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Efficient Spatial Keyword Query Processing in the Internet of Industrial Vehicles

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Abstract

With the development of the Internet of Things (IoT), the industrial vehicle ad hoc networks are revolving into the Internet of Industrial Vehicles (IoIV). Due to the popularity of the geographical devices used on the Industrial vehicle, location-based information is extensively available in IoIV. This development calls for spatial keyword queries (SKQ), which takes into account both the locations and textual descriptions of objects. This paper addresses the issue of processing SKQ in IoIV environment, which focuses on two types of SKQ queries, namely Boolean kNN Queries and Top-k Queries. A general air index called Extended Spatial Keyword query index in IoIV environment (ESKIV) is proposed, which supports both network space pruning and textual pruning simultaneously. Based on ESKIV, efficient algorithms are designed to deal with these two types of SKQ respectively. The proposed ESKIV also can be used to deal with other kinds of queries, such as range SKQ. Finally, extensive simulations are conducted to demonstrate the efficiency of our ESKIV index and the corresponding query processing algorithms.

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References

  1. Aggarwal CC, Ashish N, Sheth A (2013) The internet of things: a survey from the data-centric perspective. Managing and Mining Sensor Data

  2. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  3. Mineraud J, Mazhelis O, Su X, Tarkoma S (2016) A gap analysis of internet-of-things platforms. Comput Commun 89-90:5–16

    Article  Google Scholar 

  4. Zheng Y, Peng Z, Vasilakos AV (2014) A survey on trust management for internet of things. J Netw Comput Appl 42(3):120–134

    Google Scholar 

  5. Shu L, Mukherjee M, Xu X, Wang K (2016) A survey on gas leakage source detection and boundary tracking with wireless sensor networks. IEEE Access 4:1700–1715

    Article  Google Scholar 

  6. Hromic H, Phuoc DL, Serrano M, Antonic A (2015) Real time analysis of sensor data for the internet of things by means of clustering and event processing. In: IEEE international conference on communications, p 1421–1437

  7. Yan Z, Liu J, Vasilakos AV, Yang LT (2015) Trustworthy data fusion and mining in internet of things. Futur Gener Comput Syst 49(4):45–46

    Article  Google Scholar 

  8. Shu L, Wang L, Niu J, Zhu C (2015) Releasing network isolation problem in group-based industrial wireless sensor networks. IEEE Syst J 1–11

  9. Liu Y, Zhang Y, Yu R, Xie S (2015) Integrated energy and spectrum harvesting for 5g wireless communications. IEEE Netw 29(3):75–81

    Article  Google Scholar 

  10. Liu J, Yan Z, Yang LT (2015) Fusion c an aide to data mining in internet of things. Inf Fus 23:1–2

    Article  Google Scholar 

  11. Botta A, Donato Wd, Persico V, Pescap A (2016) Integration of cloud computing and?internet?of?things: a survey. Futur Gener Comput Syst 56:684–700

    Article  Google Scholar 

  12. Dan K, Piratla K, Matthews CJ (2015) Towards sustainable water supply: schematic development of big data collection using internet of things (iot). Procedia Eng 118:489–497

    Article  Google Scholar 

  13. Balazs JA, Velsquez JD (2016) Opinion mining and information fusion a survey. Inf Fus 27(C):95–110

    Article  Google Scholar 

  14. Yang F,Wang S, Li J, Liu Z, Sun Q (2014) An overview of internet of vehicles. Chin Commun 10:1–15

  15. Kumar N, Rodrigues JJPC, Chilamkurti N (2014) Bayesian coalition game as-a-service for content distribution in internet of vehicles. Int Things J IEEE 1(6):544–555

    Article  Google Scholar 

  16. Jin M, Zhou X, Luo E, Qing X (2015) Industrial-qos-oriented remote wireless communication protocol for the internet of construction vehicles. Ind Electron IEEE Trans 62(11):7103–7113

    Article  Google Scholar 

  17. Kumar N, Misra S, Rodrigues JJPC, Obaidat MS (2015) Coalition games for spatio-temporal big data in internet of vehicles environment: A comparative analysis 2(4):1–1

    Google Scholar 

  18. Alam KM, Saini M, Saddik AE (2015) Toward social internet of vehicles: concept, architecture, and applications. Access IEEE 3:343–357

    Article  Google Scholar 

  19. Yu R, Kang J, Huang X, Xie S (2016) Mixgroup: accumulative pseudonym exchanging for location privacy enhancement in vehicular social networks. Depend Sec Comput IEEE Trans 13(1):93–105

    Article  Google Scholar 

  20. Chen YY, Suel T, Markowetz A (2006) Efficient query processing in geographic web search engines. In: ACM SIGMOD international conference on management of data. Chicago, pp 277–288

  21. Christoforaki M, He J, Dimopoulos C, Markowetz A, Suel T (2011) Text vs. space: efficient geo-search query processing. In: ACM conference on information and knowledge management, CIKM 2011. Glasgow, pp 423–432

  22. Zhou Y, Xie X, Wang C, Gong Y, Ma W-Y (2005) Hybrid index structures for location-based web search. In: Proceedings of the 14th ACM international conference on information and knowledge management, pp 155–162

  23. Cong G, Jensen CS, Wu D (2009) Efficient retrieval of the top-k most relevant spatial web objects. Proc VLDB Endow 2(1):337–348

    Article  Google Scholar 

  24. Gao Y, Zheng B, Chen G (2014) Efficient reverse top-k boolean spatial keyword queries on road networks. IEEE Trans Knowl Data Eng PP(99):1–14

    Google Scholar 

  25. Huang W, Li G, Tan K-L, Feng J (2012) Efficient safe-region construction for moving top-k spatial keyword queries. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 932–941

  26. Li G, Feng J, Xu J (2012) Desks: direction-aware spatial keyword search. In: Proceedings of the 28th international conference on data engineering, pp 474–485

  27. Li Y, Li J, Shu L, Li Q, Li G, Yang F (2014) Searching continuous nearest neighbors in road networks on the air. Inf Syst 42(2014):177–194

    Article  Google Scholar 

  28. Zhang D, Tan KL, Tung AKH (2013) Scalable top-k spatial keyword search. In: Proceedings of the 16th international conference on extending database technology

  29. Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: Proceedings of the 15th international conference on extending database technology, pp 168–179

  30. Sun W, Chen C, Zheng B, Chen C, Liu P (2015) An air index for spatial query processing in road networks. IEEE Trans Knowl Data Eng 27(2):382–395

    Article  Google Scholar 

  31. Guo L, Shao J, Aung HH, Tan KL (2014) Efficient continuous top-k spatial keyword queries on road networks. Geoinformatica 19(1):29–60

    Article  Google Scholar 

  32. Yu R, Zhang Y, Gjessing S, Xia W, Yang K (2013) Toward cloud-based vehicular networks with efficient resource management. IEEE Netw 27(5):48–55

    Article  Google Scholar 

  33. Wang T, Song L, Han Z (2013) Coalitional graph games for popular content distribution in cognitive radio vanets. IEEE Trans Veh Technol 62(8):4010–4019

    Article  Google Scholar 

  34. Berchtold S, Keim DA, Kriegel H-P, Seidl T (2000) Indexing the solution space: a new technique for nearest neighbor search in high-dimensional space. IEEE Trans Knowl Data Eng 12(1):45–57

    Article  Google Scholar 

  35. Li Y, Shu L, Li J, Zhu R, Chen Y (2016) Spatial keyword query processing in the internet of vehicles. In: 2nd EAI international conference on industrial networks and intelligent systems. Leicester

  36. De Felipe I, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: Proc. ICDE. IEEE, pp 656–665

  37. Wang Y, Xu C, Gu Y, Chen M, Yu G (2013) Spatial query processing in road networks for wireless data broadcast. Wireless Netw 19(4):477–494

    Article  Google Scholar 

  38. Rocha-Junior JB, Gkorgkas O, Jonassen S, Nørvåg K (2011) Efficient processing of top-k spatial keyword queries. In: Advances in spatial and temporal databases. Springer, pp 205–222

  39. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523

    Article  Google Scholar 

  40. Möhring RH, Schilling H, Schütz B, Wagner D, Willhalm T (2007) Partitioning graphs to speedup dijkstra’s algorithm. J Exper Algor (JEA) 11:2–8

    MathSciNet  MATH  Google Scholar 

  41. Imielinski T, Viswanathan S, Badrinath B (1997) Data on air: organization and access. IEEE Trans Knowl Data Eng 9(3):353–372

    Article  Google Scholar 

  42. Brinkhoff T (2002) A framework for generating network-based moving objects. GeoInformatica 6(2):153–180

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by National Science Foundation of China (No.61309002, No.61272497), Fundamental Research Funds for the Central Universities (No.CZZ17003) and Youth Elite Project of State Ethnic Affairs Commission of China.

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Authors and Affiliations

  1. College of Computer Science, South-Central University for Nationalities, Wuhan, China

    Yanhong Li & Rongbo Zhu

  2. School of Computer, Central China Normal University, Wuhan, China

    Changyin Luo

  3. Guangdong University of Petrochemical Technology, Maoming, China

    Yuanfang Chen

  4. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Huacheng Zeng

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  1. Yanhong Li
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  2. Changyin Luo
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Correspondence to Rongbo Zhu.

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Li, Y., Luo, C., Zhu, R. et al. Efficient Spatial Keyword Query Processing in the Internet of Industrial Vehicles. Mobile Netw Appl 23, 864–878 (2018). https://doi.org/10.1007/s11036-017-0877-y

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