Skip to main content
SpringerLink
Log in

Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sangaiah AK, Hosseinabadi AAR, Shareh MB, Bozorgi Rad SY, Zolfagharian A, Chilamkurti N (2020) IoT resource allocation and optimization based on heuristic algorithm. Sensors 20:539. https://doi.org/10.3390/s20020539

    Article  Google Scholar 

  2. Saleem MA, Shijie Z, Sharif A (2019) Data transmission using IoT in vehicular ad-hoc networks in smart city congestion. Mob Networks Appl 24:248–258. https://doi.org/10.1007/s11036-018-1205-x

    Article  Google Scholar 

  3. Ristvej J, Lacinák M, Ondrejka R (2020) On smart city and safe city concepts. Mob Networks Appl 25:1–10. https://doi.org/10.1007/s11036-020-01524-4

    Article  Google Scholar 

  4. Aly AM, Mahmood AR, Hassan MS, Aref WG, Ouzzani M, Elmeleegy H, Qadah T (2015) AQWA: adaptive query workload aware partitioning of big spatial data. Proc VLDB endow 8:2062–2073. https://doi.org/10.14778/2831360.2831361

    Article  Google Scholar 

  5. Chen G, Zhao J, Gao Y, Chen L, Chen R (2017) Time-aware boolean spatial keyword queries. IEEE Trans Knowl Data Eng 29:2601–2614. https://doi.org/10.1109/TKDE.2017.2742956

    Article  Google Scholar 

  6. Cong G, Jensen CS (2016). Querying geo-textual data: spatial keyword queries and beyond. In: Proceedings of the 2016 international conference on Management of Data - SIGMOD ‘16. ACM Press, New York, pp. 2207–2212

  7. Deng Z, Wang M, Wang L, Huang X, Han W, Chu J, Zomaya A (2019) An efficient indexing approach for continuous spatial approximate keyword queries over geo-textual streaming data. ISPRS Int J Geo-Information 8:57. https://doi.org/10.3390/ijgi8020057

    Article  Google Scholar 

  8. Mahmood A, Aref WG (2017). Query processing techniques for big spatial-keyword data. In: Proceedings of the 2017 ACM international conference on Management of Data. ACM Press, New York, pp. 1777–1782

  9. Sangaiah AK, Medhane DV, Han T, Hossain MS, Muhammad G (2019) Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in real-time industrial informatics. IEEE Trans Ind Informatics 15:4189–4196. https://doi.org/10.1109/TII.2019.2898174

    Article  Google Scholar 

  10. Jo B, Ahn J, Jung S (2019) Efficient spatial keyword search methods for reflecting multiple keyword domains. J Inf Sci Eng 35:903–921. https://doi.org/10.6688/JISE.201907_35(4).0012

    Article  Google Scholar 

  11. Jiang J, Lu H, Yang B, Cui B (2015). Finding top-k local users in geo-tagged social media data. In: Proceedings of IEEE 31st International Conference on Data Engineering. IEEE, pp 267–278

  12. Lee T, Park J, Lee S, Hwang S-W, Elnikety S, He Y (2015) Processing and optimizing main memory spatial-keyword queries. Proc VLDB endow 9:132–143. https://doi.org/10.14778/2850583.2850588

    Article  Google Scholar 

  13. Zhang C, Zhang Y, Zhang W, Lin X (2016) Inverted linear quadtree: efficient top k spatial keyword search. IEEE Trans Knowl Data Eng 28:1706–1721. https://doi.org/10.1109/TKDE.2016.2530060

    Article  Google Scholar 

  14. Chen L, Cong G, Jensen CS, Wu D (2013) Spatial keyword query processing: an experimental evaluation. Proc VLDB endow 6:217–228. https://doi.org/10.14778/2535569.2448955

    Article  Google Scholar 

  15. Salgado C, Cheema MA, Ali ME (2018) Continuous monitoring of range spatial keyword query over moving objects. World Wide Web 21:687–712. https://doi.org/10.1007/s11280-017-0488-3

    Article  Google Scholar 

  16. Dong Y, Chen H, Kitagawa H (2019). Continuous search on dynamic spatial keyword objects. In: 2019 IEEE 35th international conference on data engineering. IEEE, pp 1578–1581

  17. Jung H, Kim YS, Chung YD (2014) QR-tree: an efficient and scalable method for evaluation of continuous range queries. Inf Sci (Ny) 274:156–176. https://doi.org/10.1016/j.ins.2014.02.061

    Article  MathSciNet  Google Scholar 

  18. Jung H, Song M, Youn H, Kim U (2015) Evaluation of content-matched range monitoring queries over moving objects in mobile computing environments. Sensors 15:24143–24177. https://doi.org/10.3390/s150924143

    Article  Google Scholar 

  19. Phan T-K, Jung H, Youn HY, Kim U-M (2017) QR*-tree: an adaptive space-partitioning index for monitoring moving objects. J Inf Sci Eng 33:385–411. https://doi.org/10.6688/JISE.2017.33.2.7

    Article  MathSciNet  Google Scholar 

  20. Shen J-H, Lu C-T, Chen M-Y, Yen NY (2020) Grid-based indexing with expansion of resident domains for monitoring moving objects. J Supercomput 76:1482–1501. https://doi.org/10.1007/s11227-017-2224-2

    Article  Google Scholar 

  21. Prabhakar S, Xia Y, Kalashnikov DV, Aref WG, Hambrusch SE (2002) Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects. IEEE Trans Comput 51:1124–1140. https://doi.org/10.1109/TC.2002.1039840

    Article  MathSciNet  MATH  Google Scholar 

  22. Hu H, Xu J, Lee DL (2005). A generic framework for monitoring continuous spatial queries over moving objects. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of Data. ACM Press, New York, pp. 479–490

  23. Qi J, Zhang R, Jensen CS, Ramamohanarao K, He J (2018) Continuous spatial query processing: a survey of safe region based techniques. ACM Comput Surv 51:1–39. https://doi.org/10.1145/3193835

    Article  Google Scholar 

  24. Cheema MA, Lin X, Zhang W, Zhang Y (2013). A safe zone based approach for monitoring moving skyline queries. ACM Int Conf proceeding Ser 275–286. https://doi.org/10.1145/2452376.2452409

  25. Allheeib N, Taniar D, Al-Khalidi H, Islam MS, Adhinugraha KM (2020) Safe regions for moving reverse neighbourhood queries in a peer-to-peer environment. IEEE Access 8:50285–50298. https://doi.org/10.1109/ACCESS.2020.2979432

    Article  Google Scholar 

  26. Cai Y, Hua KA, Cao G, Xu T (2006) Real-time processing of range-monitoring queries in heterogeneous mobile databases. IEEE Trans Mob Comput 5:931–942. https://doi.org/10.1109/TMC.2006.105

    Article  Google Scholar 

  27. Almaslukh A, Magdy A (2018). Evaluating spatial-keyword queries on streaming data. In: Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM Press, New York, pp. 209–218

  28. Johnson DB, Maltz DA (2007). Dynamic source routing in ad hoc wireless networks. In: Mobile computing. Springer US, Boston, MA, pp. 153–181

Download references

Acknowledgements

We thank the reviewers for their valuable comments and suggestions, greatly improving the quality of this paper. Our gratitude also goes to Alison M. Fisher, professional English editor, for her assistance with proofreading. This research was supported in part by grant MOST 109-2410-H-025-015-MY2 from the Ministry of Science and Technology, Taiwan.

Author information

Authors and Affiliations

  1. Department of Information Communication, Asia University, Taichung, 41354, Taiwan

    Jun-Hong Shen, Ching-Ta Lu & Rou-Hua Wang

  2. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 40447, Taiwan

    Jun-Hong Shen & Ching-Ta Lu

  3. Department of Engineering Science, National Cheng Kung University, Tainan, 701, Taiwan

    Mu-Yen Chen

Authors
  1. Jun-Hong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mu-Yen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ching-Ta Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rou-Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mu-Yen Chen.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, JH., Chen, MY., Lu, CT. et al. Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments. Mobile Netw Appl 27, 208–218 (2022). https://doi.org/10.1007/s11036-020-01642-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01642-z

Keywords

Search

Navigation