IoT Protocols: Connecting Devices in Smart Environments

Authors

DOI:

https://doi.org/10.4108/eetsis.5665

Keywords:

Communication Efficiency, Energy Consumption, IoT Protocols, RVRR Protocol, Smart Environments

Abstract

The study delves into the implications of various IoT protocols on communication efficiency and energy consumption within smart environments. The RVRR (routing via respective reducer) protocol emerges as a standout performer, showcasing notable advantages over other conventional protocols. Specifically, the results demonstrate a substantial reduction in communication costs with RVRR, exhibiting improvements of 22.72%, 43.46%, and 49.04% when compared to ILP, SDN-Smart, and R-Drain, respectively.  excels in data transmission, achieving commendable reductions in Round-Trip Time (RTT) and enhancing overall energy efficiency. It registers an 18.80% decrease in energy consumption compared to ILP, 28.65% compared to SDN-Smart, and a significant 37% reduction when compared to R-Drain. This suggests that RVRR is adept at optimizing resource usage (routing via respective reducer )and minimizing energy consumption, crucial aspects in the context of IoT applications. The study reveals that RVRR contributes to an extended network lifespan, outperforming other protocols by substantial margins. It showcases a 19.45% improvement over ILP, 39.16% over SDN-Smart, and an impressive 54.60% over R-Drain. This underscores the sustainability and longevity benefits offered by RVRR (routing via respective reducer), making it a promising protocol for efficient and enduring IoT applications within smart environments.

References

Amadeo, M., Campolo, M., & Molinaro, A., A survey on the use of wireless sensor networks for industrial applications. IEEE Sensors Journal, 2020, 20(10), 5818-5839.

Balasubramanian, S., & Sivakumar, D. , A survey on IoT protocol stack for smart cities. In Proceedings of the International Conference on Electrical, Electronics, and Computer Science (ICEEECS),2019, 1-8.

Mahmood S. Mahmood, Najla B. AI-Dabagh, Improving IoT Security using Lightweight Based Deep Learning Protection Model, Tikrit Journal of Engineering Sciences, 2023, 30 (1), 119-129.

Boualem, M., & Bhuiyan, M. A. A. , A survey on industrial IoT security with blockchain technology. IEEE Consumer Electronics Magazine,2019, 8(4), 62-70.

MahbubaAfrin1, & Redowan Mahmud, Software Defined Network-based Scalable ResourceDiscovery for Internet of Things, EAI Endorsed Transactions on Scalable Information Systems, 2017, Volume4, Issue14.

NarayanDG, RashmiB., PavitraH, YashawardanD, A framework for Data Provenance Assurance in Cloud Environment using Ethereum Blockchain, EAI Endorsed Transactions on Scalable Information Systems, 2024, Volume 11, Issue 2.

Chen, M., & Hu, Y. C. , A survey of LPWAN technology for IoT-based smart cities. IEEE Access, 2018, 6, 7616-7632.

Cui, Y., Zhang, Y., Zhao, Y., & Li, Y.,A survey of network protocols for low-power wide-area networks in smart cities. Sensors, 2019, 19(1), 114.

Shen, Y., Zhang, T., Wang, Y., Wang, H., & Jiang, X. (2017). Microthings: A generic iot architecture for flexible data aggregation and scalable service cooperation. IEEE Communications Magazine, 55(9), 86-93.

Zhang, Y., Shen, Y., Wang, H., Yong, J., & Jiang, X. (2015). On secure wireless communications for IoT under eavesdropper collusion. IEEE Transactions on Automation Science and Engineering, 13(3), 1281-1293.

Wang, H., Zhang, Z., & Taleb, T. (2018). Special issue on security and privacy of IoT. World Wide Web, 21, 1-6.

Lakshmi, M. S., Kashyap, K. J., Khan, S. M. F., Reddy, N. J. S. V., & Achari, V. B. K. (2023). Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT. EAI Endorsed Transactions on Scalable Information Systems, 10(6).

Zhu, X., Hu, C., Lu, Y., Wang, Z., & Xue, H. (2023). Lightweight Cryptographic Simulation of Power IoT Fused with Bayesian Network Algorithms. EAI Endorsed Transactions on Scalable Information Systems, 10(4), e1-e1.

Li, S., Xu, L., & Zhao, S., 5G Internet of Things: A survey. IEEE Communications Surveys & Tutorials, 2018, 20(3), 2244-2253.

Mahmood, A., Ullah, F., & Shah, G., A survey of security challenges in smart cities. IEEE Communications Surveys & Tutorials, 2020, 22(2), 733-764.

Mtibaa, A., & Mahmoudi, H., A survey on machine learning for predictive maintenance in the IoT context. IEEE Access, 2019, 7, 16283-16303.

Raza, A., Kulkarni, P., & Somayaji, M. K. , Low-power wide-area networks (LPWANs): Towards sustainable IoT applications. IEEE Communications Magazine, 2019, 57(5), 70-77.

Kanellopoulos, Dimitris, et al. “Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives.” Electronics, vol. 12, no. 11, 31 May 2023, pp. 2490–2490, https://doi.org/10.3390/electronics12112490.

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Published

26-04-2024

How to Cite

1.
Hadi TH. IoT Protocols: Connecting Devices in Smart Environments. EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 26 [cited 2024 Jul. 19];11(6). Available from: https://publications.eai.eu/index.php/sis/article/view/5665