  With the rapid proliferation of connected devices in the Internet of Things
(IoT), the centralized cloud solution faces several challenges, out of which,
there is an overwhelming consensus to put energy efficiency at the top of the
research agenda. In this paper, we evaluate the impact of demand splitting over
heterogeneous processing resources in an IoT platform, supported by Fog and
Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP)
model to study the gains of splitting resource intensive demands among IoT
nodes, Fog devices and Cloud servers. A surveillance application is considered,
which consists of multiple smart cameras capable of capturing and analyzing
real-time video streams. The PON access network aggregates IoT layer demands
for processing in the Fog, or the Cloud which is accessed through the IP/WDM
network. For typical video analysis workloads, the results show that splitting
medium demand sizes among IoT and Fog resources yields a total power
consumption saving of up to 32%, even if they can host only 10% of the total
workload and this can reach 93% for lower number of demands, compared to the
centralized cloud solution. However, the gains in power savings from splitting
decreases as the number of splits increases. Keywords: IoT surveillance, PON,
energy efficiency, fog, distributed processing.
