Topology Control in Wireless Sensor Network using Fuzzy Logic
Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.<br />Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power suppl...
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| Format: | Article |
| Language: | English |
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Science and Research Branch,Islamic Azad University
2015-05-01
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| Series: | Journal of Advances in Computer Engineering and Technology |
| Subjects: | |
| Online Access: | http://jacet.srbiau.ac.ir/article_6711_63f98c853ee43d424ed8f3ad37fe3fd9.pdf |
| _version_ | 1818855529901981696 |
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| author | Samaneh Nazari Dastjerdi Hamid Haj Seyyad Javadi |
| author_facet | Samaneh Nazari Dastjerdi Hamid Haj Seyyad Javadi |
| author_sort | Samaneh Nazari Dastjerdi |
| collection | DOAJ |
| description | Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.<br />Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network accessibility.<br />In this paper, a network is modeled by a graph. Each vertex in the graphindicatesa sensor node and the edges display the communication links between them.Changes in the graph show changes in network topology and a different path to the sink.<br />In this research, “fuzzy logic” is used for topology control. <br />As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text. <br />In both algorithms, we haveload balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other. |
| first_indexed | 2024-12-19T08:10:04Z |
| format | Article |
| id | doaj.art-5ffb6357485e42628ef062b4ad84b3ee |
| institution | Directory Open Access Journal |
| issn | 2423-4192 2423-4206 |
| language | English |
| last_indexed | 2024-12-19T08:10:04Z |
| publishDate | 2015-05-01 |
| publisher | Science and Research Branch,Islamic Azad University |
| record_format | Article |
| series | Journal of Advances in Computer Engineering and Technology |
| spelling | doaj.art-5ffb6357485e42628ef062b4ad84b3ee2022-12-21T20:29:39ZengScience and Research Branch,Islamic Azad UniversityJournal of Advances in Computer Engineering and Technology2423-41922423-42062015-05-011239446711Topology Control in Wireless Sensor Network using Fuzzy LogicSamaneh Nazari Dastjerdi0Hamid Haj Seyyad Javadi1Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Mathematics and Applications, Shahed UniversityNetwork sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.<br />Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network accessibility.<br />In this paper, a network is modeled by a graph. Each vertex in the graphindicatesa sensor node and the edges display the communication links between them.Changes in the graph show changes in network topology and a different path to the sink.<br />In this research, “fuzzy logic” is used for topology control. <br />As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text. <br />In both algorithms, we haveload balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other.http://jacet.srbiau.ac.ir/article_6711_63f98c853ee43d424ed8f3ad37fe3fd9.pdfWireless Sensor NetworkTopology controlFuzzy Logic |
| spellingShingle | Samaneh Nazari Dastjerdi Hamid Haj Seyyad Javadi Topology Control in Wireless Sensor Network using Fuzzy Logic Journal of Advances in Computer Engineering and Technology Wireless Sensor Network Topology control Fuzzy Logic |
| title | Topology Control in Wireless Sensor Network using Fuzzy Logic |
| title_full | Topology Control in Wireless Sensor Network using Fuzzy Logic |
| title_fullStr | Topology Control in Wireless Sensor Network using Fuzzy Logic |
| title_full_unstemmed | Topology Control in Wireless Sensor Network using Fuzzy Logic |
| title_short | Topology Control in Wireless Sensor Network using Fuzzy Logic |
| title_sort | topology control in wireless sensor network using fuzzy logic |
| topic | Wireless Sensor Network Topology control Fuzzy Logic |
| url | http://jacet.srbiau.ac.ir/article_6711_63f98c853ee43d424ed8f3ad37fe3fd9.pdf |
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