Wireless sensor networks (WSNs) and IoT are often deployed for long-term monitoring. However, the network lifetime of these applications is limited by non-rechargeable battery-powered. To vastly reduce energy consumption, this paper proposes a spatio-temporal approximate data collection (STAC) method to prolong the network lifetime. Under the tolerable accuracy, STAC utilizes spatial correlation among neighbors to select partial network for data collection with balanced energy distribution, and takes advantage of temporal redundancy to dynamically adjust the sampling interval by Q-learning based method. With the spatio-temporal approximate and correlation-variation verification mechanism, STAC prolongs the network lifetime with error-bounded data precision. Simulation results demonstrate STAC significantly improves network lifetime in various circumstances.

无线传感器网络（WSN）和IoT通常被部署用于长期监控。但是，这些应用的网络寿命受到不可充电电池供电的限制。为了大大降低能耗，本文提出了一种小号patio-吨emporal一个pproximate数据Ç倾斜（STAC）方法可延长网络寿命。在可容忍的精度范围内，STAC利用邻居之间的空间相关性选择能量平衡的局部网络进行数据收集，并利用时间冗余通过基于Q学习的方法动态调整采样间隔。借助时空近似和相关变量验证机制，STAC可以以误差范围内的数据精度延长网络寿命。仿真结果表明，STAC可在各种情况下显着提高网络寿命。