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The Two Faces of AI in Green Mobile Computing: A Literature Review

arXiv 2308.04436 ↗First posted 21 Jul 2023cs.AI cs.CYcited by 0

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Abstract (arXiv)

Artificial intelligence is bringing ever new functionalities to the realm of mobile devices that are now considered essential (e.g., camera and voice assistants, recommender systems). Yet, operating artificial intelligence takes up a substantial amount of energy. However, artificial intelligence is also being used to enable more energy-efficient solutions for mobile systems. Hence, artificial intelligence has two faces in that regard, it is both a key enabler of desired (efficient) mobile functionalities and a major power draw on these devices, playing a part in both the solution and the problem. In this paper, we present a review of the literature of the past decade on the usage of artificial intelligence within the realm of green mobile computing. From the analysis of 34 papers, we highlight the emerging patterns and map the field into 13 main topics that are summarized in details. Our results showcase that the field is slowly increasing in the past years, more specifically, since 2019. Regarding the double impact AI has on the mobile energy consumption, the energy consumption of AI-based mobile systems is under-studied in comparison to the usage of AI for energy-efficient mobile computing, and we argue for more exploratory studies in that direction. We observe that although most studies are framed as solution papers (94%), the large majority do not make those solutions publicly available to the community. Moreover, we also show that most contributions are purely academic (28 out of 34 papers) and that we need to promote the involvement of the mobile software industry in this field.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 21 Jul 2023.

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References · 15

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3 more
  1. 2018Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learningcited by 6
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  3. 2022Context-aware adaptation of mobile video decoding resolutioncited by 1

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