Abstract:
Vehicular communication is considered to be the integral part of smart cities.
Furthermore, self-driven cars imposes the stringent challenges in terms of capacity,
delay and mobility. Recently, internet of intelligent vehicle (IoIV) is emerged as the
promising solution self-driven vehicles. Considering the diversity of data required
and generated through IoIV nodes in terms of real-time and non-real time traffic, there
is a need of efficient and intelligent communication platform to cater the stringent
demands of IoIV traffic. Therefore, In this thesis, we developed an intelligent channel
allocation algorithm which cater the demands of heterogeneous IoT nodes considering
their minimal quality of service (QoS) requirements in terms of data rate, reliability
and latency. We first formed the classes based on their traffic types and then allocated
the channels considering their minimal QoS requirements. For classification we
propose a novel two-tie classification model. Under our proposed model, an IoIV
node can also have a secondary communication platform in case when the primary
communication systems fails to provide communication channel for the transmission.
Our designed system is able to achieve better performance in terms of reliability,
delay and throughput. In addition, our system ensures the availability of routing path
for big data information.
