MSRM-IoT: A Reliable Resource Management for Cloud, Fog, and Mist-Assisted IoT Networks

Hosen, A. S. M. Sanwar; Sharma, Pradip Kumar; Cho, Gihwan

doi:10.1109/jiot.2021.3090779

Cited by 10 publications

(4 citation statements)

References 41 publications

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“…Despite this information, studies that were simulated and/or emulated for CF are presented below, from which there are several proposals on the improvement of infrastructures in dynamic resource provisioning [ 52 ] developing different methodologies that operate close to a shopfloor (Fog or Edge architecture). In this sense [ 53 ], proposes an FC framework called FITOR, through orchestration of resources for devices of different layers, services and links, adding value to Service Deployer for the optimization of location, computation and network requirements in Fog nodes (FN) and Mist nodes (MN) [ 54 , 55 ]. The author in Ref.…”

Section: Industrial Sector Implementations In Iot and Iiotmentioning

confidence: 99%

Analysis of architectures implemented for IIoT

Oñate¹,

Sanz²

2023

Heliyon

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Section: Industrial Sector Implementations In Iot and Iiotmentioning

confidence: 99%

Analysis of architectures implemented for IIoT

Oñate¹,

Sanz²

2023

Heliyon

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“…Resource management and allocating resources for IoT tasks accurately is one of the keys to achieving better latency, bandwidth, and energy efficiency for IoT networks. Therefore, Hosen et al, 2022 [ 8 ] proposed a new algorithm called MSRM-IoT to allocate resources for IoT tasks. All IoT tasks are first sent to the Edge broker (EB) in this algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…MATLAB19a was used to validate the proposed algorithm and compare it with three evolutionary algorithms: router, FCFS, and short job first. The results showed that MSRM-IoT outperforms the three algorithms across all measures [ 8 ]. The parameters mentioned in MSRM-IoT are mainly based on the computing size only to determine a resource for the IoT tasks, which is not efficient from our point of view.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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“…Several approaches have been introduced to enable the services and meet the objectives over the last few years. Efficient resource and task allocation, one of the primary objectives of edge computing, can be achieved by applying statistical techniques or artificial intelligence (AI), such as machine learning (ML) and deep learning (DL) [3], [4], [5]. Securing confidential data and user privacy are of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

SECBlock-IIoT: A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

Hosen

Sharma

Puthal

et al. 2023

Proceedings of the Third International Symposium on Advanced Security on Software and Systems

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The IoT is widely used in a number of industries and generates large amounts of data. The data are processed, computed, and stored through distributed computing for analytical purposes. This invokes serious security and privacy concerns, and presents scalability issues. This paper describes a secure P2P and group communication supportive edge computing framework for IIoT systems, a consortium blockchain, and IPFS-based immutable data storage system, and an intelligent threat detection model to protect confidential data and identify cyber-attacks. Secure communications were ensured using a hybrid security scheme that included modified ECC, PUF, and Lagrange interpolation. We utilized a modified PoV consensus algorithm to resolve latency issues due to overhead and point of failure errors during block mining. The threat intelligence model used an autoencoder to transform data into a new format which was then fed into an RNN-DL to identify cyber-attacks. The model detected normal and anomalous activity, and then identified the category of detected malicious activity. We evaluated the framework according to various metrics and compared it with ECC, PoV, and ML-based classifiers. The results showed that the proposed system demonstrated a higher efficiency and improved scalability than conventional frameworks.

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MSRM-IoT: A Reliable Resource Management for Cloud, Fog, and Mist-Assisted IoT Networks

Cited by 10 publications

References 41 publications

Analysis of architectures implemented for IIoT

Analysis of architectures implemented for IIoT

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

SECBlock-IIoT: A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

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