skip to main content
research-article

Fog in the Clouds: UAVs to Provide Edge Computing to IoT Devices

Authors Info & Claims
Published:26 August 2020Publication History
Skip Abstract Section

Abstract

Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Reinforcement Learning (RL) is used to support SC in its decisions. A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework.

References

  1. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. 2015. Internet of things: A survey on enabling technologies, protocols, and applications. In IEEE Commun. Surv. Tutor. 17, 4 (2015), 2347--2376.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sisco Systems, Inc. 2019. Cisco's Mobile Visual Networking Index (VNI) Forecast (2017--2022), Retrieved from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.pdf.Google ScholarGoogle Scholar
  3. P. Mach and Z. Becvar. 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surveys Tut. 19, 3 (2017), 1628--1656.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Wang et al. 2017. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access 5 (2017), 6757--6779.Google ScholarGoogle ScholarCross RefCross Ref
  5. H. T. Dinh, C. Lee, D. Niyato, and P. Wang. 2013. A survey of mobile cloud computing: Architecture, applications, and approaches. Wirel. Commun. Mobile Comput. 13, 18 (2013), 1587--1611.Google ScholarGoogle ScholarCross RefCross Ref
  6. Alessio Botta, Walter de Donato, Valerio Persico, Antonio Pescapè. 2015. Integration of cloud computing and internet of things: A survey. J. Fut. Gen. Comput. Syst. 18 (2015), 1--54.Google ScholarGoogle Scholar
  7. E. K. Markakis et al. 2017. Efficient next generation emergency communications over multi-access edge computing. IEEE Commun. Mag. 55, 11 (2017), 92--97.Google ScholarGoogle ScholarCross RefCross Ref
  8. N. Hassan, S. Gillani, E. Ahmed, I. Yaqoob, and M. Imran. 2018. The role of edge computing in internet of things. IEEE Commun. Mag. 56, 11 (2018), 110--115.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. Jia, J. Cao, and W. Liang. 2017. Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans. Cloud Comput. 5, 4 (2017), 725--37.Google ScholarGoogle ScholarCross RefCross Ref
  10. A. M. M. Ali, N. M. Ahmad, and A. H. M. Amin. 2014. Cloudlet-based cyber foraging framework for distributed video surveillance provisioning. In Proceedings of the 4th World Congress on InfOrmation and Communication Technologies (WICT’14). 199--204.Google ScholarGoogle ScholarCross RefCross Ref
  11. T. Taleb et al. 2017. Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55, 3 (2017), 38--43.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the 1st MCC Workshop on Mobile Cloud Computing. New York, NY. ACM, 2012, 13--16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Yi, C. Li, and Q. Li, 2015. A survey of fog computing: Concepts, applications and issues. In Proceedings of the Workshop on Mobile Big Data. ACM, 37--42.Google ScholarGoogle Scholar
  14. M. Yannuzzi, R. Milito, R. Serral-Graci, D. Montero, and M. Nemirovsky. 2014. Key ingredients in an IoT recipe: Fog computing, cloud computing, and more fog computing. In Proceedings of the IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD’14). 325--329.Google ScholarGoogle Scholar
  15. M. Aazam and Eui-Nam Huh. Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In Proceedings of the IEEE 29th International Conference on Advanced Information Networking and Applications (AINA’15). 687--694.Google ScholarGoogle Scholar
  16. N. Siriphun, S. Kashihara, D. Fall, and A. Khurat. 2018. Distinguishing drone types based on acoustic wave by IoT device. In Proceedings of the 22nd International Computer Science and Engineering Conference (ICSEC’18). 1--4.Google ScholarGoogle Scholar
  17. F. Zhou, Y. Wu, H. Sun, and Z. Chu. 2018. UAV-enabled mobile edge computing: Offloading optimization and trajectory design. ArXiv Eprints, Feb. 2018.Google ScholarGoogle Scholar
  18. L. Wang and M. Hua. 2017. Optimal bit allocation for UAV-enabled mobile communication. In Proceedings of the IEEE International Conference on Computer and CommunIcations. 474--478.Google ScholarGoogle Scholar
  19. G. Lee, W. Saad, and M. Bennis. 2018. Online optimization for UAV-assisted distributed fog computing in smart factories of industry 4.0. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’18). 1--6.Google ScholarGoogle Scholar
  20. N. Mohamed, J. Al-Jaroodi, I. Jawhar, H. Noura, and S. Mahmoud. 2017. UAVFog: A UAV-based fog computing for internet of things. In Proceedings of the IEEE SmartWorld, Ubiquitous Intelligence 8 Computing, Advanced 8 Trusted Computed, Scalable Computing 8 Communications, Cloud 8 Big Data Computing, Internet of People, and Smart City Innovation Conference. (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI’17). 1--8.Google ScholarGoogle Scholar
  21. H. Ye, G. Y. Li, and B. F. Juang. 2019. Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans. Vehic. Technol. 68, 4 (2019), 3163--3173.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. A. Salahuddin, A. Al-Fuqaha, and M. Guizani. 2016. Reinforcement learning for resource provisioning in the vehicular cloud. IEEE Wirel. Commun. 23, 4 (2016), 128--135.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Y. He, N. Zhao, and H. Yin. 2018. Integrated networking, caching and computing for connected vehicles: A deep reinforcement learning approach. IEEE Trans. Vehic. Technol. 67, 1 (2018), 44--55.Google ScholarGoogle ScholarCross RefCross Ref
  24. W. Wang et al. 2018. A network traffic flow prediction with deep learning approach for large-scale metropolitan area network. In Proceedings of the IEEE/IFIP Network Operations and Management Symposium. 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  25. Akshay Agrawal. 2016. CS221 Final Report, July 24, 2016. Retrieved from https://www.debugmind.com/xavier.pdf. Last access: August 2020.Google ScholarGoogle Scholar
  26. R. Ghasem Aghaei, M. A. Rahman, W. Gueaieb, and A. El Saddik. 2007. Ant-colony-based reinforcement learning algorithm for routing in wireless sensor networks. In Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC’07). 1--6.Google ScholarGoogle Scholar
  27. S. Wang et al. 2018. Deep reinforcement learning for dynamic multichannel access in wireless networks. IEEE Trans. Cog. Commun. Netw. 4 (Feb. 2018).Google ScholarGoogle Scholar
  28. H. Bayat-Yeganeh, V. Shah-Mansouri, and H. Kebriaei. 2018. A multi-state Q-learning based CSMA MAC protocol for wireless networks. Wirel. Netw. 24, 4 (2018), 1251--1264.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. U. S. Shanthamallu, A. Spanias, C. Tepedelenlioglu, and M. Stanley. 2017. A brief survey of machine learning methods and their sensor and IoT applications. In Proceedings of the 8th International Conference on Information, Intelligence, Systems 8 Applications (IISA’17). 1--8.Google ScholarGoogle Scholar
  30. P. S. Pandey. 2017. Machine learning and IoT for prediction and detection of stress. In Proceedings of the 17th International Conference on Computational Science and Its Applications (ICCSA’17). 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  31. L. Li and A. Ghasemi. 2019. IoT-enabled machine learning for an algorithmic spectrum decision process. IEEE Internet Things J. 6, 2 (2019), 1911--1919.Google ScholarGoogle ScholarCross RefCross Ref
  32. H. Li, K. Ota, and M. Dong. 2018. Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Netw. 32, 1 (2018), 96--101.Google ScholarGoogle ScholarCross RefCross Ref
  33. D. Nemirovsky, T. Arkose, N. Markovic, M. Nemirovsky, O. Unsal, and A. Cristal. 2017. A machine learning approach for performance prediction and scheduling on heterogeneous CPUs. In Proceedings of the 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD’17). 121--128.Google ScholarGoogle Scholar
  34. Y. Yan, B. Zhang, and J. Guo. 2016. An adaptive decision making approach based on reinforcement learning for self-managed cloud applications. In Proceedings of the IEEE International Conference on Web Services (ICWS’16). 720--723.Google ScholarGoogle Scholar
  35. A. Y. Zomaya, M. Clements, and S. Olariu. 1998. A framework for reinforcement-based scheduling in parallel processor systems. IEEE Trans. Parallel Distrib. Syst. 9, 3 (1998), 249--260.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. G. Tesauro. 2007. Reinforcement learning in autonomic computing: A manifesto and case studies. IEEE Internet Comput. 11, 1 (2007), 22--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. R. Li et al. 2018. Deep reinforcement learning for resource management in network slicing. IEEE Access 6 (2018), 74429--74441.Google ScholarGoogle ScholarCross RefCross Ref
  38. I. Stojmenovic and S. Wen. 2014. The fog computing paradigm: Scenarios and security issues. In Proceedings of the IEEE Federated Conference on Computer Science and Information Systems (FedCSIS’14).Google ScholarGoogle Scholar
  39. S. Yi, C. Li, and Q. Li. 2015. A survey of fog computing: Concepts, applications and issues. In Proceedings of the ACM Workshop on Mobile Big Data. 37--42.Google ScholarGoogle Scholar
  40. M. A. Hassan, M. Xiao, Q. Wei, and S. Chen. 2015. Help your mobile applications with fog computing. In Proceedings of the Fog Networking for 5G and IoT Workshop.Google ScholarGoogle Scholar
  41. Ivan Stojmenovic, Sheng Wen, Xinyi Huang, and Hao Luan. 2016. An overview of fog computing and its security issues. Concurr. Comput.: Pract. Exper. 28, 10 (2016), 2751--3020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. C. Dsouza, G. J. Ahn, and M. Taguinod. 2014. Policy-driven security management for fog computing: Preliminary framework and a case study. In Proceedings of the IEEE 15th International Conference on Information Reuse and Integration (IRI’14). 16--23.Google ScholarGoogle Scholar
  43. C. C. Byers. 2017. Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled IoT networks. IEEE Commun. Mag. 55, 8 (2017), 14--20.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. D. Georgakopoulos et al. 2016. Internet of things and edge cloud computing roadmap for manufacturing. IEEE Cloud Comput. 3, 4 (2016), 66--73.Google ScholarGoogle ScholarCross RefCross Ref
  45. P. Patel, M. I. Ali, and A. Sheth. 2017. On using the intelligent edge for IoT analytics. IEEE Intell. Syst. 32, 5 (2017), 64--69.Google ScholarGoogle ScholarCross RefCross Ref
  46. T. Suganuma et al. 2018. Multiagent-based flexible edge computing architecture for IoT. IEEE Netw. 32, 1 (2018), 16--23.Google ScholarGoogle ScholarCross RefCross Ref
  47. P. Mach and Z. Becvar. 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surv. Tut. 19, 3 (2017), 1628--1656.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah. 2016. Unmanned aerial vehicle with underlaid device-to-device communications: Performance and tradeoffs. IEEE Trans. Wirel. Commun. 15, 6 (2016), 3949--3963.Google ScholarGoogle ScholarCross RefCross Ref
  49. Y. Zeng, R. Zhang, and T. J. Lim. 2016. Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Commun. Mag. 54, 5 (2016), 36--42.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. F. Jiang and A. L. Swindlehurst. 2012. Optimization of UAV heading for the ground-to-air uplink. IEEE J. Select. Areas Commun. 30, 5 (2012), 993--1005.Google ScholarGoogle ScholarCross RefCross Ref
  51. Y. Zeng, R. Zhang, and T. J. Lim. 2016. Throughput maximization for UAV-enabled mobile relaying systems. IEEE Trans. Commun. 64, 12 (2016), 4983--4996.Google ScholarGoogle ScholarCross RefCross Ref
  52. V. V. Chetlur and H. S. Dhillon. 2017. Downlink coverage analysis for a finite 3-D wireless network of unmanned aerial vehicles. IEEE Trans. Commun. 65, 10 (2017), 4543--4558.Google ScholarGoogle Scholar
  53. M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong. 2017. Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience. IEEE J. Select Areas Commun. 35, 5 (2017), 1046--1061.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah. 2017. Mobile unmanned aerial vehicles (UAVs) for energy-efficient internet of things communications. IEEE Trans. Wirel. Commun. 16, 11 (2017), 7574--7589.Google ScholarGoogle ScholarCross RefCross Ref
  55. I. Rubin and R. Zhang. 2007. Placement of UAVs as communication relays aiding mobile Ad Hoc wireless networks. In MILCOM 2007 - IEEE Military Communications Conference, Orlando, FL, USA, 2007. 1--7.Google ScholarGoogle ScholarCross RefCross Ref
  56. S. Rohde and C. Wietfeld. 2012. Interference aware positioning of aerial relays for cell overload and outage compensation. In Proceedings of the IEEE Vehicular Technology Conference. 1--5.Google ScholarGoogle Scholar
  57. Y. Wang, W. Peng, Q. Dou, and Z.-h. Gong. 2013. Energy-constrained ferry route design for sparse wireless sensor networks. J. Centr. South Univ. 20, 11 (2013), 3142--3149.Google ScholarGoogle ScholarCross RefCross Ref
  58. I. Jawhar, N. Mohamed, J. Al-Jaroodi, and S. Zhang. 2014. A framework for using unmanned aerial vehicles for data collection in linear wireless sensor networks. J. Intell. Robot. Syst. 74, 1 (2014), 437--453.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. S. Jeong, O. Simeone, and J. Kang. 2017. Mobile cloud computing with a UAV-mounted cloudlet: Optimal bit allocation for communication and computation. IET Commun. 11, 7 (2017), 969--974.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, and B. Koldehofe. 2013. Mobile fog: A programming model for large-scale applications on the internet of things. In Proceedings of the 2nd ACM SIGCOMM Workshop on Mobile Cloud Computing. 15--20.Google ScholarGoogle Scholar
  61. H. T. Dinh, C. Lee, D. Niyato, and P. Wang. 2013. A survey of mobile cloud computing: Architecture, applications, and approaches. Wirel. Commun. Mobile Comput. 13, 18 (2013), 1587--1611.Google ScholarGoogle ScholarCross RefCross Ref
  62. A. Ahmed and E. Ahmed. 2016. A survey on mobile edge computing. In Proceedings of the IEEE 10th International Conference on Intelligent Systems and Control (ISCO’16).Google ScholarGoogle Scholar
  63. H. Eom, P. S. Juste, R. J. O. Figueiredo, O. Tickoo, R. Illikkal, and R. Iyer. 2013. Machine learning-based runtime scheduler for mobile offloading framework. In Proceedings of the IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC’13). 17--25.Google ScholarGoogle Scholar
  64. H. Eom, R. J. O. Figueiredo, H. Cai, Y. Zhang, and G. Huang. 2015. MALMOS: Machine learning-based mobile offloading scheduler with online training. In Proceedings of the IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud’15). 51--60.Google ScholarGoogle Scholar
  65. D. Huang, P. Wang, and D. Niyato. 2012. A dynamic offloading algorithm for mobile computing. IEEE Trans. Wirel. Commun. 11, 6 (2012), 1991--1995.Google ScholarGoogle ScholarCross RefCross Ref
  66. R. Kobayashi and K. Adachi. 2019. Radio and computing resource allocation for minimizing total processing completion time in mobile edge computing. IEEE Access 7 (2019), 141119--141132.Google ScholarGoogle ScholarCross RefCross Ref
  67. G.-Y. Pan, J.-Y. Jou, and B.-C. Lai. 2014. Scalable power management using multilevel reinforcement learning for multiprocessors. ACM Trans. Des. Autom. Electron. Syst. 19, 4 (2014).Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Y. Wang and M. Pedram. 2016. Model-free reinforcement learning and Bayesian classification in system-level power management. IEEE Trans. Comput. 65, 12 (2016), 3713--3726.Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. G. Faraci, A. Raciti, S. A. Rizzo, G. Schembra. 2019. Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry. Elsevier Appl. Energy. DOI:https://doi.org/10.1016/j.apenergy.2019.114204Google ScholarGoogle Scholar
  70. Richard S. Sutton and Andrew G. Barto. 2012. Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA.Google ScholarGoogle Scholar
  71. O. Hashida, Y. Takahashi, and S. Shimogawa. 1991. Switched batch Bernoulli process (SBBP) and the discrete-time SBBP/G/1 queue with application to statistical multiplexer performance. IEEE J. Select. Areas Commun. 9, 3 (1991), 394--401.Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. A. Lombardo, G. Morabito, G. Schembra. 2004. Modeling intramedia and intermedia relationships in multimedia network analysis through multiple time-scale statistics. IEEE Trans. Multimedia 6, 1 (2004).Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Y. Zeng, J. Xu, and R. Zhang. 2019. Energy minimization for wireless communication with rotary-wing UAV. IEEE Trans. Wirel. Commun. 18, 4 (2019), 2329--2345.Google ScholarGoogle ScholarCross RefCross Ref
  74. J. C. Charr, R. Couturier, A. Fanfakh, and A. Giersch. 2014. Dynamic frequency scaling for energy consumption reduction in synchronous distributed applications. In 2014 IEEE International Symposium on Parallel and Distributed Processing with Applications, Milan, 2014. 225--230.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Fog in the Clouds: UAVs to Provide Edge Computing to IoT Devices

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 20, Issue 3
            SI: Evolution of IoT Networking Architectures papers
            August 2020
            259 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3408328
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            Copyright © 2020 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 26 August 2020
            • Online AM: 7 May 2020
            • Accepted: 1 February 2020
            • Revised: 1 December 2019
            • Received: 1 July 2019
            Published in toit Volume 20, Issue 3

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!