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Due to the rapid advances of Information and Communication Technologies (ICT), especially 5G and Artificial Intelligence (AI), the Internet of Everything is gradually becoming a reality, and human beings living environments are becoming smarter and smarter. Every day there will be generated large amounts of data in Humans-Machines-Things hybrid space, which is also called Cyber-Physical-Social Systems (CPSSs). Today, the city we live in has become a data-driven society. However, how to effectively mine valuable information from these massive data to provide proactive and personalized services for human beings is a challenging problem. Thus, top-k search remains an important topic of ongoing research. In this paper, we focus on a basic problem of geo-tagged data: find the top-k frequent terms among the geo-tagged data in a specific region from the cloud. We first construct a Region Tree Index (RTI) for geo-tagged data. Then the list storage structure is proposed to Store Sorted Terms and Weights (SSTW) in RTI. And then an efficient kTermsSearch algorithm is presented to compute top-k frequent terms in a given region. Finally, extensive experiments verify the validity of the proposed scheme.