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UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City

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Abstract

With the rapid development of software-defined technologies, emerging multimedia applications are booming, which require real-time communication and computation via devices. Meanwhile, the program codes in multimedia applications need to be updated periodically to accommodate changes in the edge networking environment. Therefore, a huge number of smart sensing devices which are deployed in the infrastructures of smart city can play a bigger role than ever before due to their program codes in multimedia applications can be updated by sensing data. However, how to spread program codes to a large amount of devices which are distributed in smart city in a low-cost and fast way is a challenging issue. To solve the issue, in this paper, an Unmanned Aerial Vehicles (UAVs) joint Vehicles as Data Mules for Fast Codes Dissemination (UVDCD) scheme is proposed to spread codes as a fast and low-cost pattern for edge networking in smart city. In UVDCD scheme, first of all, large amount of vehicles in smart city act as mules for code dissemination. Although the cost of the method is low and can quickly cover most networks, there is a very long trailing phenomenon for this approach, i.e. in the early stage of code dissemination, the code dissemination rate increases rapidly over time and have a high efficiency, but after a short time, the increase of code dissemination rate has become very slowly over time. So, in this situation, the unmanned aerial vehicle is used to spread the program codes of intelligent devices where vehicles are hard to spread, thereby eliminating the trailing phenomenon in code dissemination. In order to achieve a high dissemination efficiency for UAV, first, we cluster the device nodes which have not received codes, then we select the optimized UAV flight trajectory based on the cluster so that the total length of the UAV flight path is the shortest, i.e. low-cost, and the number of devices which can receive codes is the largest. Finally, the validity of UVDCD scheme is confirmed based on real vehicle data, and through experiments, it can effectively overcome the trailing phenomenon in code dissemination after using UAV. The coverage ratio and dissemination speed are higher than those of previous strategies and increase 2.11% and 69.44% respectively.

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References

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

    Google Scholar 

  2. Internet of things market forecast: Cisco. [Online]. Available: http://postscapes.com/internet-of-things-market-size. Accessed Dec 2018

  3. 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 Peer Netw Appl 12(1):1–12

    Google Scholar 

  4. Li Z, Liu Y, Liu A, Wang S, Liu H (2018) Minimizing Convergecast time and energy consumption in green internet of things. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2018.2844282

  5. Zhang H, Zheng WX (2018) Denial-of-service power dispatch against linear quadratic control via a fading channel. IEEE Trans Autom Control 63(9):3032–3039

    MathSciNet  MATH  Google Scholar 

  6. Sood K, Mahajan I (2018) Fog-cloud based cyber-physical system for distinguishing, detecting and preventing mosquito borne diseases. Futur Gener Comput Syst 88:764–775

    Google Scholar 

  7. Zhang ZH, Meng W, Qi J, Wang X, Zheng W (2019) Distributed load sharing under false data injection attack in an inverter-based microgrid. IEEE Trans Ind Electron 66(2):1543–1551

    Google Scholar 

  8. Liu Y, Liu A, Zhang N, Liu X, Ma M, Hu Y (2019) DDC: dynamic duty cycle for improving delay and energy efficiency in wireless sensor networks. J Netw Comput Appl 131:16–27

    Google Scholar 

  9. Li T, Tian S, Liu A, Liu H, Pei T (2018) DDSV: optimizing delay and delivery ratio for multimedia big data collection in Mobile sensing vehicles. IEEE Internet Things J 5(5):3474–3486

    Google Scholar 

  10. Kong X, Xia F, Ning Z, Rahim A, Cai Y, Gao Z, Ma J (2018) Mobility dataset generation for vehicular social networks based on floating car data. IEEE Trans Veh Technol 67(5):3874–3886

    Google Scholar 

  11. Ren J, Guo H, Xu C, Zhang Y (2017) Serving at the edge: a scalable IoT architecture based on transparent computing. IEEE Netw 31(5):96–105

    Google Scholar 

  12. Kong X, Xia F, Wang J, Rahim A, Das SK (2017) Time-location-relationship combined service recommendation based on taxi trajectory data. IEEE Trans Ind Inf 13(3):1202–1212

    Google Scholar 

  13. Zhu Y, Zhong Z, Basin MV, Zhou D (2018) A descriptor system approach to stability and stabilization of discrete-time switched PWA systems. IEEE Trans Autom Control 63(10):3456–3463

    MathSciNet  MATH  Google Scholar 

  14. Malandra F, Chiquette L et al (2018) Traffic characterization and LTE performance analysis for M2M communications in smart cities. Pervasive Mob Comput 48:59–68

    Google Scholar 

  15. Ren J, Zhang Y, Zhang N, Zhang D, Shen X (2016) Dynamic Channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Trans Wirel Commun 15(5):3143–3156

    Google Scholar 

  16. Tan J, Liu W, Wang T, Xiong N, Song H, Liu A, Zeng Z (2019) An adaptive collection scheme based matrix completion for data gathering in energy-harvesting wireless sensor network. IEEE Access 7(1):6703–6723

    Google Scholar 

  17. Li T, Ota K, Wang T, Li X, Cai Z, Liu A (2019) Optimizing the coverage via the UAVs with lower costs for information-centric internet of things. IEEE Access 7(1):15292–15309

    Google Scholar 

  18. Ren Y, Liu Y, Zhang N, Liu A, Xiong NN, Cai Z (2018) Minimum-cost Mobile crowdsourcing with QoS guarantee using matrix completion technique. Pervasive Mob Comput 49:23–44

    Google Scholar 

  19. Zhu Y, Zhong Z, Zheng V, Zhou D (2018) HMM-based H∞ filtering for discrete-time Markov jump LPV systems over unreliable communication channels. IEEE Trans Syst Man Cybern Syst 48(12):2035–2046

    Google Scholar 

  20. 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

    Google Scholar 

  21. Guo Y, Hu X, Hu B, Cheng J, Zhou M, Kwok RYK (2018) Mobile cyber physical systems: current challenges and future networking applications. IEEE Access 6:12360–12368

    Google Scholar 

  22. 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 T Netw Sci Eng https://doi.org/10.1109/TNSE.2018.2881152

  23. Thiagarajan A, Ravindranath L, LaCurts K, et al. (2009) VTrack: accurate, energy-aware road traffic delay estimation using mobile phone. Proceedings of the 7th ACM conference on embedded networked sensor systems. ACM, 85–98

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

  25. Gui J, Hui L, Xiong N (2018) Enhancing cellular coverage quality by virtual access point and wireless power transfer. Wirel Commun Mob Comput 2018: 9218239:1–19. https://doi.org/10.1155/2018/9218239

    Article  Google Scholar 

  26. Tan J, Liu W, Xie M, Song H, Liu A, Zhao M, Zhang G (2019) A low redundancy data collection scheme to maximize lifetime using matrix completion technique. EURASIP J Wirel Commun Netw 2019:5. https://doi.org/10.1186/s13638-018-1313-0

    Article  Google Scholar 

  27. Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S (2017) Narrowband internet of things: implementations and applications. IEEE Internet Things J 4(6):2309–2314

    Google Scholar 

  28. Yang G, He S, Shi Z, Chen J (2017) Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun Mag 55(3):86–92

    Google Scholar 

  29. Liu Z, Tsuda T, Watanabe H, Ryuo S, Iwasawa N (2018) Data driven cyber-physical system for landslide detection. Mobile Netw Appl https://doi.org/10.1007/s11036-018-1031-1

    Google Scholar 

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

    Google Scholar 

  31. 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

    Google Scholar 

  32. Peng X, Ren J, She L, Zhang D, Li J, Zhang Y (2018) BOAT: a block-streaming app execution scheme for lightweight IoT devices. IEEE Internet Things J 5(3):1816–1829

    Google Scholar 

  33. Liu Y, Liu A, Liu X, Huang X (2019) A statistical approach to participant selection in location-based social networks for offline event marketing. Inf Sci 480:90–108

    Google Scholar 

  34. Chen Z, Mo Y, Ouyang P, Shen H, Li D, Zhao R (2019) Retinal vessel optical coherence tomography images for Anemia screening. Med Biol Eng Comput 2019. https://doi.org/10.1007/s11517-018-1927-8

    Google Scholar 

  35. Liu A, Zhao S (2018) High performance target tracking scheme with low prediction precision requirement in WSNs. Int J Ad Hoc Ubiquitous Comput 29:270–289

    Google Scholar 

  36. Bonola M, Bracciale L, Loreti P, Amici R, Rabuffi A, Bianchi G (2016) Opportunistic communication in smart city: experimental insight with small-scale taxi fleets as data carriers. Ad Hoc Netw 43:43–55

    Google Scholar 

  37. Zeng Y, Liao S, Tang P, Zhao Y, Liao M, Chen Y, Liang Y (2018) Automatic liver vessel segmentation using 3D region growing and hybrid active contour model. Comput Biol Med 97:63–73

    Google Scholar 

  38. Liu Y, Liu A, Guo S, Li Z, Choi Y, Sekiya H (2017) Context-aware collect data with energy efficient in cyber–physical cloud systems. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.05.029

    Google Scholar 

  39. Wang T, Zhang G, Bhuiyan M, Liu A, Jia W, Xie M (2018) A novel trust mechanism based on fog computing in sensor-cloud system. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.05.049

  40. Zhang G, Wang T, Wang G, Liu A, Jia W (2018) Detection of hidden data attacks combined fog computing and trust evaluation method in sensor-cloud system. Concurr Comp-Pract E. https://doi.org/10.1002/cpe.5109

  41. Liu Y, Liu A, Xiong N, Wang T, Gui W (2019) Content propagation for content-centric networking from location-based social networks. IEEE Trans Syst Man Cybern Syst 2019:1–15. https://doi.org/10.1109/TSMC.2019.2898982

    Article  Google Scholar 

  42. Huang M, Liu A, Xiong N et al (2019) A low-latency communication scheme for Mobile wireless sensor control systems. IEEE Trans Syst Man Cybern Syst 49(2):317–332

    Google Scholar 

  43. Huang M, Liu A, Zhao M, Wang T (2018) Multi working sets alternate covering scheme for continuous partial coverage in WSNs. Peer-to-Peer Netw Appl 12:553–567. https://doi.org/10.1007/s12083-018-0647-z

    Google Scholar 

  44. Liu X, Zhang P (2018) Data drainage: a novel load balancing strategy for wireless sensor networks. IEEE Commun Lett 22(1):125–128

    MathSciNet  Google Scholar 

  45. Liu X (2016) A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. J Netw Comput Appl 67:43–52

    Google Scholar 

  46. Xu X, Yuan M, Liu X, Liu A, Xiong N, Cai Z, Wang T (2018) Cross-layer optimized opportunistic routing scheme for loss-and-delay sensitive WSNs. Sensors 18(5):1422. https://doi.org/10.3390/s18051422

    Article  Google Scholar 

  47. Yang C, Shi Z, Han K, Zhang JJ, Gu Y, Qin Z (2018) Optimization of particle CBMeMBer filters for hardware implementation. IEEE Trans Veh Technol 67:9027–9031. https://doi.org/10.1109/TVT.2018.2853120

    Article  Google Scholar 

  48. Zhou C, Gu Y, He S, Shi Z (2018) A robust and efficient algorithm for coprime array adaptive beamforming. IEEE Trans Veh Technol 67(2):1099–1112

    Google Scholar 

  49. Nguyen P, Ji Y, Liu Z et al (2017) Distributed hole-bypassing protocol in WSNs with constant stretch and load balancing. Comput Netw 129:232–250

    Google Scholar 

  50. Tham C, Luo T (2014) Fairness and social welfare in service allocation schemes for participatory sensing. Comput Netw 73:58–71

    Google Scholar 

  51. Liu W, Zhuang P, Liang h PJ, Huang Z (2018) Distributed economic dispatch in microgrids based on cooperative reinforcement learning. IEEE T Neur Net Lear 29(6):2192–2203

    MathSciNet  Google Scholar 

  52. 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

    Google Scholar 

  53. Tang Z, Liu A, Huang C (2016) Social-aware data collection scheme through opportunistic communication in vehicular Mobile networks. IEEE Access 4:6480–6502

    Google Scholar 

  54. Xu Y, Chen X, Liu A, Hu C (2017) A latency and coverage optimized data collection scheme for smart cities based on vehicular ad-hoc networks. Sensors 17(4):888. https://doi.org/10.3390/s17040888

    Article  Google Scholar 

  55. Rego A, Garcia L, Sendra S, Lloret J (2018) Software defined network-based control system for an efficient traffic management for emergency situations in smart cities. Futur Gener Comput Syst 88:243–253

    Google Scholar 

  56. Liu X, Liu Y, Zhang N, Wu W, Liu A (2019) Optimizing trajectory of unmanned aerial vehicles for efficient data acquisition: a matrix completion approach. IEEE Internet Things J 6(2):1829–1840. https://doi.org/10.1109/JIOT.2019.2894257

    Google Scholar 

  57. Ren J, Zhang Y, Zhang K, Liu A, Chen J, Shen X (2016) Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans Ind Inf 12(2):788–800

    Google Scholar 

  58. Cheng L, Niu J, Luo C, Shu L, Kong L, Zhao Z, Gu Y (2018) Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks. Comput Netw 134:66–77

    Google Scholar 

  59. Jung J, Seo D, Lee J (2018) Counter-based broadcast scheme considering reachability, network density, and energy efficiency for wireless sensor networks. Sensors 18(1):120

    Google Scholar 

  60. Liu X, Dong M, Liu Y, Liu A, Xiong NN (2018) Construction low complexity and low delay CDS for big data codes dissemination. Complexity 2018, 5429546:1–19. https://doi.org/10.1155/2018/5429546

    Article  Google Scholar 

  61. Tao M, Ota K, Dong M, Qian Z (2018) AccessAuth: capacity-aware security access authentication in federated-IoT-enabled V2G networks. J Parallel Distrib Comput 118:107–117

    Google Scholar 

  62. Tao M, Ota K, Dong M (2017) Foud: integrating fog and cloud for 5G-enabled V2G networks. IEEE Netw 31(2):8–13

    Google Scholar 

  63. Garcia F, Lopez Y, Andres F (2018) On the use of unmanned aerial vehicles for antenna and coverage diagnostics in Mobile networks. IEEE Commun Mag 56(7):72–78

    Google Scholar 

  64. Khelifi F, Bradai A, Singh K, Atri M (2018) Localization and energy-efficient data routing for unmanned aerial vehicles: fuzzy-logic-based approach. IEEE Commun Mag 56(4):129–133

    Google Scholar 

  65. Darong H, Peng W (2012) Grid-based DBSCAN algorithm with referential parameters. Phys Procedia 24:1166–1170

    Google Scholar 

  66. Yang G, He S, Shi Z (2017) Leveraging crowdsourcing for efficient malicious users detection in large-scale social networks. IEEE Internet Things J 4(2):330–339

    Google Scholar 

  67. Yuan J, Zheng Y, Xie X, et al. (2011) Driving with knowledge from the physical world. in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery data mining KDD, New York, NY, USA, 2011, 316–324

  68. SUVnet-trace data. Accessed 1, Sep, 2018. Available: http://wirelesslab.sjtu.edu.cn/taxi_trace_data.html.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61772554) and Natural Science Foundation of Zhejiang Province (LY17F020032).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Lang Hu & Anfeng Liu

  2. School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China

    Mande Xie

  3. Department of Computer Science and Technology, Huaqiao University, Xiamen, 361021, Fujian Province, China

    Tian Wang

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  1. Lang Hu
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  4. Tian Wang
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Correspondence to Mande Xie.

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This article is part of the Topical Collection: Special Issue on Fog/Edge Networking for Multimedia Applications

Guest Editors: Yong Jin, Hang Shen, Daniele D'Agostino, Nadjib Achir, and James Nightingale

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Hu, L., Liu, A., Xie, M. et al. UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City. Peer-to-Peer Netw. Appl. 12, 1550–1574 (2019). https://doi.org/10.1007/s12083-019-00752-0

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