Dynamic Activation Policies for Event Capture in Rechargeable Sensor Network
Dynamic Activation Policies for Event Capture in Rechargeable Sensor Network
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DOI：
10.1109/tpds.2013.2297096
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发表时间：
2014-01
影响因子：
5.3
通讯作者：
Zhu Ren;Peng Cheng;Jiming Chen;David K. Y. Yau;Youxian Sun
中科院分区：
文献类型：
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作者：
Zhu Ren;Peng Cheng;Jiming Chen;David K. Y. Yau;Youxian Sun
我们考虑的问题，事件捕获的可充电传感器网络。我们假设感兴趣的事件遵循更新过程，其事件到达间隔时间是从一般的概率分布中提取的，并且随机充电过程用于为传感器的操作提供能量。动态的事件和充电过程，使最佳的传感器激活问题极具挑战性。在本文中，我们首先考虑单传感器问题。使用动态控制理论，我们考虑一个全信息模型，在该模型中，独立于其激活时间表，传感器将知道事件是否发生在最后一个时隙或没有。在这种情况下，一个简单的和最优的贪婪策略的解决方案。然后，我们进一步考虑一个部分信息模型，传感器知道一个事件的发生，只有当它是活跃的。这个问题属于部分可观测马尔可夫决策过程（POMDP）的类福尔斯。由于POMDP的最优策略具有指数级的计算复杂度，本质上是难以解决的，我们提出了一个有效的启发式聚类策略，并评估其性能。最后，我们的解决方案进行了扩展，以处理多个传感器协作捕获事件的网络设置。我们还提供了大量的模拟结果来评估我们的解决方案的性能。
We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a full-information model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, a simple and optimal greedy policy for the solution is developed. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We also provide extensive simulation results to evaluate the performance of our solutions.