Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things
Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-ba...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8701626/ |
| _version_ | 1819120391597064192 |
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| author | Farooq Aftab Ali khan Zhongshan Zhang |
| author_facet | Farooq Aftab Ali khan Zhongshan Zhang |
| author_sort | Farooq Aftab |
| collection | DOAJ |
| description | Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm. |
| first_indexed | 2024-12-22T06:19:55Z |
| format | Article |
| id | doaj.art-7e72a97e319449ad86c6da1c85a3a4e5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T06:19:55Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7e72a97e319449ad86c6da1c85a3a4e52022-12-21T18:35:59ZengIEEEIEEE Access2169-35362019-01-017562175622710.1109/ACCESS.2019.29139128701626Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of ThingsFarooq Aftab0https://orcid.org/0000-0002-2939-466XAli khan1https://orcid.org/0000-0002-2695-3307Zhongshan Zhang2School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaNetwork management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm.https://ieeexplore.ieee.org/document/8701626/self-organizationclusteringInternet of Dronesrouting |
| spellingShingle | Farooq Aftab Ali khan Zhongshan Zhang Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things IEEE Access self-organization clustering Internet of Drones routing |
| title | Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things |
| title_full | Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things |
| title_fullStr | Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things |
| title_full_unstemmed | Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things |
| title_short | Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things |
| title_sort | hybrid self organized clustering scheme for drone based cognitive internet of things |
| topic | self-organization clustering Internet of Drones routing |
| url | https://ieeexplore.ieee.org/document/8701626/ |
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