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Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems

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Abstract

Billions of sensing devices have been connected to the Internet of Things (IoT), generating a large volume of data that can be turned into valuable insights for many applications. Location information is critical for many IoT applications. However, most sensor devices are randomly deployed and locations are unknown. Thus, it is a challenging issue to discover the physical topology of the IoT system consisted of thousands of low-cost sensor devices. In this paper, a Vehicles joint UAVs Topology Discovery (VUTD) scheme is proposed that can discover the physical topology with low-cost and accuracy. There are two main steps in VUTD scheme: (1) Vehicles are used as mobile anchors to assist adjacent sensor devices in positioning. They are also used to collect logical topology information of the IoT system. The collected logical topology information and location information can be combined into physical topology information that will be sent to the cloud platform through vehicles. (2) The cloud platform analyzes the received information to determine the area where the physical topology discovery is not completed. Then, the cloud platform dispatches the UAV as a flight anchor to locate these points. Experiments based on realworld taxi trajectory are conducted to verify the effectiveness of VUTD scheme. The experimental results show that the VUTD scheme has better performance. Compared with the VTD scheme, the localization ratio is increased by up to 13.6%, and the mean localization error is reduced by up to 90.78%. Compared with UTD, the cost of location discovery is reduced by up to 77.7%.

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References

  1. Internet of things market forecast: Cisco. [Online]. Available: http://postscapes.com/internet-of-things-market-size

  2. Tang W, Ren J, Zhang Y (2019) Enabling trusted and privacy-preserving healthcare Services in Social Media Health Networks. IEEE Transactions on Multimedia 21(3):579–590

    Article  Google Scholar 

  3. Kuang Z, Li L, Gao J, Zhao L, Liu A (2019) Partial offloading scheduling and power allocation for Mobile edge computing systems. IEEE Internet Things J 6(4):6774–6785

    Article  Google Scholar 

  4. Sarkar S, Chatterjee S, Misra S (2018) Assessment of the suitability of fog computing in the context of internet of things. IEEE Transactions on Cloud Computing 6(1):46–59

    Article  Google Scholar 

  5. Zhao W (2016) Performance optimization for state machine replication based on application semantics: a review. J Syst Softw 112:96–109

    Article  Google Scholar 

  6. Liu Q, Hou P, Wang G, Peng T, Zhang S (2019) Intelligent route planning on large road networks with efficiency and privacy. Journal of Parallel and Distributed Computing 133:93–106

    Article  Google Scholar 

  7. Deng X, Luo J, He L, Liu Q, Li X, Cai L (2019) Cooperative channel allocation and scheduling in multi-interface wireless mesh networks. Peer-to-Peer Networking and Applications 12(1):1–12

    Article  Google Scholar 

  8. Liu X, Wang T, Jia W, Liu A, Chi K (2019) Quick convex hull-based rendezvous planning for delay-harsh mobile data gathering in disjoint sensor networks. IEEE Transactions on System, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2019.2938790

  9. Liu Y, Ma M, Liu X, Xiong N, Liu A, Zhu Y (2018) Design and analysis of probing route to defense sink-hole attacks for internet of things security. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2018.2881152

  10. Zhang D, Shen R, Ren J, Zhang Y (2018) Delay-optimal proactive service framework for block-stream as a service. IEEE Wireless Communications Letters 7(4):598–601. https://doi.org/10.1109/LWC.2018.2799935

    Article  Google Scholar 

  11. Liu Q, Tian Y, Wu J, Peng T, Wang G (2019) Enabling verifiable and dynamic ranked search over outsourced data transactions on services computing. https://doi.org/10.1109/TSC.2019.2922177

  12. Liu X, Liu A, Wang T, Ota K, Dong M, Liu Y, Cai Z (2020) Adaptive data and verified message disjoint security routing for gathering big data in energy harvesting networks. Journal of Parallel and Distributed Computing 135:140–155

    Article  Google Scholar 

  13. Zhang D, Tan L, Ren J, Awad MK, Zhang S, Zhang Y, Wan PJ (2019) Near-optimal and truthful online auction for computation offloading in green edge-computing systems. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2019.2901474

  14. Cheng N, Lyu F, Quan W, Zhou C, He H, Shi W, Shen X (2019) Space/aerial-assisted computing offloading for IoT applications: a learning-based approach. IEEE Journal on Selected Areas in Communications 37(5):1117–1129

    Article  Google Scholar 

  15. Luo X, Jiang C, Wang W, Xu Y, Wang JH, Zhao W (2019) User behaviour prediction in social networks using weighted extreme learning machine with distribution optimization. Futur Gener Comput Syst 93:1023–1035

    Article  Google Scholar 

  16. Wang T, Luo H, Jia W, Liu A, Xie M (2019) MTES: an intelligent trust evaluation scheme in sensor-cloud enabled industrial internet of things. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2019.2930286

  17. Duan S, Zhang D, Wang Y, Li L, Zhang Y (2019) JointRec: a deep learning-based joint cloud video recommendation framework for Mobile IoTs. IEEE Internet of Things. https://doi.org/10.1109/JIOT.2019.2944889

  18. Tang W, Ren J, Zhang K, Zhang D, Zhang Y, Shen XS (2019) Efficient and privacy-preserving fog-assisted health data sharing scheme. ACM Trans Intell Syst Technol. https://doi.org/10.1145/3341104

  19. Thiagarajan A, Ravindranath L, LaCurts K, Madden S, Balakrishnan H, Toledo S, Eriksson J (2009) VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. Proceedings of the 7th ACM conference on embedded networked sensor systems 85-98

  20. Waze - outsmarting traffic, together (2013) [Online]. Available: http://www.waze.com/

  21. WeatherLah iPhone application (2012) [online] Available: http://itunes.apple.com/us/app/weatherlah/id411646329?mt=8

  22. Wang T, Ke H, Zheng X, Wang K, Sangaiah A, Liu A (2019) Big data cleaning based on Mobile edge computing in industrial sensor-cloud. IEEE Transactions on Industrial Informatics 99:1–1. https://doi.org/10.1109/TII.2019.2938861

    Article  Google Scholar 

  23. Ren J, Zhang Y, Zhang K, Liu A, Chen J, Shen XS (2016) Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Transactions on Industrial Informatics 12(2):788–800

    Article  Google Scholar 

  24. Liu W, Zhuang P, Liang H, Peng J, Huang Z (2018) Distributed economic dispatch in microgrids based on cooperative reinforcement learning. IEEE Transactions on Neural Networks and Learning 29(6):2192–2203

    Article  MathSciNet  Google Scholar 

  25. Liu Y, Liu A, Liu X, Ma M (2019) A trust-based active detection for cyber-physical security in industrial environments. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2019.2931394

  26. Hu L, Liu A, Xie M, Wang T (2019) UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City. Peer-to-Peer Networking and Applications 12(6):1550–1574. https://doi.org/10.1007/s12083-019-00752-0

    Article  Google Scholar 

  27. Yu T, Wang X, Jin J, McIsaac K (2018) Cloud-orchestrated physical topology discovery of large-scale IoT systems using UAVs. IEEE Transactions on Industrial Informatics 14(5):2261–2270

    Article  Google Scholar 

  28. Teng H, Liu Y, Liu A, Xiong NN, Cai Z, Wang T, Liu X (2019) A novel code data dissemination scheme for internet of things through Mobile vehicle of smart cities. Futur Gener Comput Syst 94:351–367

    Article  Google Scholar 

  29. Wang Y, Su Z, Xu Q, Yang T, Zhang N (2019) A novel charging scheme for electric vehicles with smart communities in vehicular networks. IEEE Trans Veh Technol 68(9):8487–8501. https://doi.org/10.1109/TVT.2019.2923851

    Article  Google Scholar 

  30. Lyu F, Zhu H, Cheng N, Zhou H, Xu W, Li M, Shen XS (2019) Characterizing urban vehicle-to-vehicle Communications for Reliable Safety Applications. IEEE Trans Intell Transp Syst:1–17. https://doi.org/10.1109/TITS.2019.2920813

  31. Li L, Ota K, Dong M (2017) When weather matters: IoT-based electrical load forecasting for smart grid. IEEE Commun Mag 55(10):46–51

    Article  Google Scholar 

  32. Li L, Ota K, Dong M (2018) Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics 14(10):4665–4673

    Article  Google Scholar 

  33. Liu X, Zhao M, Liu A, Wong K (2020) Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks. Information Fusion 53:183–195

    Article  Google Scholar 

  34. Li Q, Liu A, Wang T, Xie M, Xiong N (2019) Pipeline slot based fast rerouting scheme for delay optimization in duty cycle based M2M communications. Peer-to-Peer Networking and Applications 12(6):1673–1704. https://doi.org/10.1007/s12083-019-00753-z

    Article  Google Scholar 

  35. Zhang D, Chen Z, Cai LX, Zhou H, Duan S, Ren J, Zhang Y (2017) Resource allocation for green cloud radio access networks with hybrid energy supplies. IEEE Trans Veh Technol 67(2):1684–1697

    Article  Google Scholar 

  36. Zhang D, Qiao Y, She L, Shen R, Ren J, Zhang Y (2019) Two time-scale resource management for green internet of things networks. IEEE Internet Things J 6(1):545–556. https://doi.org/10.1109/JIOT.2018.2842766

    Article  Google Scholar 

  37. Dong M, Ota K, Liu A (2016) RMER: reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet Things J 3(4):511–519

    Article  Google Scholar 

  38. Liu Y, Liu A, Wang T, Liu X, Xiong N (2019) An intelligent incentive mechanism for coverage of data collection in cognitive internet of things. Futur Gener Comput Syst 100:701–714

    Article  Google Scholar 

  39. Ota K, Dong M, Gui J, Liu A (2018) QUOIN: incentive mechanisms for crowd sensing networks. IEEE Netw 32(2):114–119

    Article  Google Scholar 

  40. Wang J, Wang Y, Zhang D, Lv Q, Chen C (2019) Crowd-powered sensing and actuation in smart cities: current issues and future directions. IEEE Wirel Commun 26(2):86–92

    Article  Google Scholar 

  41. Wang J, Wang Y, Zhang D, Wang F, Xiong H, Chen C, Qiu Z (2018) Multi-task allocation in mobile crowd sensing with individual task quality assurance. IEEE Trans Mob Comput 17(9):2101–2113

    Article  Google Scholar 

  42. Zhang C, Chen R, Zhu L, Liu A, Lin Y, Huang F (2018) Hierarchical information Quadtree: efficient spatial temporal image search for multimedia stream. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6284-y

  43. Yi G, Park JH, Choi S (2016) Energy-efficient distributed topology control algorithm for low-power IoT communication networks. IEEE Access 4:9193–9203

    Article  Google Scholar 

  44. Wang T, Zhao D, Cai S, Jia W, Liu A (2019) Bidirectional prediction based underwater data collection protocol for end-edge-cloud orchestrated system. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2019.2940745

  45. Tang XF, Niu XZ, Ali S (2014) Research on energy-aware topology strategy based on wireless sensor in internet of things. International Journal of Computational Intelligence Systems 7(6):1137–1147

    Article  Google Scholar 

  46. Ccori, PC, De Biase LCC, Zuffo MK, da Silva, FSC (2016) Device discovery strategies for the IoT. In 2016 IEEE International Symposium on Consumer Electronics (ISCE) 97–98

  47. Abdolmaleki N, Ahmadi M, Malazi HT, Milardo S (2017) Fuzzy topology discovery protocol for SDN-based wireless sensor networks. Simul Model Pract Theory 79:54–68

    Article  Google Scholar 

  48. Coluccia A, Ricciato F (2014) Rss-based localization via bayesian ranging and iterative least squares positioning. IEEE Commun Lett 18(5):873–876

    Article  Google Scholar 

  49. Yaghoubi F, Abbasfar A, Maham B (2014) Energy-efficient rssi-based localization for wireless sensor networks. IEEE Commun Lett 18(6):973–976

    Article  Google Scholar 

  50. Xu Y, Zhou J, Zhang P (2014) Rss-based source localization when path-loss model parameters are unknown. IEEE Commun Lett 18(6):1055–1058

    Article  Google Scholar 

  51. Bandiera F, Coluccia A, Ricci G (2015) A cognitive algorithm for received signal strength based localization. IEEE Trans Signal Process 63(7):1726–1736

    Article  MathSciNet  Google Scholar 

  52. A. Goldsmith, Wireless communications. Cambridge university press, 2005

  53. Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world. In proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining 316-324

  54. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. Proceedings of the 18th SIGSPATIAL International conference on advances in geographic information systems 99–108

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (61772554).

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Correspondence to Anfeng Liu.

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This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge

Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong

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Teng, H., Ota, K., Liu, A. et al. Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems. Peer-to-Peer Netw. Appl. 13, 1720–1743 (2020). https://doi.org/10.1007/s12083-020-00879-5

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