Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure
Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to deve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7416 |
| _version_ | 1797581852495577088 |
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| author | Arun Kumar Sangaiah Amir Javadpour Pedro Pinto Haruna Chiroma Lubna A. Gabralla |
| author_facet | Arun Kumar Sangaiah Amir Javadpour Pedro Pinto Haruna Chiroma Lubna A. Gabralla |
| author_sort | Arun Kumar Sangaiah |
| collection | DOAJ |
| description | Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system’s intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times. |
| first_indexed | 2024-03-10T23:13:26Z |
| format | Article |
| id | doaj.art-769ccf45854a4839b929eca5fc92af6c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:26Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-769ccf45854a4839b929eca5fc92af6c2023-11-19T08:49:34ZengMDPI AGSensors1424-82202023-08-012317741610.3390/s23177416Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing InfrastructureArun Kumar Sangaiah0Amir Javadpour1Pedro Pinto2Haruna Chiroma3Lubna A. Gabralla4International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou 64002, TaiwanDepartment of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen 150001, ChinaADiT-Lab, Electrical and Telecommunications Department, Instituto Politécnico de Viana do Castelo, 4200-319 Porto, PortugalCollege of Computer Science and Engineering, University of Hafr Al Batin, Hafar al-Batin 31991, Saudi ArabiaDepartment of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaAnswering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system’s intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times.https://www.mdpi.com/1424-8220/23/17/7416intelligent technique algorithmpeer to peerparticle optimizationapproximation queriesmobile cloud computing |
| spellingShingle | Arun Kumar Sangaiah Amir Javadpour Pedro Pinto Haruna Chiroma Lubna A. Gabralla Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure Sensors intelligent technique algorithm peer to peer particle optimization approximation queries mobile cloud computing |
| title | Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure |
| title_full | Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure |
| title_fullStr | Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure |
| title_full_unstemmed | Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure |
| title_short | Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure |
| title_sort | cost effective resources for computing approximation queries in mobile cloud computing infrastructure |
| topic | intelligent technique algorithm peer to peer particle optimization approximation queries mobile cloud computing |
| url | https://www.mdpi.com/1424-8220/23/17/7416 |
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