Efficient Control of IoT Devices with Event-Driven Protocols
DOI:
https://doi.org/10.51094/jxiv.911Keywords:
IoT, Edge Computing, Cloud Computing, Resource Management, Digital TwinAbstract
Recently, IoT has been rapidly evolving and attracting attention as a means of connecting edge devices to networks to realize remote operation/monitoring and inter-device communication. In addition to this, edge AI technologies have been introduced, in which edge devices are equipped with AI, and the acquired data can be analyzed in real time on the devices. These technologies are being applied to various fields in society, and their impact is expanding in our daily lives by improving the efficiency of data collection and processing. On the other hand, the main challenges faced by these general-purpose devices are short battery life, difficulty of continuous power supply in some environments, and limited resources such as network bandwidth, processing power, and storage. In this study, we propose an event-driven protocol to solve these issues. The key technologies at the core of this protocol are (1) intermittent device control and state management assuming battery operation, (2) optimization to appropriately use edge processing and cloud processing, and (3) forwarding control to effectively utilize limited communication bandwidth. This paper describes the outline, concept, and design of the proposed protocol. As a proof of concept of the proposed method, we have operated an IoT device with the event-driven protocol in a scenario where the number of people in a room is detected. We measured the power consumption and processing time of the devices during system operation and compared the results. Based on these experimental results, we discussed the effectiveness and use cases of the proposed method, and summarized issues and future directions.
Conflicts of Interest Disclosure
There are no conflicts of interest to disclose.Downloads *Displays the aggregated results up to the previous day.
References
Masaharu AKATSU. Synthesis of knowledge for realizing society 5.0. Oukan (Journal of Transdisciplinary Fed- eration of Science and Technology), Vol. 14, No. 1, pp. 3–6, 2020.
Mohsen Attaran and Bilge Gokhan Celik. Digital twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, Vol. 6, p. 100165, 2023.
Hang Wang, Huansheng Ning, Yujia Lin, Wenxi Wang, Sahraoui Dhelim, Fadi Farha, Jianguo Ding, and Mahmoud Daneshmand. A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges. IEEE Internet of Things Journal, Vol. 10, No. 16, pp. 14671–14688, 2023.
Kwok Tai Chui, Brij B. Gupta, Jiaqi Liu, Varsha Arya, Nadia Nedjah, Ammar Almomani, and Priyanka Chaurasia. A survey of internet of things and cyber- physical systems: Standards, algorithms, applications, security, challenges, and future directions. Information, No. 7, 2023.
S. Narasimha Swamy and Solomon Raju Kota. An empirical study on system level aspects of internet of things (iot). IEEE Access, Vol. 8, pp. 188082–188134, 2020.
Partemie-Marian Mutescu, Adrian Ioan Petrariu, and Alexandru Lavric. Wireless communications for iot: Energy efficiency survey. In 2021 12th International Sym- posium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–4, 2021.
Hao Chen, Hua Qin, Weimin Chen, Ni Li, Tao Wang, Jianxin He, Gelan Yang, and Yang Peng. Bms: Bandwidth-aware multi-interface scheduling for energy-efficient and delay-constrained gateway-to-device com- munications in iot. Computer Networks, Vol. 225, p. 109645, 2023.
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, and M. Hadi Amini. A survey on federated learning for resource-constrained iot devices. IEEE Internet of Things Journal, Vol. 9, No. 1, pp. 1–24, 2022.
Guneet Kaur Walia, Mohit Kumar, and Sukhpal Singh Gill. Ai-empowered fog/edge resource management for iot applications: A comprehensive review, research challenges, and future perspectives. IEEE Communications Surveys Tutorials, Vol. 26, No. 1, pp. 619–669, 2024.
Matheus Araujo Gava, Helder Roberto Oliveira Rocha, Menno Jan Faber, Marcelo Eduardo Vieira Segatto, Heinrich W ̈ortche, and Jair Adriano Lima Silva. Opti- mizing resources and increasing the coverage of internet- of-things (iot) networks: An approach based on lorawan. Sensors, Vol. 23, No. 3, 2023.
Ke Wang. Migration strategy of cloud collaborative computing for delay-sensitive industrial iot applications in the context of intelligent manufacturing. Computer Communications, Vol. 150, pp. 413–420, 2020.
Hua Qin, Buwen Cao, Jianxin He, Xiang Xiao, Weihong Chen, and Yang Peng. Cross-interface scheduling toward energy-efficient device-to-gateway communications in iot. IEEE Internet of Things Journal, Vol. 7, No. 3, pp. 2247–2262, 2020.
Pethuru Raj, J. Akilandeswari, and M. Marimuthu. Chapter five - the edge ai paradigm: Technologies, plat- forms and use cases. In Pethuru Raj, Kavita Saini, and Chellammal Surianarayanan, editors, Edge/Fog Com- puting Paradigm: The Concept Platforms and Applications, Vol. 127 of Advances in Computers, pp. 139–182. Elsevier, 2022.
Hessel Stefan and Rebmann Andreas. Regulation of internet-of-things cybersecurity in europe and germany as exemplified by devices for children. International Cybersecurity Law Review, Vol. 1, , 2020.
Massimo Merenda, Carlo Porcaro, and Demetrio Iero. Edge machine learning for ai-enabled iot devices: A review. Sensors, Vol. 20, No. 9, 2020.
Md Eshrat E. Alahi, Arsanchai Sukkuea, Fah- mida Wazed Tina, Anindya Nag, Wattanapong Kurdthongmee, Korakot Suwannarat, and Subhas Chandra Mukhopadhyay. Integration of iot-enabled technologies and artificial intelligence (ai) for smart city scenario: Recent advancements and future trends. Sensors, Vol. 23, No. 11, 2023.
Maulshree Singh, Evert Fuenmayor, Eoin P. Hinchy, Yuansong Qiao, Niall Murray, and Declan Devine. Digital twin: Origin to future. Applied System Innovation, Vol. 4, No. 2, 2021.
Hansong Xu, Jun Wu, Qianqian Pan, Xinping Guan, and Mohsen Guizani. A survey on digital twin for industrial internet of things: Applications, technologies and tools. IEEE Communications Surveys Tutorials, Vol. 25, No. 4, pp. 2569–2598, 2023.
A. Chaurasia G. Jocher and J. Qiu. Yolo by ultralytics, 2023. Available online at: https://github.com/ultra lytics/ultralytics, last accessed on March 21, 2024.
Downloads
Posted
Submitted: 2024-09-24 03:20:07 UTC
Published: 2024-10-15 08:05:43 UTC
License
Copyright (c) 2024
Yusuke Sugizaki
Jin Nakazato
Manabu Tsukada
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
