IoT Big Data Security, Privacy and Challenges Related to Smart Grid

2022 ◽  
pp. 368-379
Author(s):  
Kimmi Kumari ◽  
M. Mrunalini
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Type Systems ◽  
Heterogeneous Data ◽  
Security Measures ◽  
Smart Meters ◽  
Efficient Manner ◽  
Security Issues ◽  
Iot Devices

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

IoT Big Data Security, Privacy and Challenges Related to Smart Grid

2019 ◽  
Vol 8 (2) ◽  
pp. 1-10
Author(s):  
Kimmi Kumari ◽  
M. Mrunalini
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Type Systems ◽  
Heterogeneous Data ◽  
Security Measures ◽  
Smart Meters ◽  
Efficient Manner ◽  
Industrial Sectors ◽  
Iot Devices

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

scholarly journals IoT based Smart Grid Attack Resistance in AC and DC State Estimation

2021 ◽  
Vol 2 (3) ◽  
pp. 118-122
Author(s):  
Vivekanadam B
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Smart Grid ◽  
Error Detection ◽  
Smart Grids ◽  
Data Detection ◽  
Bad Data Detection ◽  
Security Issues ◽  
Number Of Factors ◽  
Bad Data

Use of automation and intelligence in smart grids has led to implementation in a number of applications. When internet of things is incorporated it will result in the significant improvement a number of factors such as fault recovery, energy delivery efficiency, demand response and reliability. However, the collaboration of internet of things and smart grid gives rise to a number of security issues and threats. This is especially the case when using internet based protocols and public communication infrastructure. To address these issues we should ensure that the data stored is secure and critical information from the data is extracted in a careful manner. If any threat to its security is detective an early blackout warning should be issued immediately. In this paper we have proposed a geometric view point for big data attacks which is capable of bypassing bad data detection. We have created an environment where replay scheme is used launch blind energy big data attack. The defence mechanism of our proposed work is studied and found to be efficient. Experimental evidence supports our theory and we have found our methodology to efficiently improve error detection rate.

scholarly journals Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

2018 ◽  
Vol 7 (2.26) ◽  
pp. 85
Author(s):  
Dr E. Laxmi Lydia ◽  
B Prasanna Kumar ◽  
D Ramya
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Processing ◽  
Energy Management ◽  
Electricity Market ◽  
Smart Grids ◽  
Data Analytics ◽  
Performance Estimation ◽  
Smart Meters ◽  
Dynamic Energy Management

The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield. 

Survey of Big Data Role in Smart Grids: Definitions, Applications, Challenges, and Solutions

2018 ◽  
Vol 12 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Mahmoud Ghofrani ◽  
Andrew Steeble ◽  
Christopher Barrett ◽  
Iman Daneshnia
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Power Grids ◽  
Time Data ◽  
Smart Meters ◽  
Communication Devices ◽  
Big Data Technologies ◽  
Big Data Application ◽  
Real Time Data Processing

Objective:This paper provides a literature review on smart grids and big data. Smart grid refers to technologies used to modernize the energy delivery of traditional power grids, using intelligent devices and big data technologies.Methods:The modernization is performed by deploying equipment such as sensors, smart meters, and communication devices, and by invoking procedures such as real-time data processing and big data analysis. A large volume of data with high velocity and diverse variety are generated in a smart grid environment.Conclusion:This paper presents definitions and background of smart grid and big data. Current studies and research developments of big data application in smart grids are also introduced. Additionally, big data challenges in smart grid systems such as security and data quality are discussed.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

2021 ◽  
pp. 1-16
Author(s):  
Sasikala Chinthakunta ◽  
Shoba Bindu Chigarapalle ◽  
Sudheer Kumar E.
Keyword(s):  
Data Storage ◽  
Smart Grids ◽  
Traffic Management ◽  
Fog Computing ◽  
Poor Quality ◽  
Data Traffic ◽  
Cloud Data ◽  
Security Issues ◽  
Huge Data ◽  
Iot Devices

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

Vulnerabilities and Threats in Smart Grid Communication Networks

2021 ◽  
pp. 1508-1535
Author(s):  
Yona Lopes ◽  
Natalia Castro Fernandes ◽  
Tiago Bornia de Castro ◽  
Vitor dos Santos Farias ◽  
Julia Drummond Noce ◽  
...  
Keyword(s):  
Smart Grid ◽  
Communication Networks ◽  
Smart Grids ◽  
Electrical Network ◽  
Physical Security ◽  
Smart Meters ◽  
Interconnected System ◽  
Other Information ◽  
Data Volume ◽  
Active Attacks

Advances in smart grids and in communication networks allow the development of an interconnected system where information arising from different sources helps building a more reliable electrical network. Nevertheless, this interconnected system also brings new security threats. In the past, communication networks for electrical systems were restrained to closed and secure areas, which guaranteed network physical security. Due to the integration with smart meters, clouds, and other information sources, physical security to network access is no longer available, which may compromise the electrical system. Besides smart grids bring a huge growth in data volume, which must be managed. In order to achieve a successful smart grid deployment, robust network communication to provide automation among devices is necessary. Therefore, outages caused by passive or active attacks become a real threat. This chapter describes the main architecture flaws that make the system vulnerable to attacks for creating energy disruptions, stealing energy, and breaking privacy.

Early Detection and Recovery Measures for Smart Grid Cyber-Resilience

2021 ◽  
pp. 91-110
Author(s):  
Ismail Butun ◽  
Alparslan Sari
Keyword(s):  
Smart Grid ◽  
Cyber Security ◽  
Early Stage ◽  
Security Measures ◽  
Smart Meters ◽  
Grid Systems ◽  
Technological Advances ◽  
Response And Recovery ◽  
Operational Failures ◽  
Security Incidents

The internet of things (IoT) has recently brought major technological advances in many domains, including the smart grid. Despite the simplicity and efficiency that IoT brings, there are also underlying risks that are slowing down its adoption. These risks are caused by the presence of legacy systems inside existing infrastructures that were built with no security in mind. In this chapter, the authors propose a method for early-stage detection of cyber-security incidents and protection against them through applicable security measures. This chapter introduces security techniques such as anomaly detection, threat investigation through a highly automated decision support system (DSS), as well as incident response and recovery for smart grid systems. The introduced framework can be applied to industrial environments such as cyber-threats targeting the production generator as well as the electricity smart meters, etc. The chapter also illustrates the framework's cyber-resilience against zero-day threats and its ability to distinguish between operational failures as well as cyber-security incidents.

scholarly journals A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

2021 ◽  
Vol 13 (23) ◽  
pp. 13322
Author(s):  
Vinoth Kumar Ponnusamy ◽  
Padmanathan Kasinathan ◽  
Rajvikram Madurai Elavarasan ◽  
Vinoth Ramanathan ◽  
Ranjith Kumar Anandan ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Management ◽  
Smart Grids ◽  
Data Analytics ◽  
Management Practices ◽  
Research Work ◽  
Big Data Analytics ◽  
Energy Data ◽  
The Sustainable Development

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

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