Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks
Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in...
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Gespeichert in:
| Autor*in: |
Senthil Kumar, K. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 90(2015), 2 vom: 18. Nov., Seite 609-623 |
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| Übergeordnetes Werk: |
volume:90 ; year:2015 ; number:2 ; day:18 ; month:11 ; pages:609-623 |
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| DOI / URN: |
10.1007/s11277-015-3130-7 |
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| 520 | |a Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. | ||
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10.1007/s11277-015-3130-7 doi (DE-627)OLC2053799218 (DE-He213)s11277-015-3130-7-p DE-627 ger DE-627 rakwb eng 620 VZ Senthil Kumar, K. verfasserin aut Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. Wireless multi-hop network Search techniques Dynamic optimization problem Ramkumar, D. aut Enthalten in Wireless personal communications Springer US, 1994 90(2015), 2 vom: 18. Nov., Seite 609-623 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:90 year:2015 number:2 day:18 month:11 pages:609-623 https://doi.org/10.1007/s11277-015-3130-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 90 2015 2 18 11 609-623 |
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10.1007/s11277-015-3130-7 doi (DE-627)OLC2053799218 (DE-He213)s11277-015-3130-7-p DE-627 ger DE-627 rakwb eng 620 VZ Senthil Kumar, K. verfasserin aut Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. Wireless multi-hop network Search techniques Dynamic optimization problem Ramkumar, D. aut Enthalten in Wireless personal communications Springer US, 1994 90(2015), 2 vom: 18. Nov., Seite 609-623 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:90 year:2015 number:2 day:18 month:11 pages:609-623 https://doi.org/10.1007/s11277-015-3130-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 90 2015 2 18 11 609-623 |
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10.1007/s11277-015-3130-7 doi (DE-627)OLC2053799218 (DE-He213)s11277-015-3130-7-p DE-627 ger DE-627 rakwb eng 620 VZ Senthil Kumar, K. verfasserin aut Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. Wireless multi-hop network Search techniques Dynamic optimization problem Ramkumar, D. aut Enthalten in Wireless personal communications Springer US, 1994 90(2015), 2 vom: 18. Nov., Seite 609-623 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:90 year:2015 number:2 day:18 month:11 pages:609-623 https://doi.org/10.1007/s11277-015-3130-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 90 2015 2 18 11 609-623 |
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10.1007/s11277-015-3130-7 doi (DE-627)OLC2053799218 (DE-He213)s11277-015-3130-7-p DE-627 ger DE-627 rakwb eng 620 VZ Senthil Kumar, K. verfasserin aut Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. Wireless multi-hop network Search techniques Dynamic optimization problem Ramkumar, D. aut Enthalten in Wireless personal communications Springer US, 1994 90(2015), 2 vom: 18. Nov., Seite 609-623 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:90 year:2015 number:2 day:18 month:11 pages:609-623 https://doi.org/10.1007/s11277-015-3130-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 90 2015 2 18 11 609-623 |
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Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. © Springer Science+Business Media New York 2015 |
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Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. © Springer Science+Business Media New York 2015 |
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Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes. © Springer Science+Business Media New York 2015 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2053799218</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504075221.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11277-015-3130-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2053799218</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11277-015-3130-7-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Senthil Kumar, K.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless multi-hop network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Search techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic optimization problem</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramkumar, D.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wireless personal communications</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">90(2015), 2 vom: 18. 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