A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm
Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase net...
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Wiley
2024-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/jcnc/8349653 |
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| author | Hassan Sh. Alshehri Fuad Bajaber |
| author_facet | Hassan Sh. Alshehri Fuad Bajaber |
| author_sort | Hassan Sh. Alshehri |
| collection | DOAJ |
| description | Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality-of-service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well. |
| format | Article |
| id | doaj-art-476c771fd3fa4f439b06a363045d69eb |
| institution | Kabale University |
| issn | 2090-715X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-476c771fd3fa4f439b06a363045d69eb2024-12-31T05:00:04ZengWileyJournal of Computer Networks and Communications2090-715X2024-01-01202410.1155/jcnc/8349653A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization AlgorithmHassan Sh. Alshehri0Fuad Bajaber1Department of Information TechnologyDepartment of Information TechnologyData are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality-of-service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well.http://dx.doi.org/10.1155/jcnc/8349653 |
| spellingShingle | Hassan Sh. Alshehri Fuad Bajaber A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm Journal of Computer Networks and Communications |
| title | A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm |
| title_full | A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm |
| title_fullStr | A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm |
| title_full_unstemmed | A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm |
| title_short | A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm |
| title_sort | cluster based data aggregation in iot sensor networks using the firefly optimization algorithm |
| url | http://dx.doi.org/10.1155/jcnc/8349653 |
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