量,产生的能量存储在能量缓冲器中。传感器节点从EH源接收用于数据传输的能量。EH源必须在节点之间有效地共享存储的能量,以最小化数据传输中的长期平均延迟。我们制定的平均成本的框架内的节点之间的能量共享的问题,无限时域马尔可夫决策过程(MDP)。我们开发了高效的能量共享算法,即Q-学习算法的探索机制的基础上的$ε$ -贪婪的方法以及置信上限(UCB)。我们扩展这些算法,将状态和动作空间聚合,以解决状态动作空间爆炸的MDP。我们还开发了一个交叉熵为基础的方法,结合政策参数化,以找到接近最佳的能源共享政策。通过模拟,我们表明,我们的算法产生的能量共享政策,优于启发式贪婪方法。

英文摘要:

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $ε$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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