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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 theresearch agenda. In this paper, we evaluate the impact of demand splitting overheterogeneous processing resources in an IoT platform, supported by Fog andCloud infrastructure. We develop a Mixed Integer Linear Programming (MILP)model to study the gains of splitting resource intensive demands among IoTnodes, Fog devices and Cloud servers. A surveillance application is considered,which consists of multiple smart cameras capable of capturing and analyzingreal-time video streams. The PON access network aggregates IoT layer demandsfor processing in the Fog, or the Cloud which is accessed through the IP/WDMnetwork. For typical video analysis workloads, the results show that splittingmedium demand sizes among IoT and Fog resources yields a total powerconsumption saving of up to 32%, even if they can host only 10% of the totalworkload and this can reach 93% for lower number of demands, compared to thecentralized cloud solution. However, the gains in power savings from splittingdecreases as the number of splits increases. Keywords: IoT surveillance, PON,energy efficiency, fog, distributed processing.

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications