Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments
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Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments Jun-Hong Shen 1,2
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Mu-Yen Chen 3
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Ching-Ta Lu 1,2 & Rou-Hua Wang 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In IoT environments, geo-tagged data have rapidly been emerging as smart things, e.g., mobile devices or connected cars, are generally equipped with the global positioning system (GPS) module. A large volume of geo-tagged data can be fundamental to providing applications of location-based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and reports its up-to-date query result. This paper presents a resident-domain-based approach for continuously monitoring spatial keyword queries. The proposed approach shifts the monitoring of tasks of affected queries from the server to mobile objects which have computational and storage abilities. According to their computational ability, the proposed approach assigns as large as possible resident domains to mobile objects. Within the resident domain, the mobile object informs the server about its spatial information only when crossing the boundary of its monitored queries, thereby reducing the communication cost between it and the server. The experimental evaluation has verified that the proposed approach outperforms the existing approach. Keywords Continuous monitoring . Geo-tagged data . Internet of things . Location-based services . Spatial keyword queries
1 Introduction With the rapid development of the Internet of Things (IoT) technologies, building smart cities has become an important global trend. In the era of IoT, all smart things empowered with data analytics, such as laptops, sensors, smart cars and
* Mu-Yen Chen [email protected] Jun-Hong Shen [email protected] Ching-Ta Lu [email protected] Rou-Hua Wang [email protected] 1
Department of Information Communication, Asia University, Taichung 41354, Taiwan
2
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40447, Taiwan
3
Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan
wearable devices, are connected via wireless/wired networks. This is changing our way of working and lifestyle [1]. For example, connected smart cars have emerged thanks to the development of IoT for smart transportation in smart cities [2, 3]. Smart cities can collect valuable data of where drivers travel with their IoT-connected cars with the aim to provide a better road plan. Integrated apps on the dashboard of connected smart cars such as GasBuddy (https://www.gasbuddy. com/) provide drivers with information about nearby gas stations with the best gas price according to their loca
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