Energy Efficient Neural Network Embedding in IoT over Passive Optical Networks

Abstract: In the near future, IoT based application services are anticipated to collect massive amounts of data on which complex and diverse tasks are expected to be performed. Machine learning algorithms such as Artificial Neural Networks (ANN) are increasingly used in smart environments to predict the output for a given problem based on a set of tuning parameters as the input. To this end, we present an energy efficient neural network (EE-NN) service embedding framework for IoT based smart homes. The developed framework considers the idea of Service Oriented Architecture (SOA) to provide service abstraction for multiple complex modules of a NN which can be used by a higher application layer. We utilize Mixed Integer Linear Programming (MILP) to formulate the embedding problem to minimize the total power consumption of networking and processing simultaneously. The results of the MILP model show that our optimized NN can save up to 86% by embedding processing modules in IoT devices and up to 72% in fog nodes due to the limited capacity of IoT devices.