Big Data Mining of Users’ Energy Consumption Patterns in the Wireless Smart Grid

A growing number of utility companies are starting to use cellular wireless networks to transmit data in the smart grid. Consequently, millions of users' daily energy consumption data are sent by wireless smart meters. However, the broadcast transfer manner of wireless communication makes it na...

Full description

Saved in:
Bibliographic Details
Published in:IEEE wireless communications 2018-02, Vol.25 (1), p.84-89
Main Authors: Liehuang Zhu, Meng Li, Zijian Zhang, Xiaojiang Du, Guizani, Mohsen
Format: Article
Language:English
Subjects:
Big Data
Energy consumption
Machine learning algorithms
Smart grids
Smart meters
Wireless communication
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A growing number of utility companies are starting to use cellular wireless networks to transmit data in the smart grid. Consequently, millions of users' daily energy consumption data are sent by wireless smart meters. However, the broadcast transfer manner of wireless communication makes it naturally vulnerable to cyber attacks. Since smart meter readings can easily be leaked, users' energy patterns could be inferred. Hence, users' privacy at home is under serious threat. This article begins by introducing the existing work on stealing data from wireless communication networks. Then three types of big data mining schemes for analyzing stolen data are represented. Finally, we discuss several ongoing defense strategies in the era of the wireless smart grid.
ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.2018.1700157