Enhancement of an intelligent energy-efficient clustering routing protocol for wireless sensor network /
Wireless sensor network (WSN) is a relatively new advancing technology which has opened up many possibilities in the field of remote sensing, data monitoring, wildlife migration, monitoring infernos, reconnaissance and surveillance, weather observation and pervasive computing. Recent advancements in...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Kuala Lumpur:
Kulliyyah of Engineering, International Islamic University Malaysia,
2012
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| Subjects: | |
| Online Access: | http://studentrepo.iium.edu.my/handle/123456789/4760 |
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| Summary: | Wireless sensor network (WSN) is a relatively new advancing technology which has opened up many possibilities in the field of remote sensing, data monitoring, wildlife migration, monitoring infernos, reconnaissance and surveillance, weather observation and pervasive computing. Recent advancements in nanotechnology, micro-electromechanical systems (MEMS) technology, radio technology, digital electronics, digital signal processing and wireless communications have immensely contributed to the design of miniaturized and smart sensors. Despite the usefulness and importance of WSN routing protocols for various applications, it is constrained by problems related to energy-efficient routing, optimal allocation of nodes with adequate energy resources, and effective management and configuration of the sensor nodes. Clusterbased routing protocols have been proposed to address the aforementioned issues but recently, a number of researchers pointed out that these problems are more of optimization problems. Hence a shift in paradigm by exploiting the capabilities of artificial intelligence (AI) techniques is necessary for enhancing protocols to have better chances of satisfactorily addressing the existent issues. Consequently, this thesis investigates the impact of jointly utilizing a novel GA-based cluster optimization algorithm together with a novel biased energy distribution (BED) scheme for clusterbased routing in WSN. An optimal solution which dynamically configures the network into higher and lower energy nodes has been proposed in this work. The proposed improved methodology ensures balanced energy consumption in order to maximize network lifetime by development of an analytical model that depicts the network behavior in optimizing energy allocation and cluster configuration of the sensor nodes using genetic algorithm (GA). The performance of the proposed methodology has been evaluated using OMNET++ and MATLAB and the results obtained were benchmarked against LEACH, PEGASIS AND GAF. It has been shown by simulation that the novel BED and GA-based BED (GA-BED) techniques display better performance in comparison with existing clustering routing protocols with respect to energy consumption, network lifetime and throughput. The obtained throughput, network lifetime and energy consumption performance in the novel BED scheme shows an improvement of 74.12%, 69.67% and 10.08% respectively when benchmarked against LEACH. With the novel GA-BED scheme, an improvement of 75.66%, 74.55% and 20.39% in throughput, network lifetime and energy consumption performance respectively is obtained when benchmarked against LEACH. Both BED and GA-BED display better performance than PEGASIS and GAF with respect to energy consumption, network lifetime and throughput. |
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| Item Description: | Abstract in English and Arabic. "A thesis submitted in fulfilment of the requirement for the degree of Master of Science (Computer and Information Engineering)."--On t.p. |
| Physical Description: | xvi, 126 leaves : ill. ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 120-125). |