Smart devices such as mobile phones, sensors, cameras, etc., are producing a huge amount of data every second. This data is normally sent to cloud servers for processing. This approach is normally slow and expensive, and it also raises a lot of privacy issues as data is sent over the internet, which takes a lot of time to return with results.

This study aims to address the issue through the integration of machine learning techniques and edge computing technology. Edge computing refers to a system that processes data closer to or on the actual device that is sending it, as opposed to sending it to a cloud server for processing. If this data is integrated with ML, it makes it possible for smart devices to make smart decisions without having to rely on cloud servers.

The main idea of this research is to develop a system that can efficiently run machine learning models in small devices that require low power. This means developing a machine learning model that requires low power and memory and using other technologies such as model compression and federated learning.

The main idea of this research will be implemented in different real-world scenarios, for example, in health monitoring systems, traffic systems, and factory machines. The performance of the system in terms of speed, accuracy, and efficiency in consuming power and ensuring the privacy of the data will be evaluated. The main idea of this research is to develop a fast and intelligent edge computing system that uses machine learning as its main technology. This could be useful in developing smarter applications that respond quickly without relying too much on the cloud and could be useful in the development of future technologies in healthcare, transportation, and other sectors.

Keywords: AI, Machine Learning, Edge Computing, Real Time Analytics, IoT.

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1. Bablu TA, Rashid MT. Edge computing and its impact on real-time data processing for IoT-driven applications. Journal of advanced computing systems. 2025 Jan 11;5(1):26-43.
2. Chen J, Yan H, Liu Z, Zhang M, Xiong H, Yu S. When federated learning meets privacy-preserving computation. ACM Computing Surveys. 2024 Oct 3;56(12):1-36.
3. Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY. Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal. 2020 Apr 1;7(8):7457-69.
4. Gold Futures Price History Chart - Investing.com IN. Investing.com India. 2026. Available from: https://in.investing.com/commodities/gold-historical-data
5. Kumar S, Pradhan AK, Tiwari AK, Kang SH. Correlations and volatility spillovers between oil, natural gas, and stock prices in India. Resources Policy. 2019 Aug 1;62:282-91.
6. Meneses JG, Burela JM, Rivera MD, Nájera MF. OIL PRICE ESTIMATION BASED ON CHANGES IN DOLLAR-DENOMINATED ETFS: A MULTI-INSTRUMENT REGRESSION APPROACH (2025-2026). International Journal of Education Studies and Social Science Research. 2026 Apr 3;1(2):52-61
7. Chen W, Qiu H, Zhuang J, Zhang C, Hu Y, Lu Q, Wang T, Shi Y, Huang M, Xu X. Quantization of deep neural networks for accurate edge computing. ACM Journal on Emerging Technologies in Computing Systems (JETC). 2021 Jun 30;17(4):1-1.
8. Robissout D, Bossuet L, Habrard A, Grosso V. Improving deep learning networks for profiled side-channel analysis using performance improvement techniques. ACM Journal on Emerging Technologies in Computing Systems (JETC). 2021 Jun 30;17(3):1-30.
9. Sakhdari J, Zolfaghari B, Izadpanah S, Mahdizadeh Zargar S, Rahati Quchani M, Shadi M, Abrishami S, Rasoolzadegan A. Edge computing: A systematic mapping study. Concurrency and Computation: Practice and Experience. 2023 Oct 10;35(22):e7741.
10. Pereira CV, de Oliveira EM, de Souza AD. Machine learning applied to edge computing and wearable devices for healthcare: Systematic mapping of the literature. Sensors. 2024 Sep 29;24(19):6322.
11. Shankar V. Edge AI: A comprehensive survey of technologies, applications, and challenges. In2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET) 2024 Aug 23 (pp. 1-6). IEEE.
12. Rodrigues TK, Kato N. Hybrid centralized and distributed learning for MEC-equipped satellite 6G networks. IEEE Journal on Selected Areas in Communications. 2023 Feb 10;41(4):1201-11.
13. Wang X, Tang Z, Guo J, Meng T, Wang C, Wang T, Jia W. Empowering edge intelligence: A comprehensive survey on on-device AI models. ACM Computing Surveys. 2025 Apr 4;57(9):1-39.Click or tap here to enter text.

Smart devices such as mobile phones, sensors, cameras, etc., are producing a huge amount of data every second. This data is normally sent to cloud servers for processing. This approach is normally slow and expensive, and it also raises a lot of privacy issues as data is sent over the internet, which takes a lot of time to return with results.

This study aims to address the issue through the integration of machine learning techniques and edge computing technology. Edge computing refers to a system that processes data closer to or on the actual device that is sending it, as opposed to sending it to a cloud server for processing. If this data is integrated with ML, it makes it possible for smart devices to make smart decisions without having to rely on cloud servers.

The main idea of this research is to develop a system that can efficiently run machine learning models in small devices that require low power. This means developing a machine learning model that requires low power and memory and using other technologies such as model compression and federated learning.

The main idea of this research will be implemented in different real-world scenarios, for example, in health monitoring systems, traffic systems, and factory machines. The performance of the system in terms of speed, accuracy, and efficiency in consuming power and ensuring the privacy of the data will be evaluated. The main idea of this research is to develop a fast and intelligent edge computing system that uses machine learning as its main technology. This could be useful in developing smarter applications that respond quickly without relying too much on the cloud and could be useful in the development of future technologies in healthcare, transportation, and other sectors.

Keywords: AI, Machine Learning, Edge Computing, Real Time Analytics, IoT.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Bablu TA, Rashid MT. Edge computing and its impact on real-time data processing for IoT-driven applications. Journal of advanced computing systems. 2025 Jan 11;5(1):26-43.
2. Chen J, Yan H, Liu Z, Zhang M, Xiong H, Yu S. When federated learning meets privacy-preserving computation. ACM Computing Surveys. 2024 Oct 3;56(12):1-36.
3. Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY. Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal. 2020 Apr 1;7(8):7457-69.
4. Gold Futures Price History Chart - Investing.com IN. Investing.com India. 2026. Available from: https://in.investing.com/commodities/gold-historical-data
5. Kumar S, Pradhan AK, Tiwari AK, Kang SH. Correlations and volatility spillovers between oil, natural gas, and stock prices in India. Resources Policy. 2019 Aug 1;62:282-91.
6. Meneses JG, Burela JM, Rivera MD, Nájera MF. OIL PRICE ESTIMATION BASED ON CHANGES IN DOLLAR-DENOMINATED ETFS: A MULTI-INSTRUMENT REGRESSION APPROACH (2025-2026). International Journal of Education Studies and Social Science Research. 2026 Apr 3;1(2):52-61
7. Chen W, Qiu H, Zhuang J, Zhang C, Hu Y, Lu Q, Wang T, Shi Y, Huang M, Xu X. Quantization of deep neural networks for accurate edge computing. ACM Journal on Emerging Technologies in Computing Systems (JETC). 2021 Jun 30;17(4):1-1.
8. Robissout D, Bossuet L, Habrard A, Grosso V. Improving deep learning networks for profiled side-channel analysis using performance improvement techniques. ACM Journal on Emerging Technologies in Computing Systems (JETC). 2021 Jun 30;17(3):1-30.
9. Sakhdari J, Zolfaghari B, Izadpanah S, Mahdizadeh Zargar S, Rahati Quchani M, Shadi M, Abrishami S, Rasoolzadegan A. Edge computing: A systematic mapping study. Concurrency and Computation: Practice and Experience. 2023 Oct 10;35(22):e7741.
10. Pereira CV, de Oliveira EM, de Souza AD. Machine learning applied to edge computing and wearable devices for healthcare: Systematic mapping of the literature. Sensors. 2024 Sep 29;24(19):6322.
11. Shankar V. Edge AI: A comprehensive survey of technologies, applications, and challenges. In2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET) 2024 Aug 23 (pp. 1-6). IEEE.
12. Rodrigues TK, Kato N. Hybrid centralized and distributed learning for MEC-equipped satellite 6G networks. IEEE Journal on Selected Areas in Communications. 2023 Feb 10;41(4):1201-11.
13. Wang X, Tang Z, Guo J, Meng T, Wang C, Wang T, Jia W. Empowering edge intelligence: A comprehensive survey on on-device AI models. ACM Computing Surveys. 2025 Apr 4;57(9):1-39.Click or tap here to enter text.