Battery-Efficient Mobile Systems: Design Patterns and Performance Trade-offs

As mobile devices become increasingly central to modern life, optimizing battery efficiency remains a critical design concern for developers and system architects. This paper explores the fundamental design patterns and architectural strategies employed to create battery-efficient mobile systems. It examines various trade-offs between energy consumption, system performance, user experience, and responsiveness. Through case studies and performance benchmarks, we analyze the impact of techniques such as adaptive resource management, background task scheduling, sensor fusion, and hardware-software co-optimization. The paper also discusses emerging trends in low-power design, including machine learning-based energy profiling and context-aware optimization. Our findings offer practical guidelines for balancing functionality and energy usage in resource-constrained mobile environments.

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Publication Details

Journal
Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023
Published
2025-05-01
DOI
https://doi.org/10.60087/jaigs.v8i1.388
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
0.00
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article

Battery-Efficient Mobile Systems: Design Patterns and Performance Trade-offs

Sri Harsha Pothineni
Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023
Green IT and Sustainability
article

Battery-Efficient Mobile Systems: Design Patterns and Performance Trade-offs

Sri Harsha Pothineni
article en

Abstract

As mobile devices become increasingly central to modern life, optimizing battery efficiency remains a critical design concern for developers and system architects. This paper explores the fundamental design patterns and architectural strategies employed to create battery-efficient mobile systems. It examines various trade-offs between energy consumption, system performance, user experience, and responsiveness. Through case studies and performance benchmarks, we analyze the impact of techniques such as adaptive resource management, background task scheduling, sensor fusion, and hardware-software co-optimization. The paper also discusses emerging trends in low-power design, including machine learning-based energy profiling and context-aware optimization. Our findings offer practical guidelines for balancing functionality and energy usage in resource-constrained mobile environments.

Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023Vol. 8(1)
BC Platforms (Finland) (FI)
Openalex Percentile: Top 84%
Green IT and Sustainability
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