Advertisement

A Predictive Approach for Monitoring Services in the Internet of Things

  • Shubhi Asthana
  • Aly Megahed
  • Mohamed Mohamed
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

In Internet of Things (IoT) environments, devices offer monitoring services that would allow tenants to collect real-time data of different metrics through sensors. Values of monitored metrics can go above (or below) certain predefined thresholds, triggering the need to monitor these metrics at a higher or lower frequency since there are limited monitoring resources on the IoT devices. Also, such triggers might require additional metrics to be included or excluded from the monitoring service. An example for this is in a healthcare application, where if the blood pressure increases beyond a certain threshold, it might be necessary to start monitoring the heart beat at a higher frequency. Similarly, the change of the environmental context might necessitate the need to change/update the monitored metrics. For instance, in a smart car application, if an accident is observed on the monitored route, another route might need to be monitored. Whenever a trigger happens, there are optimization-based methods in the literature that calculate the optimal set of metrics to keep/start measuring and their frequencies. However, running these methods takes a considerable amount of time, making the approach, of waiting until the trigger happens and executing the optimization models, impractical. In this paper, we propose a novel system that predicts the next trigger to happen, run the optimization-based methods beforehand, and thus have the results ready before the triggers happen. The prediction is built as a tree structure of the state of the system followed with its predicted child nodes/states, and the children states of these children… etc. Whenever part of that predicted tree actually occurs, one can remove the calculations of the part that did not occur to save storage resources.

Keywords

Internet of Things (IoT) Monitoring services Resource constraints Optimization Predictive analytics 

References

  1. 1.
    Yang SH. Internet of things. Wireless Sens Netw. 2014: 247–61 (Springer).Google Scholar
  2. 2.
    Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst. 2013;29(7):1645–60.CrossRefGoogle Scholar
  3. 3.
    Megahed A, Tata S, Nazeem A. Cognitive determination of policies for data management in IoT systems. In: International conference on service-oriented computing. 2017: 188–97 (Springer).CrossRefGoogle Scholar
  4. 4.
    Mohamed M. Generic monitoring and reconfiguration for service-based applications in the cloud. Ph.D. thesis, Institut National des Telecommunications, 2014.Google Scholar
  5. 5.
    Gajananan K, Megahed A, Nakamura T, Abe M, Smith M. A top-down pricing algorithm for IT service contracts using lower level service data. In: Proceedings of the IEEE international conference on services computing (SCC), 2016: 720–7.Google Scholar
  6. 6.
    Megahed A, Gajananan K, Abe M, Jiang S, Smith M, NakamuraT. Pricing IT services deals: a more agile top-down approach. In: International conference on service-oriented computing. Berlin: Springer; 2015: 461–473.CrossRefGoogle Scholar
  7. 7.
    Gil D, Ferŕandez A, Mora-Mora H, Peral J. Internet of things: a review of surveys based on context aware intelligent services. Sensors 2016; 16(7): 1069.CrossRefGoogle Scholar
  8. 8.
    Issarny V, Bouloukakis G, Georgantas N, Billet B. Revisiting service-oriented architecture for the IoT: a middleware perspective. In: International conference on service-oriented computing, 2016: 3–17 (Springer).Google Scholar
  9. 9.
    Megahed A, Asthana S, Becker V, Nakamura T, Gajananan K. A method for selecting peer deals in IT service contracts. In: Proceedings of the IEEE international conference on artificial intelligence and mobile services (AIMS), 2017: 1–7.Google Scholar
  10. 10.
    Tata S, Mohamed M, Megahed A. An optimization approach for adaptive monitoring in IoT environments. In: 2017 IEEE international conference on services computing (SCC), 2017: 378–385. IEEE.Google Scholar
  11. 11.
    Megahed A, Pazour J, Nazeem A, Tata S, Mohamed M. Monitoring services in the internet of things: an optimization approach. Computing. 2018 (To Appear).Google Scholar
  12. 12.
    Megahed A, Yin P, Nezhad HRM. An optimization approach to services sales forecasting in a multi-staged sales pipeline. In: Proceedings of the IEEE international conference on services computing (SCC), 2016: 713–719.Google Scholar
  13. 13.
    Fukuda MA, Gajananan K, Jiang S, Megahed A, Nakamura T, Smith MA. Method and system for determining an optimized service package model for market participation. U.S. Patent Application 15/050,986, filed 24 Aug 2017.Google Scholar
  14. 14.
    Asthana S, Megahed A, Strong R. A recommendation system for proactive health monitoring using IoT and wearable technologies. In: 2017 IEEE International Conference on AI & Mobile Services (AIMS), 2017: 14–21. IEEE.Google Scholar
  15. 15.
    Asthana S, Strong R, Megahed A. Healthadvisor: recommendation system for wearable technologies enabling proactive health monitoring. arXiv:1612.00800, 2016.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IBM ResearchSan JoseUSA

Personalised recommendations