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Cost Efficient Edge Service Placement for Crowdsensing via Bus Passengers

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Abstract

Edge computing is highly recommended to support Mobile Crowdsensing (MCS) applications for sensing data processing. In this paper, we consider the MCS applications supported by the mobile phones of bus passengers, who transfer between different bus stations equipped with edge servers. The edge servers deployed with the corresponding MCS services can acquire and process the sensing data directly from the participants’ sensors by device-to-device (D2D) communication. Therefore, with the help of edge services, it is desirable to deploy more MCS services to explore more D2D communications, without incurring cellular communication cost. Different stations are with different the number of passengers passing through, resulting in different benefits of edge service deployment. Taking the bus passenger mobility characteristics into consideration, we shall seek a tradeoff between the communication cost and service deployment cost to pursue overall cost efficiency. We first formulate the problem into a mixed integer linear programming model and then design a low-complexity heuristic algorithm. Performance evaluation results verify the high efficiency of our algorithm by the fact that it can much approach the optimal solution.

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References

  1. Ganti RK, Ye F, Lei H (2011) . IEEE Commun Mag 49(11):32. https://doi.org/10.1109/MCOM.2011.6069707

    Article  Google Scholar 

  2. Guo B, Wang Z, Yu Z, Wang Y, Yen NY, Huang R, Zhou X (2015) . ACM Comput Surv 48(1):1. https://doi.org/10.1145/2794400

    Article  Google Scholar 

  3. Yu X, Zhao H, Zhang L, Wu S, Krishnamachari B, Li VOK (2010) In: Proceedings of the IEEE international conference on communications, pp 1–5. https://doi.org/10.1109/ICC.2010.5502562

  4. Zhang D, Xiong H, Wang L, Chen G (2014) In: Proceedings of the ACM conference on ubiquitous computing (UbiComp), pp 703–714. https://doi.org/10.1145/2632048.2632059

  5. Zhou P, Jiang S, Li M (2015) In: Proceedings of the IEEE international conference on distributed computing systems, pp 21–30. https://doi.org/10.1109/ICDCS.2015.11

  6. Tang B, Chen Z, Hefferman G, Pei S, Wei T, He H, Yang Q (2017) . IEEE Trans Ind Inf 13(5):2140. https://doi.org/10.1109/TII.2017.2679740

    Article  Google Scholar 

  7. Yan J, Wu D, Wang H, Wu D, Wang R (2017) .. In: Proceedings of the international conference on wireless algorithms, systems, and applications, pp 314–325. https://doi.org/10.1007/978-3-319-60033-8_28

  8. Zhan Y, Xia Y, Zhang J, Wang Y (2018) . IEEE J Internet of Things 5(1):246. https://doi.org/10.1109/JIOT.2017.2779176

    Article  Google Scholar 

  9. Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2017) . IEEE Trans Emerg Top Comput 5(1):108. https://doi.org/10.1109/TETC.2015.2508382

    Article  Google Scholar 

  10. Gu L, Cai J, Zeng D, Zhang Y, Jin H, Dai W (2019) . Journal Future Generation Computer Systems 95:89. https://doi.org/10.1016/j.future.2018.12.062

    Article  Google Scholar 

  11. Ganz F, Puschmann D, Barnaghi PM, Carrez F (2015) . IEEE J. Internet of Things 2(4):340. https://doi.org/10.1109/JIOT.2015.2411227

    Article  Google Scholar 

  12. Wang X, Yang LT, Xie X, Jin J, Deen MJ (2017) . IEEE Commun Mag 55(11):80. https://doi.org/10.1109/MCOM.2017.1700360

    Article  Google Scholar 

  13. Razzaque MA, Milojevic-Jevric M, Palade A, Clarke S (2016) . IEEE J Internet of Things 3(1):70. https://doi.org/10.1109/JIOT.2015.2498900

    Article  Google Scholar 

  14. Ai Y, Peng M, Zhang K (2018) . Digital Commun Netw 4(2):77. https://doi.org/10.1016/j.dcan.2017.07.001

    Article  Google Scholar 

  15. Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) . IEEE J Internet of Things 4 (5):1125. https://doi.org/10.1109/JIOT.2017.2683200

    Article  Google Scholar 

  16. Brogi A, Forti S (2017) . IEEE J. Internet of Things 4(5):1185. https://doi.org/10.1109/JIOT.2017.2701408

    Article  Google Scholar 

  17. Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) . IEEE Trans Computers 65(12):3702. https://doi.org/10.1109/TC.2016.2536019

    Article  MathSciNet  Google Scholar 

  18. Zeng D, Gu L, Yao H (2018) Future generation computer systems

  19. Basudan S, Lin X, Sankaranarayanan K (2017) . IEEE J. Internet of Things 4(3):772. https://doi.org/10.1109/JIOT.2017.2666783

    Article  Google Scholar 

  20. Hou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) . IEEE Trans Vehicular Technol 65(6):3860. https://doi.org/10.1109/TVT.2016.2532863

    Article  Google Scholar 

  21. Aazam M, Huh E (2016) . IEEE J Potentials 35(3):40. https://doi.org/10.1109/MPOT.2015.2456213

    Article  Google Scholar 

  22. Hu P, Ning H, Qiu T, Song H, Wang Y, Yao X (2017) . IEEE J Internet of Things 4(5):1143. https://doi.org/10.1109/JIOT.2017.2659783

    Article  Google Scholar 

  23. Dastjerdi AV, Buyya R (2016) . IEEE Comput 49(8):112. https://doi.org/10.1109/MC.2016.245

    Article  Google Scholar 

  24. Zhang F, Jin B, Liu H, Leung Y, Chu X (2016) In: Proceedings of the IEEE global communications conference, pp 1–7. https://doi.org/10.1109/GLOCOM.2016.7841988

  25. Xiao M, Wu J, Huang H, Huang L, Hu C (2016) In: Proceedings of the IEEE international conference on distributed computing systems, pp 721–722. https://doi.org/10.1109/ICDCS.2016.15

  26. Karaliopoulos M, Telelis O, Koutsopoulos I (2015) In: Proceedings of the IEEE conference on computer communications, pp 2254–2262. https://doi.org/10.1109/INFOCOM.2015.7218612

  27. Wang Y, Li H, Li T (2017) . Pers Ubiquit Comput 21(1):31. https://doi.org/10.1007/s00779-016-0974-0

    Article  Google Scholar 

  28. Li Y, Wang W (2014) . IEEE Trans Wireless Commun 13(7):3978. https://doi.org/10.1109/TWC.2014.2317703

    Article  Google Scholar 

  29. Pan S, Zhou Y, Zhang Z, Yang S, Qian F, Hu G (2019) . J Netw Syst Manag 27(2):409. https://doi.org/10.1007/s10922-018-9471-2

    Article  Google Scholar 

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computing Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Haixiang Hou, Hai Jin & Xiaofei Liao

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  1. Haixiang Hou
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  2. Hai Jin
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  3. Xiaofei Liao
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Hou, H., Jin, H. & Liao, X. Cost Efficient Edge Service Placement for Crowdsensing via Bus Passengers. Mobile Netw Appl 26, 899–908 (2021). https://doi.org/10.1007/s11036-019-01350-3

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