The Internet of Things

The Internet of Things (IoT) is generating huge variety
of data to be computed by the cloud. However, the delay
in sending this data to the cloud reduces the probability of
the opportunity to take prompt action based on this data. For
example, even though the mobile communication devices gen-
erate plenty of data for the drivers in vehicles, informing them
about the traffic congestion condition on the road network, yetthe traffic congestion problem still exists. The reason behind
this congestion is that the data received by the drivers suffer
delays from the networks. And the rerouting suggestions made
by the cloud analysis become outdate by the time it reaches
to the drivers.
Any vehicle that is equipped with computation, storage
and the network connectivity can act as a vehicular fog
node. The vehicular fog nodes can communicate with each
other in real-time through the WAVE architecture and can
run fog computing applications with less than millisecond
response time. In order to consider vehicular fog node for
the possible fog computation it is essential to understand its
current resources and the possible integration of the WAVE
architecture. A report 34 by BMW research group predicted
that the next generation of vehicles will most likely still use
100 Mbps Ethernet link to connect the Electronic Control units
ECUs.
The emerging trend of Internet of Vehicles (IoV) has
introduced various issues that can be faced by the today’s
vehicular fog computing and cloud servers as shown in the Fig.
1. Many V2X applications may require low latencies, however
these requirements are much beyond the cloud services 35.
The fog computation capacity for a given geographical traffic
environment can be defined as the cumulative sum of the
individual fog computing nodes staying time in a certain area,
assuming each fog computing node has similar computation
capability 1. Hence, it is essential for a fog computing simu-
lation framework to identify the fog computation capacity in a
given geographical traffic scenario. The mobility of fog nodes
is highly complex and its distribution is unknown. So we can
assume that the probability distribution of vehicular mobility
follows Gaussian distribution under a given geographical area.
From the fog industry’s point of view the installation of RSUs
at the most optimum locations along with fog servers is an
important factor. The proposed framework can also predict
the optimum locations in a given geographical region, that will
also provide benefit to the fog server management industry.