Energy Consumption of Batch and Online Data Stream Learning Models for Smartphone-based Human Activity Recognition

Amezzane, Ilham; Berrazzouk, Amine; Fakhri, Yadolah; Aroussi, Mohamed El; Bakhouya, Mohamed

doi:10.1109/icocs.2019.8930763

Cited by 1 publication

(1 citation statement)

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“…In [18], the authors analyze power consumption for both batch and online data stream learning. They experimented with three online and three batch algorithms.…”

Section: Related Workmentioning

confidence: 99%

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

Cassales

Gomes

Bifet

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

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“…In [18], the authors analyze power consumption for both batch and online data stream learning. They experimented with three online and three batch algorithms.…”

Section: Related Workmentioning

confidence: 99%