Research on Interference Elimination-Based Energy Consumption of the Internet of Things

Authors

  • Lianlian Song First Affiliated Hospital of Soochow University image/svg+xml
  • Chuanhong Song Weifang University of Science and Technology image/svg+xml
  • Yicheng Liu First Affiliated Hospital of Soochow University image/svg+xml

DOI:

https://doi.org/10.4108/eetsis.14111

Keywords:

Internet of Things, energy consumption, signal interference, sink node, terminal node

Abstract

INTRODUCTION: During the uninterrupted operation of the Internet of Things, the continuous activity of a large number of terminal devices not only increases the energy consumption of the nodes, but also causes signal interference between devices, resulting in degraded channel conditions and Quality of Service of the Internet of Things, increased data transmission latency, and a further increase in energy consumption of the Internet of Things.

OBJECTIVES: To mitigate the effects of communication conflict and signal interference on data transmission and reduce the energy consumption of the Internet of Things, an interference elimination-based energy consumption optimization algorithm for the Internet of Things is proposed.

METHODS: According to the model of the Internet of Things, this paper divides the nodes of the Internet of Things into sink nodes and terminal nodes. The sink node dynamically allocates data transmission time-slots based on the Signal-to-Interference plus Noise Ratios of the terminal nodes. Meanwhile, the terminal node adaptively adjusts its power on the basis of its activity status while guaranteeing data transmission performance.

RESULTS: The simulation results showed that the data transmission latency was reduced, signal interference mitigated, channel environments improved, and energy consumption of the Internet of Things lowered.

CONCLUSION: The proposed algorithm can effectively reduce the impact of channel interference on the data transmission of terminal nodes, improve data transmission performance and Quality of Service of the Internet of Things, and reduce energy consumption of the Internet of Things.

References

[1] Chen WP, Chen MY, Lu LL. The application of IOT technology in smart hospital construction. Chin J Health Inform Manag. 2020;17(6):710-714,774. doi:10.3969/j.issn.1672-5166.2020.06.04.

[2] Gourisaria MK, Agrawal R, Singh V, et al. AI and IoT enabled smart hospital management systems. In: Data Science in Societal Applications. Springer Nature Singapore Pte Ltd; 2022:77-106. doi:10.1007/978-981-19-5154-1.

[3] Mamun-Ibn-Abdullah M, Kabir MH. A healthcare system for Internet of Things (IoT) application: machine learning based approach. J Comput Commun. 2021;8(9):21-30. doi:10.4236/jcc.2021.97003.

[4] Wang ZC, Feng JY. Application of digital health system based on Internet of Things in China. China Med Devices. 2022;37(1):174-179. doi:10.3969/j.issn.1674-1633.2022.01.044.

[5] Duan WP. Research on smart medical system and its practical application based on Internet of Things technology. Mod Inf Technol. 2022;6(3):174-176,180. doi:10.19850/j.cnki.2096-4706.2022.03.046.

[6] Wang TT, Zhou ZH, Wang JX, et al. Exploration of intelligent management mode of hospital blood inventory based on radio frequency identification technology. Chin Health Qual Manag. 2023;30(4):4-6,10. doi:10.13912/j.cnki.chqm.2023.30.4.02.

[7] Liu Y, Deng G. Automating inventorying of blood stations: a system based on ultrahigh-frequency radio-frequency identification (UHF RFID) technology. Transfus Clin Biol. 2022;29(2):134-137. doi:10.1016/j.tracli.2021.12.003.

[8] Guo HK, Ren YH, Ma JW, et al. Intravenous infusion monitoring management system based on IoT. Digit Commun World. 2021;16(3):25-26,58. doi:10.3969/j.issn.1672-7274.2021.03.009.

[9] Zhao LP. Design of intelligent infusion monitoring and management system based on RFID technology. China Plant Eng. 2024;39(3):41-43. doi:10.3969/j.issn.1671-0711.2024.03.019.

[10] Ma L, Deng SB, He M. Unmanned infusion monitoring system based on the Internet of Things. Internet Things Technol. 2022;11(3):38-41. doi:10.16667/j.issn.2095-1302.2022.03.011.

[11] Oliver N, Toumazou C, Cass A, et al. Glucose sensors: a review of current and emerging technology. Diabet Med. 2009;26(3):197-210. doi:10.1111/j.1464-5491.2008.02642.

[12] Wang G, Mintchev MP. Development of wearable semi-invasive blood sampling devices for continuous glucose monitoring: a survey. In: Proceedings of 2013 International Conference on Biomedical Engineering; 2013:42-46. doi:10.4236/eng.2013.55B009.

[13] Kuo YW, Tsao YC, Chien WC, et al. Smart health monitoring and management system for organizations using radio-frequency identification (RFID) technology in hospitals or emergency applications. Emerg Med Int. 2022;2022:2177548. doi:10.1155/2022/2177548.

[14] Luong NC, Hoang DT, Wang P, et al. Data collection and wireless communication in internet of Things (IoT) using economic analysis and pricing models: a survey. IEEE Commun Surv Tutor. 2016;18(4):2546-2590.

[15] Sun JL. Optimization and energy management of intelligent sensor networks based on the Internet of Things. In: Proceedings of 2024 Conference on Data Technology and Applications in Shandong Province, China; 2024.

[16] Çalıs Uslu B, Okay E, Dursun E. Analysis of factors affecting IoT-based smart hospital design. J Cloud Comput. 2020;9:67. doi:10.1186/s13677-020-00215-5.

[17] Zhou XJ. Design and application of smart power supply management system based on Internet of Things. J Changchun Univ. 2021;31(2):27-32. doi:10.3969/j.issn.1009-3907.2021.02.006.

[18] Chen JX. Research on key technologies and circuit design of ultra-low-power on-chip power management for self-powered Internet-of-Things node [dissertation]. Zhejiang: Zhejiang University; 2019.

[19] Liu K, Zhong YY, Chen J, et al. Data compression about Internet of Things based on HNBJSON. J Nanjing Univ Posts Telecommun Nat Sci Ed. 2021;41(6):29-34. doi:10.14132/j.cnki.1673-5439.2021.06.005.

[20] Zhao W. Research on data compression and anomaly detection methods for heterogeneous device access in Internet of Things [dissertation]. Zhejiang: Hangzhou Dianzi University; 2023.

[21] Tong S, Wang JL. Progress and challenges of LoRa low power wide area networks. Acta Electron Sin. 2024;52(10):3623-3642. doi:10.12263/DZXB.20240471.

[22] Malathy S, Porkodi V, Sampathkumar A, et al. An optimal network coding based backpressure routing approach for massive IoT network. Wirel Netw. 2020;26(5):3657-3674.

[23] Mousavi SM, Khademzadeh A, Rahmani AM. The role of low-power wide-area network technologies in Internet of Things: a systematic and comprehensive review. Int J Commun Syst. 2022;35(3). doi:10.1002/dac.5036.

[24] Niu YY, Wei ZQ, Wang L, et al. Interference management for integrated sensing and communication systems: a survey. IEEE Internet Things J. 2024;12(7):8110-8134. doi:10.1109/JIOT.2024.3506162.

[25] Grimaldi S, Mahmood A, Gidlund M. Real-time interference identification via supervised learning: embedding coexistence awareness in IoT devices. IEEE Access. 2018;7:835-850. doi:10.1109/ACCESS.2018.2885893.

[26] Bahashwan AA, Anbar M, Abdullah N, et al. Review on common IoT communication technologies for both long-range network (LPWAN) and short-range network. In: Advances on Smart and Soft Computing. Springer; 2021:341-353.

[27] Gu B, Li D, Ding H. Breaking the interference and fading gridlock in backscatter communications: state-of-the-art, design challenges, and future directions. IEEE Commun Surv Tutor. 2025;27(2):870-911. doi:10.1109/COMST.2024.3436082.

[28] Tian X, Yu J, Ma L, et al. Distributed deterministic broadcasting algorithms under the SINR model. In: Proceedings of the IEEE INFOCOM. San Francisco, USA; 2016:1-9.

[29] Wang Y. Study of propagation characteristics for indoor short-range wireless channel [dissertation]. Jiangsu: Nanjing University of Posts and Telecommunications; 2013.

[30] Imani AH, Eslami M, Haghighat J, et al. Effect of fading on the k-coverage of wireless sensor networks. Trans Emerg Telecommun Technol. 2020;31(7). doi:10.1002/ett.3994.

[31] Said O. Design and performance evaluation of QoE/QoS-oriented scheme for reliable data transmission in Internet of Things environments. Comput Commun. 2022;189:158-174. doi:10.1016/j.comcom.2022.03.020.

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Published

28-07-2026

How to Cite

1.
Song L, Song C, Liu Y. Research on Interference Elimination-Based Energy Consumption of the Internet of Things. EAI Endorsed Scal Inf Syst [Internet]. 2026 Jul. 28 [cited 2026 Jul. 28];13(1). Available from: https://publications.eai.eu/index.php/sis/article/view/14111