传感器网络中数据驱动的睡眠调度机制

Data-Driven Sleeping Scheduling Mechanism in Sensor Networks

在能量受限的传感器网络中,尽量延长网络寿命同时保证服务质量(如感知覆盖和数据完整)是关键的研究问题.节点睡眠调度能有效延长网络寿命.研究数据驱动的睡眠调度机制,利用感知数据的时空相关性识别冗余节点.核心思想是用非参数回归方法为节点建立预测模型,求解最大支配数的节点支配集,调度多个支配集轮流工作.睡眠节点的数据可以由支配集节点恢复.分别给出集中式、半分布式和分布式3个睡眠调度方法.据知,这是第1个将统计回归模型用于睡眠调度并扩展到大规模网络的研究.实验结果表明,该方法能够有效地减少活跃节点个数,节省能耗从而延长网络寿命,同时在用户指定误差范围内保证数据的完整性.

In wireless sensor networks that consist of a large number of low-cost, battery-powered sensors, one of the main challenges is to obtain long system lifetime without sacrifying quality of service such as sensing coverage and data integrity. Scheduling sensors to work alternatively can prolong lifetime efficiently. In this paper, a novel data-driven sleeping scheduling mechanism is proposed, which can extend lifetime by identifying redundant nodes based on time-spatial correlations among sensing data. The main idea is： first, a non-parametric regression method is exploited to develop prediction models for forecasting measurements of one sensor using data from other sensors; then the maximal number of node dominating sets is created; finally the sleep/duty cycles of these node dominating sets based on prediction models are scheduled. Data in each of the dominating set is sufficient to recover the measurements of the entire sensor network. We present the centralized, semi-distributed and distributed sleeping scheduling algorithm respectively, guaranteeing that values of sleeping nodes can be recovered from awake nodes within a user＇s specified error bound. It is known that this is the first work on data-driven sleeping scheduling for large scale sensor networks. Experiments results show that the proposed methods can prolong network lifetime substantially while maintaining data integrity under the user＇s error constraint.