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| 008 | 201019s2017 th uu m rtt 0| a1eng d | ||
| 035 | _a.b12354387 | ||
| 099 | 9 | _aAIT Diss. no.ICT-17-01 | |
| 100 | 1 | _aBayu, Augustinus Primawan | |
| 245 | 1 | 0 | _aGis-based optimization framework for WLAN access point placement using multi-objective genetic algorithm |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2017 |
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| 300 |
_a1 online resource, 74 leaves : _bill. |
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| 490 | 1 |
_aDissertation ; _vno. ICT-17-01 |
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| 500 | _aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information and Communications Technologies, School of Engineering and Technology | ||
| 502 | _aThesis (Ph.D.) - Asian Institute of Technology, 2017 | ||
| 520 | _aDesigning network deployment requires a specific skill in order to perform effective planning. In the study of wireless networking, nowadays, the design-and-adjustment approach has replaced experience-based approach. Applying optimization technique on network planning will reduce cost and time in comparison with trial and error technique. In this case, GIS spatial analysis will be useful to perform prediction of coverage and signal strength. Integrating spatial data analysis and programming technique can then lead to improvement of wireless network design. Combining priority location solution and the evolutionary algorithm will provide a solution for an optimum access point location. This approach gives an appropriate solution in design and evaluation. This technique of access point placement has been successfully implemented. Location priority model was affected by the type of building and location placement but not by a number of user access. Signal prediction technique based on the empirical model was found to be better than the classical and cost231Hatta model. However, cost231Hatta gives the best solution for the placement problem with 35-meters average distance error. The optimization result from the proposed solution gives a maximum coverage area with minimum access point number, which is genetic algorithm gave optimum solution for 1000 of generations, 1.0 of crossover rate and 0.03 of mutation rate. The case study showed that the number of access points went down by 7%, while the coverage area went up by 32%. This meant that the coverage index grew up for 41% and the area with high-level RSSI by 41% respectively. Meanwhile, Kriging method will give realistic visualization on coverage signal strength of the optimum access point placement. The research contribution was developed a framework solution for access point placement problem with spatial optimization based on location priority. Which was defined as follows: {uF0B7} Giving a model to find priority location in access point placement; {uF0B7} Visualizing the coverage area and management with signal strength prediction model based on GIS; {uF0B7} Giving solution with multi-objective genetic algorithm (MO-GA) approach for the problem of access point placement and showcasing that genetic algorithm is a feasible technique for optimizing access point placement. The framework of optimization technique consists of several processes, such as location priority, node connection, an optimizing node of the access point location, and coverage strength prediction. Future research will apply the framework for wireless position and allocation in the network design. | ||
| 650 | 0 | _aGeographic information systems | |
| 650 | 0 | _aWirless communication systems | |
| 700 | 1 |
_aTripathi, Nitin Kumar, _eChairperson |
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| 700 | 0 |
_aTeerapat Sanguankotchakorn, _eExamination Committee |
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| 700 | 0 |
_aSarawut Ninsawat, _eExamination committee |
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| 710 | 2 |
_aDirectorate General of Higher Education (DIKTI), Indonesia, _eScholarship donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tDissertations ; _vno. ICT-17-01 |
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| 856 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B08713 |
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