Security Breach Possibility with RSS-Based Localization of Smart Meters Incorporating Maximum Likelihood Estimator

Abstract

Deployment of smart meters has been greatly increased over the recent years. Most of the installed smart meters have been equipped with Advanced Metering Infrastructure (AMI) which enables a bidirectional wireless communication to gather the usage data from gas, electricity and water meters. The insecure wireless channel used by AMI meters jeopardizes the privacy of costumers and brings up cybersecurity issues since it allows hackers to monitor the energy usage data from different houses. To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper. By decoding the received signal from a smart meter, one can localize the unoccupied houses or track the people's daily routines. The effectiveness of the proposed ML location estimator has been examined through MATLAB simulation, under the assumption of a log-normal path loss model and Frequency Shift Keying (FSK) modulation and demodulation. Particle Swarm Optimization (PSO) has been used to find the ML estimation. Finally, the effect of the variance, the number of the sensors and the path loss exponent has been studied on the average Miss Distance Error (MDE). © Springer International Publishing Switzerland 2015.