A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

Yang, Zekun; Chen, Ning; Chen, Yu; Zhou, N.

doi:10.1109/cfec.2018.8358730

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…Although SSA uses some statistical concepts, it does not need any statistical assumptions about the target series. Moreover, SSA algorithm can be used for processing time series with relatively small size, which make this method more suitable for edge-fog scenarios [40]. The SSA algorithm can be described as follows: 1.…”

Section: Fast Anomaly Detection For Fair Miningmentioning

confidence: 99%

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Qu¹,

Xu²,

Chen³

et al. 2021

Preprint

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Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain assures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner-Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring miner’s footage that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA) based detection achieves to identify individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect byzantine miners that collude to compromise a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of proposed MinT scheme under a distributed byzantine network environment.

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Section: Fast Anomaly Detection For Fair Miningmentioning

confidence: 99%

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Qu¹,

Xu²,

Chen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Fast Anomaly Detection For Fair Miningmentioning

confidence: 99%

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Chen

et al. 2021

Future Internet

Self Cite

View full text Add to dashboard Cite

Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain ensures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring of a miner’s footprint that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA)-based detection achieves the identification of individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect dishonest miners that collude to control a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of the proposed MinT scheme under a distributed byzantine network environment.

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“…In contrast, we model the PMU data stream individually within its local context for use in real-time detection. In [13], the authors use K-nearestneighbor (KNN) and Singular Spectrum Analysis (SSA) combined with fog computing which moves processing to edge devices. In future work, we intend to evaluate our modeling methods on multiple PMU data streams and incorporate edge computing paradigms.…”

Section: Related Workmentioning

confidence: 99%

Real-time Anomaly Detection and Classification in Streaming PMU Data

Hannon¹,

Deka²,

Dong³

et al. 2019

Preprint

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Ensuring secure and reliable operations of the power grid is a primary concern of system operators. Phasor measurement units (PMUs) are rapidly being deployed in the grid to provide fast-sampled operational data that should enable quicker decision-making. This work presents a general interpretable framework for analyzing real-time PMU data, and thus enabling grid operators to understand the current state and to identify anomalies on the fly. Applying statistical learning tools on the streaming data, we first learn an effective dynamical model to describe the current behavior of the system. Next, we use the probabilistic predictions of our learned model to define in a principled way an efficient anomaly detection tool. Finally, the last module of our framework produces on-the-fly classification of the detected anomalies into common occurrence classes using features that grid operators are familiar with. We demonstrate the efficacy of our interpretable approach through extensive numerical experiments on real PMU data collected from a transmission operator in the USA.

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A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

Cited by 17 publications

References 22 publications

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Real-time Anomaly Detection and Classification in Streaming PMU Data

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