Dear Colleagues,

For the past two decades, smartphones have fundamentally altered the way we live our lives and interact with others. From reading the news to interacting with others, to running a business, we often depend on these small but powerful devices to complete our tasks. Smartphones also allow us to connect with other digital entities (e.g., smartwatches, IoT devices, smart systems, AI assistants, etc.) in multiple ways, and communicate and control with them with ease. Smartphones generate and collect a large amount of data (~60 gigabytes) and this is expected to grow with the explosion of IoT and the highly anticipated arrival of 5G. Currently, in most cases, the generated data is uploaded onto a cloud to extract actionable knowledge by applying various machine learning techniques. However, as smartphones have become powerful computing devices and will continue to improve their computing profile, bringing machine learning techniques and algorithms to the smartphones will boost truly intelligent smartphones.

In light of the above observations, in this Special Issue, we look for original work on machine learning techniques and algorithms on smartphones, addressing particular challenges. On the one hand, conventional machine learning generally uses powerful computing infrastructures (e.g., cloud computing platforms), while smartphones only have limited resources for computations and communications. This suggests that machine learning algorithms or, at least, the implementations of machine learning algorithms, should be revisited for smartphones, which represents a considerable risk and challenge at once. This research area also allows new applications of machine learning and artificial intelligence, opening up new opportunities for smartphones. This Special Issue offers a venue for researchers from both academia and industry to present their solutions for re-designing machine learning algorithms compatible with smartphones, and for building intelligent device by machine learning techniques, possibly revealing new, compelling use cases.

Some of the relevant topics include, but are not limited to the following:

• Machine learning/deep learning techniques for smartphones
• Supervised, unsupervised, and reinforcement learning for smartphones
• Reasoning/learning and techniques applied for smartphone data management
• Machine learning for energy efficiency in the smartphone or its applications
• Evaluation metrics for machine learning algorithms and techniques for smartphones
• Testing platforms or techniques for testing machine learning techniques for smartphones

Dr. Mehdi Sookhak
Dr. Rishi Saripalle
Dr. Mahboobeh Haghparast
Guest Editors

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