Genetic algorithm based adaptive offloading for improving IoT device communication efficiency
Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature Switzerland AG
2019
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/27185/1/WN%2026%204%202019%201%2010.pdf |
| _version_ | 1803629260681773056 |
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| author | Hussain, Azham Manikanthan, S. V. Padmapriya, T. Nagalingam, Mahendran |
| author_facet | Hussain, Azham Manikanthan, S. V. Padmapriya, T. Nagalingam, Mahendran |
| author_sort | Hussain, Azham |
| collection | UUM |
| description | Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time. |
| first_indexed | 2024-07-04T06:35:02Z |
| format | Article |
| id | uum-27185 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:35:02Z |
| publishDate | 2019 |
| publisher | Springer Nature Switzerland AG |
| record_format | dspace |
| spelling | uum-271852020-07-09T06:09:23Z https://repo.uum.edu.my/id/eprint/27185/ Genetic algorithm based adaptive offloading for improving IoT device communication efficiency Hussain, Azham Manikanthan, S. V. Padmapriya, T. Nagalingam, Mahendran QA75 Electronic computers. Computer science Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time. Springer Nature Switzerland AG 2019 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/27185/1/WN%2026%204%202019%201%2010.pdf Hussain, Azham and Manikanthan, S. V. and Padmapriya, T. and Nagalingam, Mahendran (2019) Genetic algorithm based adaptive offloading for improving IoT device communication efficiency. Wireless Networks, 26 (4). pp. 2329-2338. ISSN 1022-0038 http://doi.org/10.1007/s11276-019-02121-4 doi:10.1007/s11276-019-02121-4 doi:10.1007/s11276-019-02121-4 |
| spellingShingle | QA75 Electronic computers. Computer science Hussain, Azham Manikanthan, S. V. Padmapriya, T. Nagalingam, Mahendran Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title | Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title_full | Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title_fullStr | Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title_full_unstemmed | Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title_short | Genetic algorithm based adaptive offloading for improving IoT device communication efficiency |
| title_sort | genetic algorithm based adaptive offloading for improving iot device communication efficiency |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/27185/1/WN%2026%204%202019%201%2010.pdf |
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