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

Liehuang Zhu, Meng Li, Zijian Zhang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)84-89
Number of pages6
JournalIEEE Wireless Communications
Volume25
Issue number1
DOIs
Publication statusPublished - Feb 2018

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