ORCID Profile
0000-0002-8647-1393
Current Organisation
Al-Hussein Bin Talal University College of Engineering
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Publisher: Informa UK Limited
Date: 27-02-2023
Publisher: Elsevier BV
Date: 05-2009
Publisher: MDPI AG
Date: 16-07-2022
DOI: 10.3390/S22145327
Abstract: In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.
Publisher: Springer Science and Business Media LLC
Date: 20-03-2023
Publisher: Association for Computing Machinery (ACM)
Date: 28-09-2023
DOI: 10.1145/3603711
Abstract: Academics and businesses are paying intense attention to social network alignment, which centres various social networks around their shared members. All studies to date treat the social network as static and ignore its innate dynamism. In reality, an in idual's discriminative pattern is embedded in the dynamics of social networks, and this information may be used to improve social network alignment. This study finds that these dynamics can reveal more apparent patterns better suited to lining up the social web of things (SWoT). The correlation between the user structure and attributes for each social network must be maintained to combine the binary dynamics and make the original synthetic embedding representation. Finally, the initial embedding of each network is projected to a target subspace as part of the semi-supervised spatial transformation learning process. The DSNA approach outperforms the current mainstream algorithm by 10% in this paper's extensive series of trials using real-world datasets. The findings of this study show that this alignment of enormous networks addresses the volume, variety, velocity, and veracity (or 4Vs) of vast networks. To improve the efficacy and resilience of an adversarial network alignment, adversarial learning techniques can be applied. The results show that the model with structure, attribute, and time information performs the best, while the model without attribute information comes in second, the model without time information performs mediocrely, and the model without structure information performs the worst.
Publisher: Scientific Research Publishing, Inc.
Date: 2014
Publisher: Elsevier BV
Date: 02-2023
Publisher: Springer Science and Business Media LLC
Date: 12-02-2019
Publisher: Public Library of Science (PLoS)
Date: 08-2017
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11963271_19
Publisher: Springer Science and Business Media LLC
Date: 2021
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: Inderscience Publishers
Date: 2009
Location: Jordan
No related grants have been discovered for Khaled M. Matrouk.