With the rapid development of the Internet of Things (IoT), billions of smart devices should be available for sensing environment variables and reporting events periodically that may happen in certain regions, for supporting industrial applications. It is usual that contiguous queries on point-of-interests may have some region overlapping. In this setting, sensory data retrieved by recent queries may be beneficial for answering the queries forthcoming, when these data are fresh enough. To address this challenge, we propose a popularity-based caching strategy for optimizing periodic query processing. Specifically, the network region is divided using a cell-based manner, where each grid cell is abstracted as an elementary unit for the caching purpose. Fresh sensory data are cached in the memory of the sink node. The popularity of grid cells are calculated leveraging the queries conducted in recent time slots, which reflects the possibility that grid cells may be covered by the queries forthcoming. Prefetching may be performed for grid cells with a higher degree of popularity when missed in the cache. These cached sensory data are used for facilitating the query answering afterwards. The simulation results show that our approach can reduce the communication cost significantly and increase the network capability