A Hardware-assisted Heartbeat Mechanism for Fault Identification in Large-scale IoT Systems

Banerjee, Mandrita; Borges, Carlo; Choo, Kim‐Kwang Raymond; Lee, Jung‐Hee; Nicopoulos, Chrysostomos

doi:10.1109/tdsc.2020.3009212

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…Security is the major concern for digital wireless communication, so Hamza Omar et al [27] have introduced the partitioning-centric microarchitecture to improve the digital data transmission process in a dynamic environment. Here, the partitioning process was executed based on the user's counts and the data load types.…”

Section: Related Workmentioning

confidence: 99%

“…Considering these issues, the current study was intended to develop an intelligent optimal strategy as the fault alerting system to make awareness about the data overhead and malicious events. In the past, fault prediction processes existed, such as partitioning microarchitecture [27], mechanistic gap microarchitecture [28], etc., but the proper detection is still not reported. This has tended to cause high power and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

Gaikwad,

2023

IJRITCC

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The usage Internet of Things (IoT) was maximized throughout the entire world. Hence, the different core processors incorporated microarchitecture makes this IoT communication system. However, the rise of faults due to the malicious event and the data overload might maximize energy and power utilization. So, the current study has proposed a novel Chimp-based Domain adaptation Alert System (CbDAAS) for the dynamic IoT communication microarchitecture. Before initiating the communication sharing process, the present fault in the designed IoT dynamic core microarchitecture was predicted, and those cores were removed for the current data broadcasting process. Henceforth, the designed fault alert microarchitecture is tested in the MATLAB platform. The reliability was valued using different metrics like power usage, energy consumption and detection exactness value. Finally, the validated metrics were compared with the associated studies and scored the finest outcome in fault detection score as 98% and less energy usage at 0.025mj.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

Gaikwad,

2023

IJRITCC

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“…How to manage and make use of these IoT data to derive intelligence in efficient, secure and economic ways becomes an essential research question. Failure detection is one of the most essential problems in IIoT [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT

Zhang

et al. 2020

Preprint

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Federated learning is an emerging privacypreserving machine learning paradigm which has attracted great interests from the community of Industrial Internet of Things (IIoT). Blockchain has been recently leveraged in IIoT federated learning to provide data integrity and incentives to attract sufficient client data and computation resources for training. However, there is a lack of systematic architecture design for blockchain-based federated learning systems to support methodical development and tackle the challenge of data heterogeneity in failure detection of IIoT. Also, the current solutions do not consider the incentive mechanism design and scalability issue of blockchain. Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT. To address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW FedAvg) algorithm taking into account the distance between positive class and negative class of each client dataset. To enable verifiable integrity of client data in a scalable way, each client server periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. An on-chain incentive mechanism is designed based on the size of client data used in local model training to accurately and timely calculate each client's contribution. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy and performance. The results show that the approach ensures data integrity and has satisfactory detection accuracy and performance.

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A novel CFN-Watchdog protocol for edge computing

Liang

Feng

et al. 2021

Applied Soft Computing

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A Hardware-assisted Heartbeat Mechanism for Fault Identification in Large-scale IoT Systems

Cited by 12 publications

References 37 publications

An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT

A novel CFN-Watchdog protocol for edge computing

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