Design and Performance Analysis of Energy Efficient Technique for Wireless Multimedia Sensor Networks using Machine Learning Algorithms
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Akalu, Kibrewerk | |
| dc.date.accessioned | 2018-06-28T08:16:30Z | |
| dc.date.accessioned | 2023-11-04T15:14:58Z | |
| dc.date.available | 2018-06-28T08:16:30Z | |
| dc.date.available | 2023-11-04T15:14:58Z | |
| dc.date.issued | 2011-02 | |
| dc.description.abstract | Wireless multimedia sensor networks (WMSNs) are developed from wireless sensor networks (WSNs) for acquiring and transmitting multimedia data such as images, audio and video streams and scalar data. Energy is the most critical factor in sensor networks. Its power requirement is satisfied by low capacity and low power battery. One of the reasons is the requirement of unattended operation in remote or even potentially hostile locations, sensor networks are extremely energy-limited. This constraint demands that techniques must not only be efficient but energy conscious as well, which requires new approach to addressing the common but substantial issues. Reduction of communicated multimedia volume is an important step to reduce energy consumption in WMSNs because of the relatively huge amount of data collected by the nodes compared with scalar sensors. One of the algorithms in machine learning which can reduce the dimensionality is unsupervised learning Artificial Neural Networks which typically perform dimensionality reduction through pattern clustering. In this thesis, an attempt has been made to reduce the amount of transmitted information in WMSNs using vector quantization technique using Self Organizing Map (SOM) algorithm in order to increase the lifetime of the network. In the Proposed Design, SOM is used to generate a codebook using single image and batch image training methods. An energy model has been designed to calculate the lifetime of the nodes taking into consideration the computational energy cost and communication energy cost. Using this energy model, the codebook size has been optimized to a size of 50 codewords through which the network lifetime has shown an increase of 158.03 percentages compared to the existing design. This amount of increase in the lifetime of the WMSNs is on a graceful-tradeoff with the image quality since the main purpose of sensor networks is the occurrence or non occurrence of things of interest rather than on excellence of image quality considering a surveillance which is the main application for the deployment of the sensors. Keywords – Wireless Sensor Networks, Wireless Multimedia Sensor Networks, Machine Leaning Algorithms, Self Organizing Map, Codebook. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/4531 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Wireless Sensor Networks | en_US |
| dc.subject | Wireless Multimedia Sensor Networks | en_US |
| dc.subject | Machine Leaning Algorithms | en_US |
| dc.subject | Self Organizing Map | en_US |
| dc.subject | Codebook | en_US |
| dc.title | Design and Performance Analysis of Energy Efficient Technique for Wireless Multimedia Sensor Networks using Machine Learning Algorithms | en_US |
| dc.type | Thesis | en_US |