Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing.
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| Title: | Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing. |
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| Authors: | Thakur, Rahul1 (AUTHOR) rahult.cs.21@nitj.ac.in, Sikka, Geeta2 (AUTHOR) sikkag@nitdelhi.ac.in, Bansal, Urvashi1 (AUTHOR) urvashi@nitj.ac.in, Giri, Jayant3,4 (AUTHOR) jayantpgiri@gmail.com, Mallik, Saurav5 (AUTHOR) sauravmtech2@gmail.com |
| Source: | Multimedia Tools & Applications. May2025, Vol. 84 Issue 15, p14359-14386. 28p. |
| Subjects: | Artificial intelligence, Visual programming languages (Computer science), Production scheduling, Image processing, Cloud computing |
| Abstract: | Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were implemented in C + + programming language using the Visual Studio IDE. To make sure the results were very reliable, we did each experiment 30 times and then shared the average outcomes. Comparisons across dynamic and static task scenarios consistently favor FuzzyPSG-M and PSG-M, with alpha set to 0.44. These algorithms outperform others in terms of satisfied deadlines, EC, penalties, and makespan. FuzzyPSG-M exhibits a slight edge over PSG-M, attributed to its multistart procedure. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185424311 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Thakur%2C+Rahul%22">Thakur, Rahul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rahult.cs.21@nitj.ac.in</i><br /><searchLink fieldCode="AR" term="%22Sikka%2C+Geeta%22">Sikka, Geeta</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sikkag@nitdelhi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bansal%2C+Urvashi%22">Bansal, Urvashi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> urvashi@nitj.ac.in</i><br /><searchLink fieldCode="AR" term="%22Giri%2C+Jayant%22">Giri, Jayant</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> jayantpgiri@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mallik%2C+Saurav%22">Mallik, Saurav</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> sauravmtech2@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. May2025, Vol. 84 Issue 15, p14359-14386. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+programming+languages+%28Computer+science%29%22">Visual programming languages (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were implemented in C + + programming language using the Visual Studio IDE. To make sure the results were very reliable, we did each experiment 30 times and then shared the average outcomes. Comparisons across dynamic and static task scenarios consistently favor FuzzyPSG-M and PSG-M, with alpha set to 0.44. These algorithms outperform others in terms of satisfied deadlines, EC, penalties, and makespan. FuzzyPSG-M exhibits a slight edge over PSG-M, attributed to its multistart procedure. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-19509-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 14359 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Visual programming languages (Computer science) Type: general – SubjectFull: Production scheduling Type: general – SubjectFull: Image processing Type: general – SubjectFull: Cloud computing Type: general Titles: – TitleFull: Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Thakur, Rahul – PersonEntity: Name: NameFull: Sikka, Geeta – PersonEntity: Name: NameFull: Bansal, Urvashi – PersonEntity: Name: NameFull: Giri, Jayant – PersonEntity: Name: NameFull: Mallik, Saurav IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 15 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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