Blockchain Technology for Smart Grids: Decentralized NIST Conceptual Model

Aderibole, Adedayo; Aljarwan, Aamna; Rehman, Muhammad Habib ur; Zeineldin, H. H.; Mezher, Toufic; Damiani, Ernesto; Svetinovic, Davor

doi:10.1109/access.2020.2977149

Cited by 59 publications

(18 citation statements)

References 59 publications

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“…Diverse IoT devices and frameworks are applied on smart grid to support data collection, transmission [ 4 ], real-time monitoring [ 5 ], etc. Blockchain technologies can provide decentralization, trust, and an incentive mechanism for improving the cybersecurity of smart grid system [ 6 , 7 , 8 ]. Compared with AI, the applications of AI methods including machine learning and deep learning are usually used to process and analyze data for decision-making, such as electric load forecasting [ 9 , 10 ], electric consumer categorization [ 11 ], and anomaly detection [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

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The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.

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Section: Introductionmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

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“…In order to handle this, the attacker would have to either alter the headers of all the blocks that follow the block that's being attacked or make those alterations to at least 2/3 rd of the entire network for the consensus to succeed. Since both the ways seem to be highly demanding in terms of computational and communicational power, the system is shielded against false -data injection attacks [37].…”

Section: Figurementioning

confidence: 99%

Integration Of Smart Grid And Blockchain

Garg¹

2021

Preprint

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<div><div><div><p>A majority of electricity consumed by humans comes from traditional power generators, that work on a centralised framework, distributing it through wires and grids. More than 80% of this electricity comes from fossil fuels, leading to large amounts of pollution, since it is carbon intensive. The distribution methods are inefficient as much energy is lost in transmission. This leads to it being expensive for the consumer, and makes them dependent on large corporations. A large percentage of the electricity bill goes towards paying off the investment in infrastructure needed to provide it. This model is clearly not sustainable. Furthermore, it is estimated that around 1.2 billion people live without access to safe electricity. This is primarily since electricity providers don’t deem building infrastructure for a remote or economically backward location as profitable enough, since they are unlikely to pay it back. Contrary to conventional system, our proposed framework will be focused at introducing the decentralised energy distribution and consumption using Blockchain. Many prior studies have been done to decentralise the energy distribution but due to security and trust scare the systems could not be fully adopted in the real setting. We take a look into the methodologies third parties can use to counter the security issues as well. However, by integrating blockchain into the existing smart grid architecture we open up the possibility of bypassing the tiring process of renewable certification, make a localised energy production a reality and detach the consumers from the dependency of central grid. To fully assimilate this environment with the end users we also discuss about the real time platform for prosumers and consumers to trade energy. At the end, we look at the costs, consumption and other attributes of three different community microgrids simu</p></div></div></div>

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“…For instance, approaches in the healthcare sector try to optimize data security [33] and authentication schemes [34] for electronic health records. Other authors propose to integrate the blockchain technology in the energy sector in order to enable decentralized electricity load verification, decentralized energy marketplace, P2P energy load management or energy consumption reporting [35,36]. Blockchain is also introduced to support processes in Artificial Intelligence (AI) [37] or P2P applications [38].…”

Section: A Blockchain In Different Application Fieldsmentioning

confidence: 99%

Blockchain and Trust for Secure, End-User-Based and Decentralized IoT Service Provision

et al. 2020

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Building trust relationships between different decentralized entities in the IoT ecosystem is essential. Hereof, the combination of blockchain technology and trust evaluation techniques is recently considered as an efficient measure. However, both technologies within the IoT are still facing some limitations which are addressed in this research. First, this publication reviews various blockchain-based trust approaches and depicts their strengths and limitations regarding their usage in decentralized IoT communities. Then, an optimized trust model with a multi-layer adaptive and trust-based weighting system is proposed. Additionally, different trust metric parameters and their mathematical models used for trust evaluation are presented. Moreover, this publication presents a novel approach for incentivization processes in the IoT marketplace using control loops and smart contracts. Thereby, participants are motivated to continuously improve their behavior. Finally, the proposed trust model is proved to be reliable. The experimental results conducted from different scenarios show that the presented approach provides more resiliency against various attacks than existing ones.

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Blockchain Technology for Smart Grids: Decentralized NIST Conceptual Model

Cited by 59 publications

References 59 publications

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Integration Of Smart Grid And Blockchain

Blockchain and Trust for Secure, End-User-Based and Decentralized IoT Service Provision

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