2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment
| Format: | General Document |
|---|
| _version_ | 1860798287148220416 |
|---|---|
| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2024-10-24 10:04 |
| format | General Document |
| id | 16816 |
| institution | UniSZA |
| originalfilename | 16816_45f30c4ffb6e830.pdf |
| person | Nur Khalida Binti Khalid |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16816 |
| sourcemedia | Server storage Scanned document |
| spelling | 16816 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16816 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.7 Microsoft® Word 2019 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin 144 Copyright©PWB2025 Cloud computing Dissertations, Academic Nur Khalida Binti Khalid Fuzzy systems 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment The effectiveness of decision-making techniques based on mathematical formulation is now vital in daily life. Mobile Cloud Computing (MCC) has addressed mobile hardware and software limitations. In the MCC setting, task offloading is crucial for intensive computational applications, involving deciding whether tasks should be executed locally or offloaded to a remote device. The decision-making process considers uncertainties like battery life, network signal strength, and platform privacy. Fuzzy Set Theory (Type-1 Fuzzy Set) has been used to deal with uncertain information and the environment. However, the Type-1 Fuzzy Set is unable to handle uncertainties in real-world problems because the membership value considered in this set is certain. Thus, this research proposes an improved decision-making method that helps handle uncertainty issues using the Interval Type-2 Fuzzy Set method. This research was conducted by determining the significant parameters related to offloading MCC and defining the linguistic variables' parameters, which later will be mapped to the fuzzy membership function. The parameters considered are battery level, memory storage, network signal level, and CPU clock speed. Then, the experiment was performed where each parameter defined in the membership function was analyzed in the proposed frameworks. The step began with the fuzzification, rule-based construction aggregation analysis, determining to rank, and defuzzification to get the final decision. The expert’s opinions were collected, and correlation was used to validate the final result. As a result, the improved method based on Type-2 Fuzzy Set demonstrated better decision agreement between the experts and the proposed method with the Pearson Correlation value is 0.5148. Further, for comparison purposes, the Pearson Correlation value was also calculated with the current Type-1 Fuzzy Set method with the value of 0.2199. The result shows that the proposed method of dealing with uncertainty problems during the decision-making process outperforms the existing Type-1 Fuzzy Set method. The proposed method in decision-making has achieved the objectives of better handling uncertainty in the decision-making process within offloading process. The proposed method could serve as a good decision-making tool in the MCC domain and other applications. 2024-10-24 10:04 uuid:84DA6594-BC6A-4852-A1DC-715D8B73B1E5 16816_45f30c4ffb6e830.pdf Mobile Cloud Computing (MCC) Task Offloading Interval Type-2 Fuzzy Set Fuzzy Logic Uncertainty Management Computational Intelligence Correlation Analysis Performance Evaluation Decision making—Mathematical models Algorithms—Evaluation Machine learning—Mathematical models Thesis |
| spellingShingle | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| state | Terengganu |
| subject | Cloud computing Dissertations, Academic Fuzzy systems Decision making—Mathematical models Algorithms—Evaluation Machine learning—Mathematical models |
| summary | The effectiveness of decision-making techniques based on mathematical formulation is now vital in daily life. Mobile Cloud Computing (MCC) has addressed mobile hardware and software limitations. In the MCC setting, task offloading is crucial for intensive computational applications, involving deciding whether tasks should be executed locally or offloaded to a remote device. The decision-making process considers uncertainties like battery life, network signal strength, and platform privacy. Fuzzy Set Theory (Type-1 Fuzzy Set) has been used to deal with uncertain information and the environment. However, the Type-1 Fuzzy Set is unable to handle uncertainties in real-world problems because the membership value considered in this set is certain. Thus, this research proposes an improved decision-making method that helps handle uncertainty issues using the Interval Type-2 Fuzzy Set method. This research was conducted by determining the significant parameters related to offloading MCC and defining the linguistic variables' parameters, which later will be mapped to the fuzzy membership function. The parameters considered are battery level, memory storage, network signal level, and CPU clock speed. Then, the experiment was performed where each parameter defined in the membership function was analyzed in the proposed frameworks. The step began with the fuzzification, rule-based construction aggregation analysis, determining to rank, and defuzzification to get the final decision. The expert’s opinions were collected, and correlation was used to validate the final result. As a result, the improved method based on Type-2 Fuzzy Set demonstrated better decision agreement between the experts and the proposed method with the Pearson Correlation value is 0.5148. Further, for comparison purposes, the Pearson Correlation value was also calculated with the current Type-1 Fuzzy Set method with the value of 0.2199. The result shows that the proposed method of dealing with uncertainty problems during the decision-making process outperforms the existing Type-1 Fuzzy Set method. The proposed method in decision-making has achieved the objectives of better handling uncertainty in the decision-making process within offloading process. The proposed method could serve as a good decision-making tool in the MCC domain and other applications. |
| title | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| title_full | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| title_fullStr | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| title_full_unstemmed | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| title_short | 2024_An Improved Type-2 Fuzzy Decision-Making Method For Task Offloading In A Mobile Cloud Computing Environment |
| title_sort | 2024_an improved type-2 fuzzy decision-making method for task offloading in a mobile cloud computing environment |