Abstract:
Context and challenges: In recent years, smart city systems have emerged as solutions that
transform the conventional cities and societies into information and communication (ICT) driven cities.
Smart city systems o er improved and digitized urban services such as smart health and smart shopping
to the stakeholders such as citizens, business entities and organizations. However, in mobile-cloud based
smart city systems, challenges such as context-awareness, performance of cloud servers and the resource
poverty of mobile devices must be addressed.
Solution and implications : In this thesis, we focus on the integration of the mobile
computing and cloud computing technologies to develop a system that o ers its users with context- aware
and mobility-driven recommender system for smart markets. In the proposed solution, a mobile device
represents the front-end (mobility and context-aware) interface for recommendations, while; cloud-based
server represents the back-end (computation-intensive) processor of the system to enable digital match
making between potential customer and business entities..
Evaluations and conclusions : We have utilized the ISO-IEC-9126 quality model to
evaluate the accuracy and e ciency of the proposed recommender systems. Considering the resource
poverty of a mobile device, the evaluation results suggest the accuracy, along with computational and
energy e ciency of the recommender system. The future research is focused on the application of machine
learning approaches for an intelligent, context-aware recommendation system.
