An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks
Summary Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping...
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| Veröffentlicht in: | International journal of communication systems 2018-07, Vol.31 (10), p.n/a |
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| creator | Khanmohammadi, Sohrab Gharajeh, Mohammad Samadi |
| description | Summary
Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.
Make various clusters by LEACH‐improved and a proposed SVM‐based mechanism. Manage overlapping conditions over the network by a proposed fuzzy inference system. Change cluster heads of all clusters by a suggested utility function. |
| doi_str_mv | 10.1002/dac.3577 |
| format | Article |
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Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.
Make various clusters by LEACH‐improved and a proposed SVM‐based mechanism. Manage overlapping conditions over the network by a proposed fuzzy inference system. Change cluster heads of all clusters by a suggested utility function.</description><identifier>ISSN: 1074-5351</identifier><identifier>EISSN: 1099-1131</identifier><identifier>DOI: 10.1002/dac.3577</identifier><language>eng</language><publisher>Chichester: Wiley Subscription Services, Inc</publisher><subject>Clustering ; Communications traffic ; Data management ; Energy consumption ; fuzzy logic ; Fuzzy systems ; Manufacturing ; multicriteria decision making (MCDM) ; overlapping management ; Packet transmission ; Radio equipment ; Recovery time ; Remote sensors ; Sensors ; support vector machine (SVM) ; Support vector machines ; Wireless networks ; Wireless sensor networks ; wireless sensor networks (WSNs)</subject><ispartof>International journal of communication systems, 2018-07, Vol.31 (10), p.n/a</ispartof><rights>Copyright © 2018 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2937-984b07202bbb2d4d4a8a6f6e3dd9f53b2c2df15127fb8e058aae9f754424ef093</citedby><cites>FETCH-LOGICAL-c2937-984b07202bbb2d4d4a8a6f6e3dd9f53b2c2df15127fb8e058aae9f754424ef093</cites><orcidid>0000-0002-2727-7742</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fdac.3577$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fdac.3577$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Khanmohammadi, Sohrab</creatorcontrib><creatorcontrib>Gharajeh, Mohammad Samadi</creatorcontrib><title>An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks</title><title>International journal of communication systems</title><description>Summary
Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.
Make various clusters by LEACH‐improved and a proposed SVM‐based mechanism. Manage overlapping conditions over the network by a proposed fuzzy inference system. Change cluster heads of all clusters by a suggested utility function.</description><subject>Clustering</subject><subject>Communications traffic</subject><subject>Data management</subject><subject>Energy consumption</subject><subject>fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Manufacturing</subject><subject>multicriteria decision making (MCDM)</subject><subject>overlapping management</subject><subject>Packet transmission</subject><subject>Radio equipment</subject><subject>Recovery time</subject><subject>Remote sensors</subject><subject>Sensors</subject><subject>support vector machine (SVM)</subject><subject>Support vector machines</subject><subject>Wireless networks</subject><subject>Wireless sensor networks</subject><subject>wireless sensor networks (WSNs)</subject><issn>1074-5351</issn><issn>1099-1131</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kE1OwzAQRi0EEqUgcQRLbNik2I5dJ8uq_EqV2MDacuJxldbYwU6JuuMInJGTkFC2rOYb6emb0UPokpIZJYTdGF3PciHlEZpQUpYZpTk9HrPkmcgFPUVnKW0IIQWbiwnSC48b34FzzRp8h7U3eOtD78Cs4fvzq9IJDA4fEJ1u28avce12qYM4xjaGLtTBYRsi7psIDlLCCXwadg9dH-I2naMTq12Ci785Ra_3dy_Lx2z1_PC0XKyympW5zMqCV0QywqqqYoYbrgs9t3PIjSmtyCtWM2OpoEzaqgAiCq2htFJwzjhYUuZTdHXoHb5630Hq1Cbsoh9OKka4zEtJBRmo6wNVx5BSBKva2LzpuFeUqFGgGgSqUeCAZge0bxzs_-XU7WL5y_8ABbx0PQ</recordid><startdate>20180710</startdate><enddate>20180710</enddate><creator>Khanmohammadi, Sohrab</creator><creator>Gharajeh, Mohammad Samadi</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2727-7742</orcidid></search><sort><creationdate>20180710</creationdate><title>An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks</title><author>Khanmohammadi, Sohrab ; Gharajeh, Mohammad Samadi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2937-984b07202bbb2d4d4a8a6f6e3dd9f53b2c2df15127fb8e058aae9f754424ef093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Clustering</topic><topic>Communications traffic</topic><topic>Data management</topic><topic>Energy consumption</topic><topic>fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Manufacturing</topic><topic>multicriteria decision making (MCDM)</topic><topic>overlapping management</topic><topic>Packet transmission</topic><topic>Radio equipment</topic><topic>Recovery time</topic><topic>Remote sensors</topic><topic>Sensors</topic><topic>support vector machine (SVM)</topic><topic>Support vector machines</topic><topic>Wireless networks</topic><topic>Wireless sensor networks</topic><topic>wireless sensor networks (WSNs)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khanmohammadi, Sohrab</creatorcontrib><creatorcontrib>Gharajeh, Mohammad Samadi</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of communication systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khanmohammadi, Sohrab</au><au>Gharajeh, Mohammad Samadi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks</atitle><jtitle>International journal of communication systems</jtitle><date>2018-07-10</date><risdate>2018</risdate><volume>31</volume><issue>10</issue><epage>n/a</epage><issn>1074-5351</issn><eissn>1099-1131</eissn><abstract>Summary
Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.
Make various clusters by LEACH‐improved and a proposed SVM‐based mechanism. Manage overlapping conditions over the network by a proposed fuzzy inference system. Change cluster heads of all clusters by a suggested utility function.</abstract><cop>Chichester</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/dac.3577</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-2727-7742</orcidid></addata></record> |
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| language | eng |
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| subjects | Clustering Communications traffic Data management Energy consumption fuzzy logic Fuzzy systems Manufacturing multicriteria decision making (MCDM) overlapping management Packet transmission Radio equipment Recovery time Remote sensors Sensors support vector machine (SVM) Support vector machines Wireless networks Wireless sensor networks wireless sensor networks (WSNs) |
| title | An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks |
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