Skip to main content

Intelligent Mobile Messaging for Smart Cities Based on Reinforcement Learning

  • Chapter
  • First Online:
Handbook of Smart Cities

Abstract

Mobile messaging has become a trend in our daily lives, and is vital in supporting new services in smart cities. The current schema for messaging is to route all the messages between mobile users through a centralized server. This scheme, though reliable, creates very heavy load on the server. It is possible for users to communicate through peer-to-peer (P2P) connection, especially over urban networks characterized by heavy user traffic and dense network connectivity. P2P connections however do not provide the best user experience, as they are sometimes unreliable due to network coverage fluctuation. We propose an intelligent messaging framework based on reinforcement learning to strike a balance between reducing server load and improving user experience. The system learns and adapts in real-time to user mobility and messaging patterns. The adaptive system dynamically chooses between routing through the server and routing via P2P connection. As it does not rely on user location information, user privacy is thus preserved. Performance evaluation through simulation of user movement and messaging patterns demonstrates that the system is able to find the best messaging policy for users, achieves a well balance between heavy server load and unreliable communication, and provides a fine user messaging experience while reduces server load. We believe that this work is significant for future smart cities and urban networking where mobile messaging will be prominent among mobile users as well as mobile smart objects.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mary Meeker (2015). Internet Trends Report of 2015 [Online], Available: http://www.kpcb.com/internet-trends

  2. R. d. A. Oliveira; W. C. Brandão; H. T. Marques-Neto, “Characterizing User Behavior on a Mobile SMS-Based Chat Service”, XXXIII Brazilian Symposium on Computer Networks and Distributed Systems (SBRC), pp 130 – 139, 2015.

    Google Scholar 

  3. P.-L. To, C. Liao, J. C. Chiang, M.-L. Shih and C.-Y. Chang, “An empirical investigation of the factors affecting the adoption of Instant Messaging in organizations,” Original Research Article Computer Standards & Interfaces, vol. 30, no. 3, p. 148–156, 2008.

    Article  Google Scholar 

  4. O. O. Abiona1; A. I. Oluwaranti; T. Anjali; C. E. Onime; E. O. Popoola; G. A., “Architectural model for Wireless Peer-to-Peer (WP2P) file sharing for ubiquitous mobile devices”, IEEE International Conference on Electro/Information Technology, pp. 35-39, 2009.

    Google Scholar 

  5. Noor Musmayati Musa; Fauziah Redzuan, “Understanding user behavior towards mobile messaging application use in support for banking system”, 3rd International Conference on User Science and Engineering (i-USEr), pp. 269-274, 2014.

    Google Scholar 

  6. J. Maenpaa; V. Andersson; G. Camarillo; A. Keranen, “Impact of Network Address Translator Traversal on Delays in Peer-to-Peer Session Initiation Protocol”, pp 1-6, 2010.

    Google Scholar 

  7. I. F. Akyildiz; Wenye Wang, “The predictive user mobility profile framework for wireless multimedia networks”, IEEE/ACM Transactions on Networking, pp 1021 – 1035, 2004.

    Article  Google Scholar 

  8. D. Barth; S. Bellahsene; L. Kloul, “Mobility Prediction Using Mobile User Profiles”, IEEE 19th International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp 286 – 294, 2011.

    Google Scholar 

  9. S. Khokhar; A. A. Nilsson, “Estimation of Mobile Trajectory in a Wireless Network: A Basis for User's Mobility Profiling for Mobile Trajectory Based Services”, Third International Conference on Sensor Technologies and Applications, pp. 69-74, 2009.

    Google Scholar 

  10. M. A. Bayir; M. Demirbas; N. Eagle, “Discovering spatiotemporal mobility profiles of cellphone users”, IEEE Int. Sym. on a World of Wireless, Mobile and Multimedia Networks, pp. 1-9, 2009.

    Google Scholar 

  11. G. Gupta; R. Garg, “Minimizing the cost of mobility management: distance-based scheme as a function of user's profile”, Wireless Communications and Networking, pp. 2075 – 2080, vol. 3, 2003.

    Google Scholar 

  12. T. Deng; X. Wang; P. Fan; K. Li, “Modeling and Performance Analysis of Tracking Area List-Based Location Management Scheme in LTE Networks”, IEEE Transactions on Vehicular Technology, 2015.

    Google Scholar 

  13. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, 1998.

    Google Scholar 

  14. J. Rosenberg, J. Weinberger-Dynamicsoft, C. Huitema-Microsoft, R. Mahy-Cisco, “STUN-Simple Traversal of User Datagram Protocol Through Network Address Translators,” RFC-3489, 2003.

    Google Scholar 

  15. Ha Tran Thi Thu; Jaehyung Park; Yonggwan Won; Jinsul Kim, “Combining STUN Protocol and UDP Hole Punching Technique for Peer-To-Peer Communication across Network Address Translation”, pp, 1 – 4, 2014.

    Google Scholar 

  16. Junnosuke Kuroda; Yasuichi Nakayama, “STUN-based connection sequence through symmetric NATs for TCP connection”, Network Operations and Management Symposium (APNOMS), pp. 1-4, 2011.

    Google Scholar 

  17. Yong Wang; Zhao Lu; Junzhong Gu, “Research on Symmetric NAT Traversal in P2P applications”, International Multi-Conference on Computing in the Global Information Technology, 2006.

    Google Scholar 

  18. K. S. Hwang; Y. J. Chen; C. J. Wu, “Fusion of Multiple Behaviors Using Layered Reinforcement Learning”, IEEE Trans. on Systems, Man, and Cybernetics - Part A: Systems and Humans, pp. 999 – 1004, vol. 42, 2012.

    Article  Google Scholar 

  19. X. Xu; C. Liu; S. X. Yang; D. Hu, “Hierarchical Approximate Policy Iteration with Binary-Tree State Space Decomposition”, IEEE Transactions on Neural Networks, pp. 1863 – 1877, vol. 22, 2011.

    Article  Google Scholar 

  20. K. S. Hwang; T. W. Yang; C. J. Lin, “Self Organizing Decision Tree Based on Reinforcement Learning and its Application on State Space Partition”, IEEE International Conference on Systems, Man and Cybernetics, pp. 5088 - 5093, vol. 6, 2006.

    Google Scholar 

  21. Min Wu; A. Yamashita; H. Asama, “Rule abstraction and transfer in reinforcement learning by decision tree”, IEEE/SICE International Symposium on System Integration (SII), pp. 529 – 534, 2012.

    Google Scholar 

  22. K. S. Hwang; Y. J. Chen, “Tree-like Function Approximator in Reinforcement Learning”, 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 904 – 907, 2007.

    Google Scholar 

  23. P. Boone; M. Barbeau; E. Kranakis, “Using time-of-day and location-based mobility profiles to improve scanning during handovers”, IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, pp. 1-6, 2010.

    Google Scholar 

  24. A. Sokolovsky; S. I. Bross, “Attainable error exponents for the Poisson broadcast channel with degraded message sets”, IEEE Transactions on Information Theory, vol. 51, pp. 364-374, 2005.

    Article  MathSciNet  Google Scholar 

  25. Le Tien Dung, T. Komeda ; M. Takagi, “Mixed Reinforcement Learning for Partially Observable Markov Decision Process”, International Symposium on Computational Intelligence in Robotics and Automation, pp. 7-12, 2007.

    Google Scholar 

  26. L. Li; A. Scaglione, “Learning hidden Markov sparse models”, Information Theory and Applications Workshop (ITA), pp. 1-13, 2013.

    Google Scholar 

  27. O. H. Hamid; F. H. Alaiwy; I. O. Hussien, “Uncovering cognitive influences on individualized learning using a hidden Markov models framework”, Global Summit on Computer & Information Technology (GSCIT), pp. 1-6, 2015.

    Google Scholar 

  28. Yanwen Wang, Hainan Chen, Xiaoling Wu, Lei Shu, “An energy-efficient SDN based sleep scheduling algorithm for WSNs”, Journal of Network and Computer Applications, pp. 39-45, 2016.

    Google Scholar 

  29. Dan Wu; Jinlong Wang; Rose Qingyang Hu; Yueming Cai; Liang Zhou, “Energy-Efficient Resource Sharing for Mobile Device-to-Device Multimedia Communications”, IEEE Transactions on Vehicular Technology, vol. 63, no. 5, pp. 2093-2103, 2014.

    Article  Google Scholar 

  30. Mohammad Ashraful Hoque; Matti Siekkinen; Jukka K. Nurminen, “Energy Efficient Multimedia Streaming to Mobile Devices — A Survey”, IEEE Communications Surveys & Tutorials, vol. 16, 2014.

    Google Scholar 

  31. Jin Zhao; B. K. Bose, “Evaluation of membership functions for fuzzy logic controlled induction motor drive”, IEEE 28th Annual Conference, vol. 1, pp. 229-234, 2002.

    Google Scholar 

  32. M. Moh, B. Chellappan, T.-S. Moh, and S. Venugopal, “Handoff mechanisms for IEEE 802.16 networks supporting intelligent transportation systems,” in Wireless Technologies for Intelligent Transportation Systems, edited by Ming-Tuo Zhou, Yang Zhang, and Lawrence Yang, published by Nova Science Pub., 2010.

    Google Scholar 

  33. R. Wong, T.-S. Moh, and M. Moh, “Semi-Supervised Learning BitTorrent Traffic Detection,” in Distributed Network Intelligence, Security and Applications, ed. by Qurban A. Memon, CRC Press - Taylor & Francis Group, USA, Apr 2013.

    Google Scholar 

  34. Behrooz Shahriari; Melody Moh; Teng-Sheng Moh, “Generic Online Learning for Partial Visible Dynamic Environment with Delayed Feedback: Online Learning for 5G C-RAN Load-Balancer”, International Conference on High Performance Computing & Simulation (HPCS), pp. 176-185, 2017.

    Google Scholar 

  35. Chia-Feng Juang, and Chia-Hung Hsu, “Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control”, IEEE Transactions on Industrial Electronics, Vol. 56, NO. 10. Oct. 2009.

    Google Scholar 

  36. United Nations Secretary-General’s high-level panel on global sustainability, “Resilient People, Resilient Planet: A future worth choosing,” 2012.

    Google Scholar 

  37. B. Shahriari and M. Moh, “Intelligent Mobile Messaging for Urban Networks – Adaptive Intelligent Messaging Based on Reinforcement Learning,” Proceedings of 12th IEEE Int. Conf. on Wireless and Mobile Computing, Networking and Communications (WiMob), New York, October 17-19, 2016.

    Google Scholar 

  38. C. Tsai and M. Moh, “Load Balancing in 5G Cloud Radio Access Networks Supporting IoT Communications for Smart Communities,” Proceedings of 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, Dec 2017.

    Google Scholar 

  39. Badis Hammi, Rida Khatoun, Sherali Zeadally, “IoT technologies for smart cities”, IEEE IET Networks, Vol. 7, 2017.

    Google Scholar 

  40. Walid Balid, Hazem H Refai, “On the development of self-powered iot sensor for real-time traffic monitoring in smart cities”, IEEE SENSORS, 2017.

    Google Scholar 

  41. Jay Lohokare, Reshul Dani, Ajit Rajurkar, Ameya Apte, “An IoT ecosystem for the implementation of scalable wireless home automation systems at smart city level”, IEEE Region 10 Conference, TENCON, 2017.

    Google Scholar 

  42. Su, Gary, Melody Moh. “Improving Energy Efficiency and Scalability for IoT Communications in 5G Networks.” Proc. of 12th ACM Int. Conf. on Ubiquitous Information Management and Communication (IMCOM), Langkawi, Malaysia, January 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Melody Moh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shahriari, B., Moh, M. (2018). Intelligent Mobile Messaging for Smart Cities Based on Reinforcement Learning. In: Maheswaran, M., Badidi, E. (eds) Handbook of Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-319-97271-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97271-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97270-1

  • Online ISBN: 978-3-319-97271-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics