Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric
Intelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/1/19 |
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| author | Alber Oswaldo Montoya Benitez Álvaro Suárez Sarmiento Elsa María Macías López Jorge Herrera-Ramirez |
| author_facet | Alber Oswaldo Montoya Benitez Álvaro Suárez Sarmiento Elsa María Macías López Jorge Herrera-Ramirez |
| author_sort | Alber Oswaldo Montoya Benitez |
| collection | DOAJ |
| description | Intelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium batteries often power these devices. However, their energy consumption can be high due to the need to remain in continuous listening mode and the time it takes to search for and deliver responses from the Internet. This work proposes the implementation of a VA through Artificial Intelligence (AI) training and using cache memory to minimize response time and reduce energy consumption. First, the difference in energy consumption between VAs in active and passive states is experimentally verified. Subsequently, a communication architecture and a model representing the behavior of VAs are presented, from which a metric is developed to evaluate the energy consumption of these devices. The cache-enabled prototype shows a reduction in response time and energy expenditure (comparing the results of cloud-based VA and cache-based VA), several times lower according to the developed metric, demonstrating the effectiveness of the proposed system. This development could be a viable solution for areas with limited power sources, low coverage, and mobility situations that affect internet connectivity. |
| format | Article |
| id | doaj-art-4d69b39b10ac4ef6ad482e6278c7cd78 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-4d69b39b10ac4ef6ad482e6278c7cd782025-01-24T13:50:46ZengMDPI AGTechnologies2227-70802025-01-011311910.3390/technologies13010019Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a MetricAlber Oswaldo Montoya Benitez0Álvaro Suárez Sarmiento1Elsa María Macías López2Jorge Herrera-Ramirez3Faculty of Engineering, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaGrupo de Arquitectura y Concurrencia (GAC), University Institute of Cybernetics, Business, and Society, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainGrupo de Arquitectura y Concurrencia (GAC), University Institute of Cybernetics, Business, and Society, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainFaculty of Exact and Applied Sciences, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaIntelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium batteries often power these devices. However, their energy consumption can be high due to the need to remain in continuous listening mode and the time it takes to search for and deliver responses from the Internet. This work proposes the implementation of a VA through Artificial Intelligence (AI) training and using cache memory to minimize response time and reduce energy consumption. First, the difference in energy consumption between VAs in active and passive states is experimentally verified. Subsequently, a communication architecture and a model representing the behavior of VAs are presented, from which a metric is developed to evaluate the energy consumption of these devices. The cache-enabled prototype shows a reduction in response time and energy expenditure (comparing the results of cloud-based VA and cache-based VA), several times lower according to the developed metric, demonstrating the effectiveness of the proposed system. This development could be a viable solution for areas with limited power sources, low coverage, and mobility situations that affect internet connectivity.https://www.mdpi.com/2227-7080/13/1/19voice assistantmetricartificial intelligenceenergy savingcacheintelligent systems |
| spellingShingle | Alber Oswaldo Montoya Benitez Álvaro Suárez Sarmiento Elsa María Macías López Jorge Herrera-Ramirez Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric Technologies voice assistant metric artificial intelligence energy saving cache intelligent systems |
| title | Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric |
| title_full | Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric |
| title_fullStr | Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric |
| title_full_unstemmed | Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric |
| title_short | Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric |
| title_sort | optimization of energy consumption in voice assistants through ai enabled cache implementation development and evaluation of a metric |
| topic | voice assistant metric artificial intelligence energy saving cache intelligent systems |
| url | https://www.mdpi.com/2227-7080/13/1/19 |
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