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

TL;DR

This paper evaluates how demand splitting across IoT, Fog, and Cloud resources can significantly reduce power consumption in IoT surveillance applications, highlighting the benefits of distributed processing.

Contribution

It introduces a MILP model to analyze power savings from demand splitting in IoT surveillance, demonstrating substantial energy efficiency improvements over centralized cloud solutions.

Findings

Splitting demands can save up to 32% power consumption.

Power savings reach up to 93% for fewer demands.

Efficiency gains decrease with more demand splits.

Abstract

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.