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https://ptsldigital.ukm.my/jspui/handle/123456789/476584Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ravie Chandren Muniyandi, Dr. | - |
| dc.contributor.author | Mustafa Ali Hameed (P84265) | - |
| dc.date.accessioned | 2023-10-06T09:21:37Z | - |
| dc.date.available | 2023-10-06T09:21:37Z | - |
| dc.date.issued | 2018-02-07 | - |
| dc.identifier.other | ukmvital:121673 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476584 | - |
| dc.description | Wireless sensor network (WSN) is a technology that is developed to address the increasing demands for observing and controlling the environment. Sensor nodes are typically distributed in different positions and collaborate to communicate information that was gathered from a region of interest (ROI) through links that connect the sensor nodes wirelessly and send this information using multi-hop communication to a base station called sink. Sensor nodes must be efficiently deployed to improve the network coverage and reduce energy consumed by these sensor nodes that essentially depends on a built-in battery. In this study, we developed an approach to find a trade-off among the conflicting objectives, which consist of maximizing coverage and minimizing energy consumption of sensor nodes, by finding Pareto optimal set of solutions to maximize network coverage and lifetime simultaneously while maintaining full connectivity between each sensor node and the sink in an environment contains restricted areas. The selected benchmark approach was developed by (Khalesian & Delavar 2016). This approach was aimed at deploying WSNs by maintaining the connectivity constraints among the sensor nodes. We implemented the benchmark using MATLAB environment to verify its results. Subsequently, population initialization and crossover operation were modified to obtain solutions that satisfy connectivity constraints and achieve the a less computational time in generating the solutions. Moreover, a constraint of dead zones was added to practically introduce deployment in the presence of restricted areas in the ROIs, such as lakes, rivers or unsafe areas, by preventing sensor nodes to be deployed in these areas. Results showed that the developed approach outperforms the previous approach in terms of the deployment objectives that depended on evaluation measures, that is, the number of non-dominated solutions, diversity metric, computation time, and Pareto front.,“Certification of Master’s / Doctoral Thesis” is not available,Master of Computer Science | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.subject | Wireless sensor networks | - |
| dc.title | Computationally effective and practically aware pareto-based multi-objective evolutionary approach for wireless sensor network deployment | - |
| dc.type | theses | - |
| dc.format.pages | 99 | - |
| dc.identifier.callno | TK7872.D48H345 2018 3 tesis | - |
| dc.identifier.barcode | 005471(2021)(PL2) | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_121673+SOURCE1+SOURCE1.0.PDF Restricted Access | 13.85 MB | Adobe PDF |  View/Open |
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