Summary of An Energy-efficient Ensemble Approach For Mitigating Data Incompleteness in Iot Applications, by Yousef Alshehri and Lakshmish Ramaswamy
An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications
by Yousef AlShehri, Lakshmish Ramaswamy
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine Learning (ML) is becoming increasingly important for IoT-based applications, but the dynamic nature of many IoT ecosystems poses unique challenges to ML algorithms. One such challenge is data incompleteness, caused by missing sensor readings due to factors like sensor failures and network disruption. To build robust and energy-efficient ML systems, this paper presents an empirical study of SECOE, a technique for alleviating data incompleteness in IoT. The authors propose ENAMLE, a proactive, energy-aware technique that builds an ensemble of sub-models trained with subsets of sensors chosen based on their correlations. At inference time, ENAMLE adaptively adjusts the number of models based on missing data rates and energy-accuracy trade-offs. Experimental studies demonstrate ENAMLE’s energy efficiency and ability to alleviate sensor failures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ENAMLE is a new way to make machine learning work better with IoT devices. These devices often have limited power, which makes it hard for them to run complex algorithms. The authors want to find a way to use machine learning on these devices without using too much energy. They propose a new technique called ENAMLE that uses multiple small models instead of one big model. This helps reduce the amount of energy used and makes the algorithm more reliable when there are missing data points. |
Keywords
* Artificial intelligence * Inference * Machine learning
