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Optimizing the Age of Sensed Information in Cyber-Physical Systems

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Database Systems for Advanced Applications. DASFAA 2022 International Workshops (DASFAA 2022)

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Abstract

With the wide spread of IoT applications, the timeliness of sensed information becomes more and more important. Recently, the researchers proposed to use the Age of Information (AoI) to evaluate the timeliness of sensed data, and a series of algorithms have been proposed to optimize the AoI in IoT and cyber-physical systems. There algorithms are efficient for the systems with identical sensors. However, they are not very suitable for the systems containing different sensors since the different variations of data are not sufficiently considered by them. In order to evaluate the AoI of different sensors data more fairly, we propose to use the data queue length instead of time to denote it in this paper. Based on such new metric, the problem of minimizing the max AoI is provided. Finally, the optimized scheduling algorithm is given for solving the above problem. Extensive experimental results are carried out and show that the proposed algorithm has higher performance comparing with the baseline and existing works.

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References

  1. Humayed, A., Lin, J., Li, F., Luo, B.: Cyber-physical systems security-a survey. IEEE Internet Things J. 4(6), 1802–1831 (2017)

    Article  Google Scholar 

  2. Hussain, B., Du, Q., Sun, B., Han, Z.: Deep learning-based DDoS-attack detection for cyber-physical system over 5G network. IEEE Trans. Industr. Inf. 17(2), 860–870 (2020)

    Article  Google Scholar 

  3. Fang, Y., Lim, Y., Ooi, S.E., Zhou, C., Tan, Y.: Study of human thermal comfort for cyber-physical human centric system in smart homes. Sensors 20(2), 372 (2020)

    Article  Google Scholar 

  4. Kockemann, U., et al.: Open-source data collection and data sets for activity recognition in smart homes. Sensors 20(3), 879 (2020)

    Article  Google Scholar 

  5. Limbasiya, T., Das, D.: Searchcom: vehicular cloud-based secure and energy-efficient communication and searching system for smart transportation. In: Proceedings of the 21st International Conference on Distributed Computing and Networking, pp. 1–10 (2020)

    Google Scholar 

  6. Zichichi, M., Ferretti, S., D’Angelo, G.: Are distributed ledger technologies ready for smart transportation systems? arXiv preprint arXiv:2001.09018 (2020)

  7. Namani, S., Gonen, B.: Smart agriculture based on IoT and cloud computing. In: 2020 3rd International Conference on Information and Computer Technologies (ICICT), pp. 553–556. IEEE (2020)

    Google Scholar 

  8. Jin, X.B., Yang, N.X., Wang, X.Y., Bai, Y.T., Su, T.L., Kong, J.L.: Hybrid deep learning predictor for smart agriculture sensing based on empirical mode decomposition and gated recurrent unit group model. Sensors 20(5), 1334 (2020)

    Article  Google Scholar 

  9. Sun, M., Xu, X., Qin, X., Zhang, P.: AoI-energy-aware UAV-assisted data collection for IoT networks: a deep reinforcement learning method. IEEE Internet Things J. 8(24), 17275–17289 (2021)

    Article  Google Scholar 

  10. Garca, L., Parra, L., Jimenez, J.M., Lloret, J., Lorenz, P.: IoT-based smart irrigation systems: an overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors 20(4), 1042 (2020)

    Article  Google Scholar 

  11. Kaul, S., Gruteser, M., Rai, V., Kenney, J.: Minimizing age of information in vehicular networks. In: 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, pp. 350–358. IEEE (2011)

    Google Scholar 

  12. Kaul, S., Yates, R., Gruteser, M.: Real-time status: how often should one update? In: 2012 Proceedings IEEE INFOCOM, pp. 2731–2735. IEEE (2012)

    Google Scholar 

  13. Bacinoglu, B.T., Ceran, E.T., Uysal-Biyikoglu, E.: Age of information under energy replenishment constraints. In: 2015 Information Theory and Applications Workshop (ITA), pp. 25–31. IEEE (2015)

    Google Scholar 

  14. Bacinoglu, B.T., Uysal-Biyikoglu, E.: Scheduling status updates to minimize age of information with an energy harvesting sensor. In: 2017 IEEE International Symposium on Information Theory (ISIT), pp. 1122–1126. IEEE (2017)

    Google Scholar 

  15. Kadota, I., Sinha, A., Modiano, E.: Optimizing age of information in wireless networks with throughput constraints. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 1844–1852. IEEE (2018)

    Google Scholar 

  16. Champati, J.P., Al-Zubaidy, H., Gross, J.: On the distribution of AoI for the GI/GI/1/1 and GI/GI/1/2* systems: exact expressions and bounds. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 37–45. IEEE (2019)

    Google Scholar 

  17. Moltafet, M., Leinonen, M., Codreanu, M.: On the age of information in multi-source queueing models. IEEE Trans. Commun. 68(8), 5003–5017 (2020)

    Article  Google Scholar 

  18. Kam, C., Kompella, S., Nguyen, G.D., Ephremides, A.: Effect of message transmission path diversity on status age. IEEE Trans. Inf. Theory 62(3), 1360–1374 (2015)

    Article  MathSciNet  Google Scholar 

  19. Jiang, Z., Krishnamachari, B., Zheng, X., Zhou, S., Niu, Z.: Decentralized status update for age-of-information optimization in wireless multiaccess channels. In: ISIT, pp. 2276–2280 (2018)

    Google Scholar 

  20. Yates, R.D., Kaul, S.K.: Status updates over unreliable multiaccess channels. In: 2017 IEEE International Symposium on Information Theory (ISIT), pp. 331–335. IEEE (2017)

    Google Scholar 

  21. Najm, E., Telatar, E.: Status updates in a multi-stream M/G/1/1 preemptive queue. In IEEE INFOCOM 2018-IEEE Conference On Computer Communications Workshops (INFOCOM WKSHPS), pp. 124–129. IEEE (2018)

    Google Scholar 

  22. Moltafet, M., Leinonen, M., Codreanu, M.: An approximate expression for the average AoI in a multi-source M/G/1 queueing model. In: 2020 2nd 6G Wireless Summit (6G SUMMIT), pp. 1–5. IEEE (2020)

    Google Scholar 

  23. Li, C., Li, S., Hou, Y.T.: A general model for minimizing age of information at network edge. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 118–126. IEEE (2019)

    Google Scholar 

  24. Kadota, I., Sinha, A., Modiano, E.: Scheduling algorithms for optimizing age of information in wireless networks with throughput constraints. IEEE/ACM Trans. Netw. 27(4), 1359–1372 (2019)

    Article  Google Scholar 

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Acknowledgements

This work is partly supported by the National Key Research and Development Plan Project of China under Grant No. 2019YFE0125200, the National Key R &D Program of China under Grant No. 2021ZD0110900, the Programs for Science and Technology Development of Heilongjiang Province under Grant No. 2021ZXJ05A03, the National Natural Science Foundation of China under Grant No. 61972114, 62106061, the National Natural Science Foundation of Heilongjiang Province under Grant No. YQ2019F007, and the Key Science Technology Specific Projects of Heilongjiang Province under Grant No. 2019ZX14A01.

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Correspondence to Siyao Cheng .

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Li, Y., Cheng, S., Li, F., Liu, J., Wu, H. (2022). Optimizing the Age of Sensed Information in Cyber-Physical Systems. In: Rage, U.K., Goyal, V., Reddy, P.K. (eds) Database Systems for Advanced Applications. DASFAA 2022 International Workshops. DASFAA 2022. Lecture Notes in Computer Science, vol 13248. Springer, Cham. https://doi.org/10.1007/978-3-031-11217-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-11217-1_24

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