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008			230411t20222022my a|||f |||| 000 0 eng d
020			_aTHE0009593 (Local) _qHardback
040			_aUMP _beng _cUMP _erda
090			_aFKOM .S53 2022 r Thesis
100	1		_aMuhammad Shahkhir Bin Mozamir, _eauthor.
245	1	0	_aAn improved gbln-pso algorithm for indoor localization problem in wireless sensor network / _cMuhammad Shahkhir Bin Mozamir
264		1	_aKuantan, Pahang : _bUMP, _c2022
264		4	_c©2022
300			_axvi, 115 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
337			_2rdamedia _acomputer
338			_2rdacarrier _avolume
338			_2rdacarrier _acomputer disc
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2022
504			_aIncludes bibliographical references
520	3		_aWireless Sensor Network (WSN) has become an important field of research. WSN consists of a group of wireless nodes connected between an anchor and unknown nodes. These wireless nodes have the capability to sense the surroundings, process the information and communicate with other nodes wirelessly. The challenging matter in WSN is to estimate the position of the unknown nodes, where there is the error in the distance calculation between nodes. The error in distance estimation phase, caused by noise in range measurement, effects the process of node location. Therefore, the best technique of localization to measure the position of unknown node is required. This study aims to increase the accuracy of node estimation and to minimize time taken for the node localization process. To achieve the stated aims, we implemented an Improved Global best Local Neigborhood Particle Swarm Optimization (IGbLN-PSO) algorithm. IGbLN-PSO algorithm, which is originally from GbLN-PSO algorithm, was applied in previous research into the object tracking problem and it was proved can gain high accuracy and lower the computational time. However, GbLN-PSO searching mechanism must be enhanced when applied into localization problem. This is because the neighbor particles keep searching in the same search space along the main particle’s journey without calculating the optimum value around main particles. This makes the particle calculate the same value, and it may become trapped, while there is the possibility of optimum value around the main particle. Thus, we improved GbLN-PSO, known as IGbLN-PSO algorithm, where the neighbor particles are distributed around the main particle in every iteration to localize unknown node positions. Then, we compared the result with Particle Swarm Optimization (PSO), Differential Evolution Particle Swarm Optimization (DEPSO), Health Particle Swarm Optimization (HPSO) and Global best Local Neigborhood Particle Swarm Optimization (GbLN-PSO) algorithm. The experiment is set to localize forty (40) unknown nodes in 100 × 100 meter area. Three anchors were implemented and the experiments have shown that the accuracy result is competitive where IGbLN-PSO increased 0.3% and 1.5% compared to GbLN-PSO and others, respectively. For result computational time, IGbLN-PSO recorded an increased of 88.88%, 90.99%, 89.75% and 20.49% compared to PSO, DEPSO, HPSO and GbLN-PSO, respectively
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS