Abstract:
With the advancement in technology, the implementations of smart cities and Internet of
Things (IoT), etc. have enabled the integration of various devices that can communicate
and facilitate in timely decision making. Similarly, traffic congestion is one of the very
serious problem daily commuters are facing. In developed countries like USA, Germany
etc. various sensors are used to gather real-time traffic information to analyze the traffic
status. The traffic condition is communicated to all the commuters through the internet.
The problem become more severe for developing countries where physical infrastructure
and internet connectivity is not available at most of the highways. In this thesis, we
have proposed a VCloud framework that enables route prediction through real-time
data received from neighboring vehicles in ad hoc fashion in the absence of internet
connection. The proposed scheme is implemented on embedded devices and evaluated
in terms of energy and memory consumption. On the contrary to simulation/emulation
based existing work, the proposed framework implemented on smartphone and evaluated
on real-data. The data of live urban traffic is collected from all possible routes between
two populous metropolitan locations. The route prediction through VCloud is analyzed
using collected dataset in the absence of any road side units and internet connectivity.
Moreover, data collected is found consistent with minimum variance by applying data
quality measurement techniques.
