Quantised Compressive Sampling using Genetic Algorithm in Dense Wireless Sensor Networks
dc.contributor.advisor | Gupta, O.P. | |
dc.contributor.author | Preetkamal Singh | |
dc.date.accessioned | 2016-09-18T14:37:34Z | |
dc.date.available | 2016-09-18T14:37:34Z | |
dc.date.issued | 2014 | |
dc.description.abstract | Due to increasing exchange of data in services such as internet, e-mail and data file transfer; wireless networking has gone through an exponential growth. The wireless senor network is made of small or large number of tiny nodes which have limited battery power. Source node can easily route the data to destination if it has sufficient battery power. If the destination node is far away from the source node, then the source node should have large battery power to transmit data. But after few transmissions a threshold level comes, when that source node is dead and no node is present for transmission. And the overall lifetime of network will decrease. In wireless sensor network, the nodes have limited battery capacity and limited initial energy that is consumed at different rates. The lifetime of network is defined as the time until the first node fails. Some of the major challenges in wireless sensor networks are to minimize the energy consumption, end- to- end delay and to increase the throughput of sensor node to enhance the lifetime of sensor network. The routing protocols have great impact on the lifetime of sensor network. During research work, the objective has been confined to increase the lifetime of the network using the HUFFMAN’s data compression technique and then Genetic Algorithm is used further to transmit data to the Base Station. It has been found that network lifetime is highly enhanced in terms of increased number of rounds. The data compression is being applied on the cluster head which aggregates the data being collected from all other sensor nodes. So in this way the big amount of redundant data is being removed at the cluster head level. The simulation has been performed in MATLAB software. The work is being performed in the homogeneous environment. The balance in the energy consumption is also achieved by performing this data compression technique. Because it reduces the extra load on the cluster head which makes its life much longer as compare to earlier one. In the future, the work will be focused to consider heterogeneous network and mobility of sink can also be introduced to investigate the performance of proposed technique. | en_US |
dc.identifier.uri | http://krishikosh.egranth.ac.in/handle/1/77285 | |
dc.language.iso | en | en_US |
dc.publisher | Punjab Agricultural University, Ludhiana | en_US |
dc.sub | Computer Science and Engineering | |
dc.subject | Wireless Sensor Networks | en_US |
dc.subject | biological phenomena, developmental stages, protocols (treaties), area, fruits, selection, harvesting, manpower, environment, marketing | en_US |
dc.subject | WSN | en_US |
dc.subject | genetic algorithm | en_US |
dc.these.type | M.Tech | |
dc.title | Quantised Compressive Sampling using Genetic Algorithm in Dense Wireless Sensor Networks | en_US |
dc.type | Thesis | en_US |
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