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Periodic Query Optimization Leveraging Popularity-Based Caching in Wireless Sensor Networks for Industrial IoT Applications

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Abstract

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.

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References

  1. Ali W, Shamsuddin SM, Ismail AS (2011) A survey of web caching and prefetching. Int J Adv Soft Comput Appl 3(1):18–44

    Google Scholar 

  2. Brayner A, Coelho AL, Marinho K, Holanda R, Castro W (2014) On query processing in wireless sensor networks using classes of quality of queries. Information Fusion 15:44–55

    Article  Google Scholar 

  3. Can Z, Demirbas M (2013) A survey on in-network querying and tracking services for wireless sensor networks. Ad Hoc Netw 11:596–610

    Article  Google Scholar 

  4. Chauhan N, Awasthi L, Chand N (2012) Cluster based efficient caching technique for wireless sensor networks. In: International Conference on Latest Computational Technologies, pp 85–89

  5. Diallo O, Rodrigues JJPC, Sene M (2012) Real-time data management on wireless sensor networks: A survey. J Netw Comput Appl 35:1013–1021

    Article  Google Scholar 

  6. Dimokas N, Katsaros D (2013) Detecting energy-efficient central nodes for cooperative caching in wireless sensor networks. In: IEEE 27th International Conference on Advanced Information Networking and Applications, pp 484–491

  7. Erdelj M, Loscrł V, Natalizio E, Razafindralambo T (2013) Multiple point of interest discovery and coverage with mobile wireless sensors. Ad Hoc Netw 11:2288–2300

    Article  Google Scholar 

  8. Espada JP, Garcła-Dłaz V, Crespo RG, Martłnez OS, G-Bustelo BCP, Lovelle JMC (2014) Mobile web-based system for remote-controlled electronic devices and smart objects. Mobile Networks and Applications 19(3):435–447

    Article  Google Scholar 

  9. Forster A, Forster AL, Murphy A (2010) Optimal cluster sizes for wireless sensor networks: An experimental analysis. In: First International Conference on Ad Hoc Networks, pp 49–63

  10. Grilo AM, Heidrich M (2013) Routing metrics for cache-based reliable transport in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 139

  11. Gupta R, Ramamritham K (2012) Query planning for continuous aggregation queries over a network of data aggregators. IEEE Trans Knowl Data Eng 24(6)

  12. Hariharan S, Bisdikian C, Kaplan LM, Pham T (2014) Efficient solutions framework for optimal multitask resource assignments for data fusion in wireless sensor networks. ACM Transactions on Sensor Networks 10(3):48

    Article  Google Scholar 

  13. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670

    Article  Google Scholar 

  14. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: 33rd Annual Hawaii International Conference on System Sciences, pp 1–10

  15. Hoseini-Tabatabaei SA, Gluhak A, Tafazolli R (2013) A survey on smartphone-based systems for opportunistic user context recognition. ACM Comput Surv 45(3):27

    Article  Google Scholar 

  16. Ji S, Beyah R, Cai Z (2014) Snapshot and continuous data collection in probabilistic wireless sensor networks. IEEE Trans Mob Comput 13(3):626–637

    Article  Google Scholar 

  17. Ji S, He JS, Uluagac AS, Beyah R, Li Y (2013) Cell-based snapshot and continuous data collection in wireless sensor networks. ACM Transactions on Sensor Networks 9(4):47

    Article  Google Scholar 

  18. Keskin ME, Altnel IK, Aras N, Ersoy C (2014) Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility. Ad Hoc Netw 17:18–36

    Article  Google Scholar 

  19. Khelil A (2014) A comparative effectiveness evaluation of map construction protocols in wireless sensor networks. IEEE Syst J 8(3):708–716

    Article  Google Scholar 

  20. Khoury R, Dawborn T, Gafurov B, Pink G, Tse E, Tse Q, AlmiAni K, Gaber M, Rhm U, Scholz B (2010) Corona: Energy-efficient multi-query processing in wireless sensor networks. In: 15th International Conference on Database Systems for Advanced Applications, pp 416–419

  21. Kimand D, Uma R, Abay BH, Wu W, Wang W, Tokuta AO (2014) Minimum latency multiple data mule trajectory planning in wireless sensor networks. IEEE Trans Mob Comput 13(4):838–851

    Article  Google Scholar 

  22. Kumar H, Rai MK (2014) Caching in wireless sensor networks: A survey. International Journal of Engineering Trends and Technology 10(11):549–553

    Google Scholar 

  23. Li G, Guo L, Gao X, Liao M (2014) Bloom filter based processing algorithms for the multi-dimensional event query in wireless sensor networks. J Netw Comput Appl 37:323–333

    Article  Google Scholar 

  24. Li S, Wang X, Zhao S, Wang J, Li L (2014) Local semidefinite programming-based node localization system for wireless sensor network applications. IEEE Syst J 8(3):879–888

    Article  MathSciNet  Google Scholar 

  25. Lone R, Medagliani P, Leguay J (2013) Optimizing qos in wireless sensors networks using a caching platform. In: 2nd International Conference on Sensor Networks, pp 23–32

  26. Mahajan PC (2013) A new approach for scheduling periodic aggregation queries in wireless sensor network with aggregation delay. International Journal of Advanced Research in Computer and Communication Engineering 2(4):1712–1717

    Google Scholar 

  27. Mohamed MMA, Khokhar A, Trajcevski G, Ansari R, Ouksel A (2012) Approximate hybrid query processing in wireless sensor networks. In: 20th International Conference on Advances in Geographic Information Systems, pp 542–545

  28. Pant S, Chauhan N, Kumar P (2010) Effective cache based policies in wireless sensor networks: A survey. Int J Comput Appl 11(10):17–21

    Google Scholar 

  29. Papadopoulos S, Kiayias A, Papadias D (2012) Exact in-network aggregation with integrity and confidentiality. IEEE Trans Knowl Data Eng 24(10):1760–1773

    Article  Google Scholar 

  30. Rault T, Bouabdallah A, Challal Y (2014) Energy efficiency in wireless sensor networks: A top-down survey. Comput Netw 67:104–122

    Article  Google Scholar 

  31. Razzaque MAA, Bleakley C, Dobson S (2013) Compression in wireless sensor networks: A survey and comparative evaluation. ACM Transactions on Sensor Networks 10(1):5

    Article  Google Scholar 

  32. Sarkar R, Gao J (2013) Differential forms for target tracking and aggregate queries in distributed networks. IEEE/ACM Trans. Networking 21(4):1159–1172

    Article  Google Scholar 

  33. Song J, Kunz A, Schmidt M, Szczytowski P (2014) Connecting and managing m2m devices in the future internet. Mobile Networks and Applications 19(1):4–17

    Article  Google Scholar 

  34. Stankovic JA (2014) Research directions for the internet of things. IEEE Internet of Things Journal 1(1):3–9

    Article  Google Scholar 

  35. Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645

    Article  Google Scholar 

  36. Villas LA, Boukerche A, de Oliveira HA, de Araujo RB, Loureiro AA (2014) A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks. Ad Hoc Netw 12:69–85

    Article  Google Scholar 

  37. Wang D, Amin MT, Li S, Abdelzaher T, Kaplan L, Gu S, Pan C, Liu H, Aggarwal CC, Ganti R, Wang X, Mohapatra P, Szymanski B, Le H (2014) Using humans as sensors: An estimation-theoretic perspective. In: IEEE/ACM International Conference on Information Processing in Sensor Networks, pp 35–46

  38. Wang L, Chen L, Papadias D (2013) Managing and Mining Sensor Data, chap. Query Processing in Wireless Sensor Networks, pp 51–76. Springer US

  39. Xie R, Jia X (2014) Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Transactions on Parallel and Distributed Systems 25(3):806–815

    Article  Google Scholar 

  40. Xu X, Li XY, Song M (2013) Distributed scheduling for real-time data collection in wireless sensor networks. In: IEEE Global Communications Conference, pp 426–431

  41. Xu X, Li XY, Wan PJ, Tang S (2012) Efficient scheduling for periodic aggregation queries in multihop sensor networks. IEEE/ACM Trans Networking 20(3):690–698

    Article  Google Scholar 

  42. Yun Y, Xia Y (2010) Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Trans Mob Comput 9(9):1308–1318

    Article  Google Scholar 

  43. Zhao M, Yang Y (2012) Optimization-based distributed algorithms for mobile data gathering in wireless sensor networks. IEEE Trans Mob Comput 11(10):1464–1477

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (Grant No. 61379126 and 61401107), by the Scientific Research Foundation for Returned Scholars, Ministry of Education of China, by the Educational Commission of Guangdong Province, China (Grant No. 2013KJCX0131), by 2013 Special Fund of Guangdong Higher School Talent Recruitment, and by the Fundamental Research Funds for the Central Universities (China University of Geosciences at Beijing).

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Correspondence to ZhangBing Zhou.

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Zhou, Z., Zhao, D., Xu, X. et al. Periodic Query Optimization Leveraging Popularity-Based Caching in Wireless Sensor Networks for Industrial IoT Applications. Mobile Netw Appl 20, 124–136 (2015). https://doi.org/10.1007/s11036-014-0545-4

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