A wireless interference-aware internet-of-things gateway placement framework with genetic algorithm approach
IoT (Internet-of-Things) gateways are deployed together with sensor nodes to facilitate manageability, and operational cost of the IoT system. Gateway placement optimization is implemented to strategically placing the IoT gateways, aiming to fulfil different technical requirements on top of minim...
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| Format: | Final Year Project / Dissertation / Thesis |
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2022
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| Online Access: | http://eprints.utar.edu.my/6353/1/CEA_2022_KZW_%2D_1506682.pdf http://eprints.utar.edu.my/6353/ |
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| _version_ | 1833429073672536064 |
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| author | Kong, Zan Wai |
| author_facet | Kong, Zan Wai |
| author_sort | Kong, Zan Wai |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | IoT (Internet-of-Things) gateways are deployed together with sensor
nodes to facilitate manageability, and operational cost of the IoT system.
Gateway placement optimization is implemented to strategically placing the IoT
gateways, aiming to fulfil different technical requirements on top of minimizing
the number of gateway. However, there is no existing gateway placement
scheme that considers all the factors of number of gateways, sensor nodes
coverage, lateral bound (inter-gateway) connections, redundancy for fault
tolerance and dynamic changes of sensor nodes’ location.
Therefore, this work proposes a framework to optimized gateway
placement that considers all the aforementioned factors. The solution takes the
layout of sensor nodes as input and generates a set of proposed IoT gateway
locations. The framework generates the solution using genetic algorithm. Our
experimental results show that solution can be generated with relatively low
processing power even for a relatively wide search space. One of the
contributions of this work is the formalization of the fitness function for genetic
algorithm.
A series of simulations were designed and carried out to benchmark our
framework against existing solutions with different evaluation criteria based on
the consideration factors. Our framework gave promising results in terms of
lower wireless network overlapping, minimized number of gateways required to
cover all sensor nodes without compromising redundancies for fault-tolerance,
and shorter overall distance of gateway movements required during the
relocation due to the change of sensor nodes layout. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6353 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2022 |
| record_format | eprints |
| spelling | my-utar-eprints.63532024-05-23T10:29:31Z A wireless interference-aware internet-of-things gateway placement framework with genetic algorithm approach Kong, Zan Wai T Technology (General) TD Environmental technology. Sanitary engineering IoT (Internet-of-Things) gateways are deployed together with sensor nodes to facilitate manageability, and operational cost of the IoT system. Gateway placement optimization is implemented to strategically placing the IoT gateways, aiming to fulfil different technical requirements on top of minimizing the number of gateway. However, there is no existing gateway placement scheme that considers all the factors of number of gateways, sensor nodes coverage, lateral bound (inter-gateway) connections, redundancy for fault tolerance and dynamic changes of sensor nodes’ location. Therefore, this work proposes a framework to optimized gateway placement that considers all the aforementioned factors. The solution takes the layout of sensor nodes as input and generates a set of proposed IoT gateway locations. The framework generates the solution using genetic algorithm. Our experimental results show that solution can be generated with relatively low processing power even for a relatively wide search space. One of the contributions of this work is the formalization of the fitness function for genetic algorithm. A series of simulations were designed and carried out to benchmark our framework against existing solutions with different evaluation criteria based on the consideration factors. Our framework gave promising results in terms of lower wireless network overlapping, minimized number of gateways required to cover all sensor nodes without compromising redundancies for fault-tolerance, and shorter overall distance of gateway movements required during the relocation due to the change of sensor nodes layout. 2022-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6353/1/CEA_2022_KZW_%2D_1506682.pdf Kong, Zan Wai (2022) A wireless interference-aware internet-of-things gateway placement framework with genetic algorithm approach. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/6353/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Kong, Zan Wai A wireless interference-aware internet-of-things gateway placement framework with genetic algorithm approach |
| title | A wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach
|
| title_full | A wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach
|
| title_fullStr | A wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach
|
| title_full_unstemmed | A wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach
|
| title_short | A wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach
|
| title_sort | wireless interference-aware internet-of-things gateway
placement framework with genetic algorithm approach |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6353/1/CEA_2022_KZW_%2D_1506682.pdf http://eprints.utar.edu.my/6353/ |
| url_provider | http://eprints.utar.edu.my |