Mobile Cloud Computing: Taxonomy and Challenges

Table 4

Summary of application development techniques in MCC.

	Issues	Contribution	Techniques	Implementation	Metrics	Limitations
CCIMH [43]	(i) Healthcare work is tasking, covers different context; (ii) The need for efficiency and accuracy	(i) Integration of different context components; (ii) Adaptation rule and preference	Framework	Not mentioned	Not mentioned	No simulation of the proposed framework
MCTLD [44]	Limitation of the capabilities of existing mobile devices	Architecture	Architecture	Amazon EC2 cloud, mobile phone with an Android application developed, WIFI, and traffic light	Average response time	Standard computation model not considered
CFPCMC [45]	Resources limitation of MIDs	Context-aware provisioning services	Framework and algorithms	Not mentioned	Not mentioned	Security of the provisioning service scheme not handled
APHIS [46]	High transmission rate and limited wireless resources	Adaptive, filtering and adjustable scheme	Framework and algorithm	Emulations in Exata involving HFR video encoded with H.264 codec	(i) Video peak signal-to-noise ratio; (ii) End-to-end delay; (iii) Goodput	A multipath packet transmission scheme for HFR video streaming over heterogeneous wireless networks not considered
MOMCC [47]	Long wireless area network (WAN) latency	Service-oriented architecture	Architecture	Not mentioned	Not mentioned	(i) High complexity of services,; (ii) implementation of MOMCC architecture not considered
MMCCOS [48]	To handle objective and subjective perspectives	Three phases of management architecture	Architecture and model	Prototyped system called CoLisEU	(i) Battery consumption,; (ii) Network traffic,; (iii) Response time	Machine learning techniques to adapt the requirement to each different infrastructure of MCC environment need to be considered
ICIMCA [49]	To test the impact of cloudlet design and services architecture	Cloudlet network and service architecture were achieved	Architecture and algorithm	NS-2	(i) Transfer delay,; (ii) content delivery throughput	An adaptive scheme for the cloudlet scheme not employed
MOCHA [50]	Challenges of splitting of mobile device task to the cloud and share these tasks among the servers	Design of face recognition applications	Design and architecture	Microsoft Visual Studio 2010 development platform C++, and Open CV libraries, servers’ workstations, and a laptop	Response time	During implementation, real cloud and mobile phone not considered