Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing
Recently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/2998385 |
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| author | Ahmed. A. A. Gad-Elrab Amin Y. Noaman |
| author_facet | Ahmed. A. A. Gad-Elrab Amin Y. Noaman |
| author_sort | Ahmed. A. A. Gad-Elrab |
| collection | DOAJ |
| description | Recently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for data filtering and aggregation. Each user may collect multiple data types in mobile collective sensing. For facilitating data aggregation, the same data type carried by various users is assumed to be uploaded to the same mobile edge server. The main problem is determining the server which should be activated to process each data type for reducing the overall cost. In this paper, the problem is formulated as one form of the unqualified multicommodity facility location problem. To solve this problem, two edge-server location strategies are proposed, which use a clustering method for dividing the set of mobile users with data items into clusters and use the ant colony approach to select a mobile edge server for each data type in each cluster. Extensive simulations are conducted based on widely used real data sets. The simulation results show that the proposed strategy achieves better performance than the existing methods in terms of service and facility costs. |
| format | Article |
| id | doaj-art-3571e6c352f64397b5b46e5d19b8acea |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3571e6c352f64397b5b46e5d19b8acea2025-02-03T05:57:38ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/2998385Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile CrowdsensingAhmed. A. A. Gad-Elrab0Amin Y. Noaman1Faculty of Computing and Information TechnologyFaculty of Computing and Information TechnologyRecently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for data filtering and aggregation. Each user may collect multiple data types in mobile collective sensing. For facilitating data aggregation, the same data type carried by various users is assumed to be uploaded to the same mobile edge server. The main problem is determining the server which should be activated to process each data type for reducing the overall cost. In this paper, the problem is formulated as one form of the unqualified multicommodity facility location problem. To solve this problem, two edge-server location strategies are proposed, which use a clustering method for dividing the set of mobile users with data items into clusters and use the ant colony approach to select a mobile edge server for each data type in each cluster. Extensive simulations are conducted based on widely used real data sets. The simulation results show that the proposed strategy achieves better performance than the existing methods in terms of service and facility costs.http://dx.doi.org/10.1155/2022/2998385 |
| spellingShingle | Ahmed. A. A. Gad-Elrab Amin Y. Noaman Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing Applied Computational Intelligence and Soft Computing |
| title | Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing |
| title_full | Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing |
| title_fullStr | Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing |
| title_full_unstemmed | Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing |
| title_short | Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing |
| title_sort | clustering ant colony based edge server location strategy in mobile crowdsensing |
| url | http://dx.doi.org/10.1155/2022/2998385 |
| work_keys_str_mv | AT ahmedaagadelrab clusteringantcolonybasededgeserverlocationstrategyinmobilecrowdsensing AT aminynoaman clusteringantcolonybasededgeserverlocationstrategyinmobilecrowdsensing |