  Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained
remarkable ground from academia and industry. CMA is the state-of-the-art
mobile augmentation model that employs resource-rich clouds to increase,
enhance, and optimize computing capabilities of mobile devices aiming at
execution of resource-intensive mobile applications. Augmented mobile devices
envision to perform extensive computations and to store big data beyond their
intrinsic capabilities with least footprint and vulnerability. Researchers
utilize varied cloud-based computing resources (e.g., distant clouds and nearby
mobile nodes) to meet various computing requirements of mobile users. However,
employing cloud-based computing resources is not a straightforward panacea.
Comprehending critical factors that impact on augmentation process and optimum
selection of cloud-based resource types are some challenges that hinder CMA
adaptability. This paper comprehensively surveys the mobile augmentation domain
and presents taxonomy of CMA approaches. The objectives of this study is to
highlight the effects of remote resources on the quality and reliability of
augmentation processes and discuss the challenges and opportunities of
employing varied cloud-based resources in augmenting mobile devices. We present
augmentation definition, motivation, and taxonomy of augmentation types,
including traditional and cloud-based. We critically analyze the
state-of-the-art CMA approaches and classify them into four groups of distant
fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy.
Vital decision making and performance limitation factors that influence on the
adoption of CMA approaches are introduced and an exemplary decision making
flowchart for future CMA approaches are presented. Impacts of CMA approaches on
mobile computing is discussed and open challenges are presented as the future
research directions.
