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Title:	Node localization technique in cooperative multiple-in multiple-out using adaptive mutation Artificial Bee Colony for long term evolution
Authors:	Fadhil Taher Alawe (P59549)
Supervisor:	Mahamood Ismail, Prof. Dr.
Keywords:	Universiti Kebangsaan Malaysia -- Dissertations Dissertations, Academic -- Malaysia Wireless localization Wireless communication systems MIMO systems Algorithm
Issue Date:	1-Oct-2020
Description:	Node Localization is the basis for many location dependent applications in Wireless Sensor Network (WSN) to determine the position of a sensor node assisted by known anchor nodes positions. Although Global Positioning System is the simplest method for localization of nodes, but it becomes complex if large number of nodes exists in a given network. Moreover, other algorithms such as range-based and range-free are application specific and most of the solutions are not suitable for wide range of WSN devices. The integration of Multiple-In-Multiple-Out (MIMO) in WSN using Cooperative-MIMO (CMIMO) devices will prolong node lifetime and increase the degrees of freedoms, thus allowing better localization accuracy since more signal sources available for channel estimation but the localization determination decision is quite slow. Furthermore, the localization accuracy algorithm also not been improved if the distances approximation and their distribution are not included in the objective function. The adoption of Swarm Intelligence (SI) algorithm such as Artificial Bee Colony (ABC) algorithm alone in node localization also may resulted in low accuracy and need longer time to converge for large sensors network. The main objective of this thesis is to propose a new node localization technique in CMIMO networks using Adaptive Mutation based Artificial Bee Colony (AMABC) algorithm. Firstly, an improved range-based node localization algorithm been proposed by selecting appropriate sensing nodes and decision metrics. Then, AMABC algorithm been integrated with multilateration localization determination technique by introducing adaptiveness and making mutation operation into ABC algorithm to improve localization accuracy. The proposed algorithms been validated by simulating a CMIMO scenario with four anchor nodes placed at the corners of simulation area of 100 m x100 m. The localization errors are evaluated for the proposed AMABC against standard ABC, Partical Swarm Optimization (PSO) and Genetic Algorithm (GA) by varying the numbers of sensor nodes up to 300. Simulation results shown that as the sensor nodes increase the localization errors also reduce. Moreover, AMABC algorithm outperform other algorithms ABC PSO and GA by 10%, 25% and 38% respectively in term of localization errors. Besides, the average Received Signal Strength Indicator at the sensor nodes improved by at least 4 dB while the elapsed time reduced by 30% in compared with ABC. Moreover, the effect of CMIMO LTE on the improvement of localization provided by MIMO system where multiple antennas can be deployed on both anchors and targets nodes also been evaluated. The result show that integration AMABC with CMIMO LTE at the anchor node increase the Signal-to-Noise Ratio and reduce the Bit Eror Rate, thus improving in the localization errors.,Ph.D.
Pages:	166
Call Number:	TK5103.4895.A433 2020 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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