Smart Meters as Non-purpose Built Surveillance Tools

2016 ◽  
pp. 81-95
Author(s):  
Jonida Milaj ◽  
Jeanne Pia Mifsud Bonnici
Keyword(s):  
Smart Meters

A Survey on Internet of Things : Applications and Layered Wise Security Issues

2019 ◽  
pp. 171-180
Author(s):  
Uppuluri Sirisha ◽  
G. Lakshme Eswari
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Security And Privacy ◽  
Global Network ◽  
Business Growth ◽  
Paper Briefly ◽  
Smart Meters ◽  
Security Issues ◽  
Data Points

This paper briefly introduces Internet of Things(IOT) as a intellectual connectivity among the physical objects or devices which are gaining massive increase in the fields like efficiency, quality of life and business growth. IOT is a global network which is interconnecting around 46 million smart meters in U.S. alone with 1.1 billion data points per day[1]. The total installation base of IOT connecting devices would increase to 75.44 billion globally by 2025 with a increase in growth in business, productivity, government efficiency, lifestyle, etc., This paper familiarizes the serious concern such as effective security and privacy to ensure exact and accurate confidentiality, integrity, authentication access control among the devices.

On Multiple Access of a Large Number of Machine-Type Devices in Cellular Networks

2018 ◽  
pp. 105-114
Author(s):  
O. S. Galinina ◽  
S. D. Andreev ◽  
A. M. Tyurlikov
Keyword(s):  
Success Probability ◽  
Random Access ◽  
Time Interval ◽  
Short Time Interval ◽  
Control Mechanisms ◽  
Network Access ◽  
Smart Meters ◽  
Machine To Machine ◽  
Machine To Machine Communication ◽  
Machine Communication

Introduction: Machine-to-machine communication assumes data transmission from various wireless devices and attracts attention of cellular operators. In this regard, it is crucial to recognize and control overload situations when a large number of such devices access the network over a short time interval.Purpose:Analysis of the radio network overload at the initial network entry stage in a machine-to-machine communication system.Results: A system is considered that features multiple smart meters, which may report alarms and autonomously collect energy consumption information. An analytical approach is proposed to study the operation of a large number of devices in such a system as well as model the settings of the random-access protocol in a cellular network and overload control mechanisms with respect to the access success probability, network access latency, and device power consumption. A comparison between the obtained analytical results and simulation data is also offered. 

Intrusion Detection for Cybersecurity of Smart Meters

2021 ◽  
Vol 12 (1) ◽  
pp. 612-622
Author(s):  
Chih-Che Sun ◽  
D. Jonathan Sebastian Cardenas ◽  
Adam Hahn ◽  
Chen-Ching Liu
Keyword(s):  
Intrusion Detection ◽  
Smart Meters

Particle-Swarm-Optimization-Based Nonintrusive Demand Monitoring and Load Identification in Smart Meters

2013 ◽  
Vol 49 (5) ◽  
pp. 2229-2236 ◽  
Author(s):  
Hsueh-Hsien Chang ◽  
Lung-Shu Lin ◽  
Nanming Chen ◽  
Wei-Jen Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Load Identification ◽  
Smart Meters ◽  
Swarm Optimization

scholarly journals Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1521
Author(s):  
Jihoon Lee ◽  
Seungwook Yoon ◽  
Euiseok Hwang
Keyword(s):  
Data Compression ◽  
High Frequency ◽  
Spectral Properties ◽  
Situational Awareness ◽  
Time Windows ◽  
Low Frequency ◽  
Smart Meters ◽  
Compression Performance ◽  
Reconstruction Performance ◽  
Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

Intermittently differential privacy in smart meters via rechargeable batteries

2021 ◽  
Vol 199 ◽  
pp. 107410
Author(s):  
Xing Liu ◽  
Huiwei Wang ◽  
Guo Chen ◽  
Bo Zhou ◽  
Aqeel ur Rehman
Keyword(s):  
Differential Privacy ◽  
Rechargeable Batteries ◽  
Smart Meters

scholarly journals Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3242
Author(s):  
Hamid Mirshekali ◽  
Rahman Dashti ◽  
Karsten Handrup ◽  
Hamid Reza Shaker
Keyword(s):  
Fault Location ◽  
Network Reliability ◽  
Distribution Network ◽  
Estimation Method ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Active Power ◽  
Financial Loss ◽  
Smart Meters ◽  
The Real

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

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