# Software Architecture for Mobile Cloud Computing Systems
## 4 Frequency, Classification, and Mapping of the Existing Research
### Taxonomical Classification of Existing Research Themes  
We now answer RQ-2, which aims to investigate the existing research themes, their classification, and mapping to understand the overall progress and contributions of the research. To answer RQ-2, we provide a taxonomical classification of the research state of the art on software architectural solutions for MCC systems as illustrated in Figure 4. Figure 4 represents a taxonomy of the existing research. The taxonomy is defined as: _'a systematic identification, naming, classification and presentation of various research themes and their subthemes (that exist in the literature) based on their similarity or distinctions of research contributions. '_  
We have derived the taxonomy in Figure 4 and classified the research based on the contribution of each study and by following the guidelines of the thematic analysis process [26]. For a fine-grained analysis of the existing research, we provide two-level classification, namely generic and research thematic classification, as detailed below. For example, in the context of Figure 4, the study [S4], in general, supports the (i) offloading of mobile data to the cloud (i.e., generic classification), however (ii) the study specifically focuses on optimizing the energy efficiency of a mobile device by offloading computationally intensive data to a cloud-based server (i.e., thematic classification). * **Generic Classification** provides a high-level categorization of the existing research based on the collective contributions of a number of studies. Specifically, the generic classification highlights four main types of research contributions that highlight the role of software architecture to support (1) _software as a service (SaaS) for Mobile Computing_ (17 studies, approximately 14% of total reviewed studies), (2) _Mobile to Cloud Data Offloading_ (76 studies, 63%), (3) _Internet of Things, Edge, and Fog Computing_ (14 studies, 11.5%), and (4) _Data Security and Miscellaneous_ (14 studies, 11.5%). * **Thematic Classification** provides an extension to the generic classification above by adding details about the focus of the research in a collection of related studies. Specifically, we have used the thematic analysis approach to identify the recurring themes (repeated architectural solutions) as the basis for thematic classification of the existing research [26]. For example, the thematic analysis suggests that a collection of studies such as [S7, S10, S30, S33] represent a repeated solution for offloading data to achieve storage efficiency and performing data analytics by exploiting the cloud computing servers. Thematic classification presents a fine-grained refinement of the generic classification. For example, the research on _Software as a Service for Mobile Computing_ has two specific themes: (a) _Enterprise Software Services for Mobile Computing_ (10 studies, approximately 8.25% of total studies for SMS), and (b) _Recommendation as a Services for Mobile (7 studies, 5.75%). In comparison, the research on _Mobile to Cloud Data Offloading_ can help us to achieve (a) _Computational Efficiency_ (39 studies, 32.25%), (b) _Storage Efficiency_ and _Data Analytics_ (18 studies, 15%), and (c) _Energy Efficiency_ (15 studies, 12.25%). The research on MCC-based Internet of Things, Edge, and Fog Computing supports (a) _Mobility-driven IoTs_ (5 studies, 4%), (b) _Mobile Edge Computing_ (7 studies, 5.75%), and (c) _Fog Computing_ (2 studies, 1.75%). Finally, the research on Data Security and Miscellaneous research trends is focused on (a) _Security and Privacy of Mobile_ (4 studies, 3.25% of studies) and (b) _Miscellaneous issues_ (10 studies, 8.25%). Figure 4: Classification of the research themes on software architecture for MCC. It is vital to mention that in the taxonomical classification as illustrated in Figure 4, we have classified each study exclusively under one research theme or sub-theme only.