A Proposed Hidden Markov Model Method for Dynamic Device Pairing on Internet of Things End-Devices


  • Aji Gautama Putrada Telkom University
  • Nur Ghaniaviyanto Ramadhan Telkom University




Dynamic device pairing is a context-based zero-interaction method to pair end-devices in an IoT System based on Received Signal Strength Indicator (RSSI) values. But if RSSI detection is done in high level, the accuracy is troublesome due to poor sampling rates. This research proposes the Hidden Markov Model method to increase the performance of dynamic device pairing detection. This research implements an IoT system consisting an Access Point, an IoT End Device, an IoT Platform, and an IoT application and performs a comparison of two different methods to prove the concept. The results show that the precision of dynamic device pairing with HMM is better than without HMM and the value is 83,93%.

Author Biographies

Aji Gautama Putrada, Telkom University


Nur Ghaniaviyanto Ramadhan, Telkom University



Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F. & Alonso-Zarate, J., A Survey on Application Layer Protocols for the Internet of Things, Trans. IoT Cloud Comput., 3(1), pp. 11-17, 2015.

Sarkar, C., Nambi, S.A.U., Prasad, R.V. & Rahim, A., A Scalable Distributed Architecture Towards Unifying IoT Applications, in 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 508-513, 2014.

Ruiz, C., Pan, S., Sadde, A., Noh, H.Y. & Zhang, P., Posepair: Pairing IoT Devices Through Visual Human Pose Analysis: Demo Abstract, in Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 144-145, 2018.

Mei, S., Liu, Z., Zeng, Y., Yang, L. & Ma, J.F., Listen! Audio-based Smart IoT Device Pairing Protocol, in IEEE 19th International Conference on Communication Technology (ICCT), pp. 391-397, 2019.

Yu, N., Ma, J., Jin, X., Wang, J. & Chen, K., Context-Aware Continuous Authentication and Dynamic Device Pairing for Enterprise IoT, in International Conference on Internet of Things, pp. 114-122, 2019.

Lee, G.M. & Kim, J.Y., The Internet of Things – A Problem Statement, in International Conference on Information and Communication Technology Convergence (ICTC), pp. 517-518, 2010.

Wu, R.H., Lee, Y.H., Tseng, H.W., Jan, Y.G. & Chuang, M.H., Study of Characteristics of RSSI Signal, in IEEE International Conference on Industrial Technology, pp. 1-3, 2008. DOI: 10.1109/ICIT.2008.4608603.

Sultana, A., Hamou-Lhadj, A. & Couture, M., An Improved Hidden Markov Model for Anomaly Detection Using Frequent Common Patterns, in IEEE International Conference on Communications (ICC), pp. 1113-1117, 2012.

Putrada, A.G., Ramadhan, N.G. & Abdurohman, M., Context-aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model, Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, 5(1), pp. 37-44, 2020.

Zhao, C., Yang, S., Yang, X. & McCann, J.A., Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing, IEEE Trans. Mob. Comput., 16(7), pp. 2008-2022, 2016.

Zhang, J., Wang, Z., Yang, Z. & Zhang, Q., Proximity Based IoT Device Authentication, in IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1-9, 2017.

Pan, S., Universense: IoT Device Pairing Through Heterogeneous Sensing Signals, in Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications, pp. 55-60, 2018.

Han, J., Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types, in IEEE Symposium on Security and Privacy (SP), pp. 836-852, 2018.

Sun, X., Yang, Q., Liu, S. & Yuan, X., Improving Low-Resource Speech Recognition Based on Improved NN-HMM Structures, IEEE Access, 8, pp. 73005-73014, 2020.

Marti, U.V. & Bunke, H., Using A Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System, in Hidden Markov Models: Applications in Computer Vision, World Scientific, pp. 65-90, 2001.

Tornay, S., Aran, O. & Doss, M.M., An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition, in Proceedings of the 12th Language Resources and Evaluation Conference, pp. 6049-6056, 2020.

Liu, F. & Zheng, L., RFID Data Filtering Algorithm in Supply Chain, in 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2237-2240, 2012. DOI: 10.1109/FSKD.2012.6234246.

Bento, A.C., IoT: NodeMCU 12e X Arduino Uno, Results of an Experimental and Comparative Survey, Int. J., 6(1), pp. 46-56, 2018.

Townsend, J.T., Theoretical Analysis of an Alphabetic Confusion Matrix, Percept. Psychophys., 9(1), pp. 40-50, 1971.