As the number of Internet of Things (IoT) devices continuously grows and application scenarios constantly enrich, the volume of sensor data experiences an explosive increase. However, substantial data demands considerable energy during computation and transmission. Redundant deployment or mobile assistance is essential to cover the target area reliably with fault-prone sensors. Consequently, the ``butterfly effect" may appear during the IoT operation, since unreasonable data overlap could result in many duplicate data. To this end, we propose Senses, a novel online energy saving solution for edge IoT networks, with the insight of sensing and storing less at the network edge by adopting Muti-Agent Reinforcement Learning (MARL). Senses achieves data de-duplication by dynamically adjusting sensor coverage at the sensor level. For exceptional cases where sensor coverage cannot be altered, Senses conducts data partitioning and eliminates redundant data at the controller level. Furthermore, at the global level, considering the heterogeneity of IoT devices, Senses balances the operational duration among the devices to prolong the overall operational duration of edge IoT networks. We evaluate the performance of Senses through testbed experiments and simulations. The results show that Senses saves 11.37% of energy consumption on control devices and prolongs 20% overall operational duration of the IoT device network.

翻译：随着物联网设备数量的持续增长和应用场景的不断丰富，传感器数据量呈现爆炸式增长。然而，海量数据在计算和传输过程中需要消耗大量能量。为了使用易出故障的传感器可靠地覆盖目标区域，冗余部署或移动辅助是必不可少的。因此，在物联网运行过程中可能出现“蝴蝶效应”，因为不合理的数据重叠可能导致大量重复数据。为此，我们提出Senses——一种面向边缘物联网网络的新型在线节能解决方案，其核心思想是通过采用多智能体强化学习在网络边缘实现更少的感知与存储。Senses通过在传感器层面动态调整传感器覆盖范围来实现数据去重。对于传感器覆盖范围无法改变的例外情况，Senses在控制器层面执行数据分区并消除冗余数据。此外，在全局层面，考虑到物联网设备的异构性，Senses通过平衡设备间的运行时长来延长边缘物联网网络的整体运行时间。我们通过测试床实验和仿真评估了Senses的性能。结果表明，Senses在控制设备上节省了11.37%的能耗，并将物联网设备网络的整体运行时间延长了20%。