Internet of Things for Residential Areas: Toward Personalized Energy Management Using Big Data

TL;DR

This paper proposes an intelligent, cloud-based energy management system for residential areas that uses big data, optimization, and cognitive radio networks with ant colony optimization for spectrum assignment, enhancing energy efficiency and user engagement.

Contribution

It introduces a novel integrated system combining big data, optimization, and cognitive radio for personalized energy management in homes, with a new spectrum assignment method using ACO.

Findings

The spectrum assignment method achieves fair spectrum distribution among devices.

The system effectively utilizes big data for energy optimization.

Cognitive radio enhances communication efficiency in HAN.

Abstract

Intelligent management of machines, particularly in a residence area, has been of interest for many years. However, such system design has always been limited to simple control of machines from a local area or remotely from the Internet. In this report, for the first time, an intelligent system is proposed, where not only provides intelligent control ability of machines to user, but also utilizes big data and optimization techniques to provide promotional offers to the user to optimize energy consumption of machines. Since a high traffic communication is involved among the machines and the optimization-big data core of system, the communication core of the proposed system is designed based on cloud, where many challenging issues such as spectrum assignment and resource management are involved. To deal with that, the communication network in the home area network (HAN) is designed based on the cognitive radio system, where a new spectrum assignment method based on the ant colony optimization (ACO) algorithm is proposed to perform spectrum assignment to the machines in the HAN. Performance evaluation of the proposed spectrum assignment method shows its performance in fair spectrum assignment among machines.