A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles
The evolution in micro-electro-mechanical systems technology (MEMS) has triggered the need for the development of wireless sensor network (WSN). These wireless sensor nodes has been used in many applications at many areas. One of the main issues in WSN is the energy availability, which is always...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/740/2/24p%20ALI%20HUSAM%20ABDULHASAN%20AL-JARAH.pdf http://eprints.uthm.edu.my/740/1/ALI%20HUSAM%20ABDULHASAN%20AL-JARAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/740/3/ALI%20HUSAM%20ABDULHASAN%20AL-JARAH%20WATERMARK.pdf |
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| Summary: | The evolution in micro-electro-mechanical systems technology (MEMS) has
triggered the need for the development of wireless sensor network (WSN). These
wireless sensor nodes has been used in many applications at many areas. One of the
main issues in WSN is the energy availability, which is always a constraint. In a
previous research, a relocating algorithm for mobile sensor network had been
introduced and the goal was to save energy and prolong the lifetime of the sensor
networks using Particle Swarm Optimization (PSO) where both of sensing radius and
travelled distance had been optimized in order to save energy in long-term and shortterm.
Yet, the previous research did not take into account obstacles’ existence in the
field and this will cause the sensor nodes to consume more power if obstacles are
exists in the sensing field. In this project, the same centralized relocating algorithm
from the previous research has been used where 15 mobile sensors deployed
randomly in a field of 100 meter by 100 meter where these sensors has been
deployed one time in a field that obstacles does not exist (case 1) and another time in
a field that obstacles existence has been taken into account (case 2), in which these
obstacles has been pre-defined positions, where these two cases applied into two
different algorithms, which are the original algorithm of a previous research and the
modified algorithm of this thesis. Particle Swarm Optimization has been used in the
proposed algorithm to minimize the fitness function. Voronoi diagram has also used
in order to ensure that the mobile sensors cover the whole sensing field. In this
project, the objectives will be mainly focus on the travelling distance, which is the
mobility module, of the mobile sensors in the network because the distance that the
sensor node travels, will consume too much power from this node and this will lead
to shortening the lifetime of the sensor network. So, the travelling distance, power
consumption and lifetime of the network will be calculated in both cases for original
algorithm and modified algorithm, which is a modified deployment algorithm, and compared between them. Moreover, the maximum sensing range is calculated, which
is 30 meter, by using the binary sensing model even though the sensing module does
not consume too much power compared to the mobility module. Finally, the
comparison of the results in the original method will show that this algorithm is not
suitable for an environment where obstacle exist because sensors will consume too
much power compared to the sensors that deployed in environment that free of
obstacles. While the results of the modified algorithm of this research will be more
suitable for both environments, that is environment where obstacles are not exist and
environment where obstacles are exist, because sensors in this algorithm .will
consume almost the same amount of power at both of these environments. |
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