STEER: An Architecture to Support Self-adaptive IoT Networks for Indoor Monitoring Applications




Intent-Driven Networks, Software-Defined Networks, Internet of Things, Self-Adaptive Systems


 IoT infrastructures are becoming increasingly more difficult to manage. One of the main issues is the high volatility present in the infrastruture, which increasingly demands self-adaptive solutions. As a proposal to handle this challenge, this paper presents STEER (Sdn-based inTEnt drivEn iot netwoRks), a new approach for the dynamic adaptation of IoT networks for indoor monitoring applications, based on the unification of Intent-Driven Networks (IDN) and Software-Defined Networks (SDN). Particularly, we explore the ability of IDNs to dynamically interpret an application’s intent, using an IDN-based mediator attached to an SDN-controller to autonomously adapt the IoT network behavior at runtime, thus realizing the intent according to the current operating context of the network. We demonstrate the approach using a representative application scenario related to IoT indoor environment monitoring in the domain of indoor air quality monitoring. The experiments allowed us to validate the applicability of the approach and show the system-wide effect of dynamic adaptation to the current operating environment on improving performance according to the metric under consideration, in this case, the number of application-level messages exchanged in the network.


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Almutairi, A., Alsanad, A., and Alhelailah, H. (2019). Evaluation of the indoor air quality in governmental oversight supermarkets (co-ops) in kuwait. Applied Sciences, 9(22). DOI: 10.3390/app9224950.

Aschenbruck, N., Bauer, J., Bieling, J., Bothe, A., and Schwamborn, M. (2012). Selective and secure over-the-air programming for wireless sensor networks. In 2012 21st International Conference on Computer Communications and Networks (ICCCN), pages 1-6. IEEE. DOI: 10.1109/ICCCN.2012.6289278.

Auer, P., Cesa-Bianchi, N., and Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Mach. Learn., 47(2–3):235–256. DOI: 10.1023/A:1013689704352.

Azzara, A., Alessandrelli, D., Bocchino, S., Petracca, M., and Pagano, P. (2014). Pyot, a macroprogramming framework for the internet of things. In Proceedings of the 9th IEEE international symposium on industrial embedded systems (SIES 2014), pages 96-103. IEEE. DOI: 10.1109/SIES.2014.6871193.

Bera, S., Misra, S., and Vasilakos, A. V. (2017). Software-defined networking for internet of things: A survey. IEEE Internet of Things Journal, 4(6):1994-2008. DOI: 10.1109/JIOT.2017.2746186.

Blair, G. (2018). Complex distributed systems: The need for fresh perspectives. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pages 1410-1421. DOI: 10.1109/ICDCS.2018.00142.

Cerroni, W., Buratti, C., Cerboni, S., Davoli, G., Contoli, C., Foresta, F., Callegati, F., and Verdone, R. (2017). Intent-based management and orchestration of heterogeneous openflow/iot sdn domains. In 2017 IEEE Conference on Network Softwarization (NetSoft), pages 1-9. DOI: 10.1109/NETSOFT.2017.8004109.

Elkhatib, Y., Coulson, G., and Tyson, G. (2017). Charting an intent driven network. In 2017 13th International Conference on Network and Service Management (CNSM), pages 1-5. IEEE. DOI: 10.23919/CNSM.2017.8255981.

Eriksson, J., "Osterlind, F., Finne, N., Tsiftes, N., Dunkels, A., Voigt, T., Sauter, R., and Marrón, P. J. (2009). Cooja/mspsim: interoperability testing for wireless sensor networks. In Proceedings of the 2nd International Conference on Simulation Tools and Techniques, pages 1-7. DOI: 10.4108/ICST.SIMUTOOLS2009.5637.

Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., and Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys & Tutorials, 19(1):550-586. DOI: 10.1109/COMST.2016.2610578.

Fernández-Agüera, J., Dominguez-Amarillo, S., Fornaciari, M., and Orlandi, F. (2019). Tvocs and pm 2.5 in naturally ventilated homes: Three case studies in a mild climate. Sustainability, 11(22). DOI: 10.3390/su11226225.

Floris, A., Porcu, S., Girau, R., and Atzori, L. (2021). An iot-based smart building solution for indoor environment management and occupants prediction. Energies, 14(10). DOI: 10.3390/en14102959.

Galluccio, L., Milardo, S., Morabito, G., and Palazzo, S. (2015). Sdn-wise: Design, prototyping and experimentation of a stateful sdn solution for wireless sensor networks. In 2015 IEEE Conference on Computer Communications (INFOCOM), pages 513-521. IEEE. DOI: 10.1109/INFOCOM.2015.7218418.

Gardikis, G., Koutras, I., Mavroudis, G., Costicoglou, S., Xilouris, G., Sakkas, C., and Kourtis, A. (2016). An integrating framework for efficient nfv monitoring. In 2016 IEEE NetSoft Conference and Workshops (NetSoft), pages 1-5. DOI: 10.1109/NETSOFT.2016.7502431.

Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7):1645-1660. DOI: 10.1016/j.future.2013.01.010.

Huang, T., Yu, F. R., Zhang, C., Liu, J., Zhang, J., and Liu, Y. (2017). A survey on large-scale software defined networking (sdn) testbeds: Approaches and challenges. IEEE Communications Surveys & Tutorials, 19(2):891-917. DOI: 10.1109/COMST.2016.2630047.

Jacobs, A. S., Pfitscher, R. J., Ferreira, R. A., and Granville, L. Z. (2018). Refining network intents for self-driving networks. In Proceedings of the Afternoon Workshop on Self-Driving Networks, pages 15-21. DOI: 10.1145/3229584.3229590.

Júnior, J. C., da Cunha, D. C., and Ferraz, C. A. (2021). Integrating context awareness and sdn for a lightweight approach to adaptive networking. In Anais do XIII Simpósio Brasileiro de Computação Ubíqua e Pervasiva, pages 91-101. SBC. DOI: 10.5753/sbcup.2021.16007.

Junior, S., Riker, A., Silvestre, B., Moreira, W., Oliveira-Jr, A., and Borges, V. (2020). Dynasti—dynamic multiple rpl instances for multiple iot applications in smart city. Sensors, 20(11):3130. DOI: 10.3390/s20113130.

Kreutz, D., Ramos, F. M. V., Veríssimo, P. E., Rothenberg, C. E., Azodolmolky, S., and Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1):14-76. DOI: 10.1109/JPROC.2014.2371999.

Lange, S., Gebert, S., Zinner, T., Tran-Gia, P., Hock, D., Jarschel, M., and Hoffmann, M. (2015). Heuristic approaches to the controller placement problem in large scale sdn networks. IEEE Transactions on Network and Service Management, 12(1):4-17. DOI: 10.1109/TNSM.2015.2402432.

Madureira, J., Paciência, I., Rufo, J., Ramos, E., Barros, H., Teixeira, J. P., and de Oliveira Fernandes, E. (2015). Indoor air quality in schools and its relationship with children's respiratory symptoms. Atmospheric Environment, 118:145-156. DOI: 10.1016/j.atmosenv.2015.07.028.

Mai, T., Garg, S., Yao, H., Nie, J., Kaddoum, G., and Xiong, Z. (2021). In-network intelligence control: Toward a self-driving networking architecture. IEEE Network, 35(2):53-59. DOI: 10.1109/MNET.011.2000412.

Min, Z., Sun, H., Bao, S., Gokhale, A. S., and Gokhale, S. S. (2021). A self-adaptive load balancing approach for software-defined networks in iot. In 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pages 11-20. DOI: 10.1109/ACSOS52086.2021.00034.

Noor, J., Tseng, H.-Y., Garcia, L., and Srivastava, M. (2019). Ddflow: visualized declarative programming for heterogeneous iot networks. In Proceedings of the International Conference on Internet of Things Design and Implementation, pages 172-177. DOI: 10.1145/3302505.3310079.

Ontanón, S. (2017). Combinatorial multi-armed bandits for real-time strategy games. Journal of Artificial Intelligence Research, 58:665-702. DOI: 10.1613/jair.5398.

Pang, L., Yang, C., Chen, D., Song, Y., and Guizani, M. (2020). A survey on intent-driven networks. IEEE Access, 8:22862-22873. DOI: 10.1109/ACCESS.2020.2969208.

Rodrigues-Filho, R. and Porter, B. (2017). Defining emergent software using continuous self-assembly, perception, and learning. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 12(3):1-25. DOI: 10.1145/3092691.

Rodriguez-Zurrunero, R., Tirado-Andrés, F., and Araujo, A. (2018). Yetios: An adaptive operating system for wireless sensor networks. In 2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), pages 16-22. IEEE. DOI: 10.1109/LCNW.2018.8628500.

Shafi, N. B., Ali, K., and Hassanein, H. S. (2012). No-reboot and zero-flash over-the-air programming for wireless sensor networks. In 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pages 371-379. IEEE. DOI: 10.1109/SECON.2012.6275799.

Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1):22-32. DOI: 10.1109/JIOT.2014.2306328.




How to Cite

Cordeiro, B. M. O. S., Rodrigues Filho, R., Júnior, I. G. S., & Costa, F. M. (2023). STEER: An Architecture to Support Self-adaptive IoT Networks for Indoor Monitoring Applications. Journal of Internet Services and Applications, 14(1), 107–123.



Research article