Next Article in Journal
Determination of Maximum Wind Power Penetration in an Isolated Island System by Considering Spinning Reserve
Previous Article in Journal
A Proposal on Low Frequency AC Transmission as a Multi-Terminal Transmission System
Article Menu

Export Article

Open AccessArticle
Energies 2016, 9(9), 691; doi:10.3390/en9090691

Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach

Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Chunhua Liu
Received: 20 April 2016 / Revised: 11 July 2016 / Accepted: 22 August 2016 / Published: 29 August 2016
View Full-Text   |   Download PDF [1793 KB, uploaded 29 August 2016]   |  

Abstract

In smart cities, advanced metering infrastructure (AMI) of the smart grid facilitates automated metering, control and monitoring of power distribution by employing a wireless network. Due to this wireless nature of communication, there exist potential threats to the data privacy in AMI. Decoding the energy consumption reading, injecting false data/command signals and jamming the networks are some hazardous measures against this technology. Since a smart meter possesses limited memory and computational capability, AMI demands a light, but robust security scheme. In this paper, we propose a localization-based key management system for meter data encryption. Data are encrypted by the key associated with the coordinate of the meter and a random key index. The encryption keys are managed and distributed by a trusted third party (TTP). Localization of the meter is proposed by a method based on received signal strength (RSS) using the maximum likelihood estimator (MLE). The received packets are decrypted at the control center with the key mapped with the key index and the meter’s coordinates. Additionally, we propose the k-nearest neighbors (kNN) algorithm for node/meter authentication, capitalizing further on data transmission security. Finally, we evaluate the security strength of a data packet numerically for our method. View Full-Text
Keywords: advanced metering infrastructure (AMI); data security; key management system; k-nearest neighbors (kNN); received signal strength (RSS); smart city; smart meter; smart grid advanced metering infrastructure (AMI); data security; key management system; k-nearest neighbors (kNN); received signal strength (RSS); smart city; smart meter; smart grid
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Parvez, I.; Sarwat, A.I.; Wei, L.; Sundararajan, A. Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach. Energies 2016, 9, 691.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top