Traffic congestion is an inherent and hard issue to be tackled in huge urban areas, particularly in developing countries where transportation infrastructures have not been grown well to fulfill speedy developing request demands. This paper proposes novel solutions to these issues by devising mobile crowd-sourcing based approaches to traffic estimation. A framework for effective collecting, integrating and analyzing traffic-related data shared by mobile crowds has been devised. Besides, essential issues on predicting traffic conditions at streets where real-time data is missed are also resolved by applying data mining techniques to historical data. A prototype system has been developed to validate the proposed solutions. The experimental results show the feasibility and the effectiveness of the proposed methods revealing that they are ready to be applied in the practice.

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.