A lightweight trust management based on Bayesian and Entropy for wireless sensor networks
ABSTRACT With the rapid development of wireless sensor networks, the issue of designing a reasonable trust management has attracted more and more research attention. Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. First, the evaluated n...
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| Veröffentlicht in: | Security and communication networks 2015-01, Vol.8 (2), p.168-175 |
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| creator | Che, Shenyun Feng, Renjian Liang, Xuan Wang, Xiao |
| description | ABSTRACT
With the rapid development of wireless sensor networks, the issue of designing a reasonable trust management has attracted more and more research attention. Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. First, the evaluated node's direct trust value is calculated by Bayesian and periodically updated according to the combination of effective history records and adaptive decay factor. We use effective history records rather than all the records to save nodes’ memory, and the adaptive decay factor enhances the algorithm's accuracy and dynamic. Then, according to the confidence level of the direct trust value, we decide whether the direct trust is credible enough to be the integrated trust. This can reduce the energy computation and make the algorithm lightweight. Last, if the direct trust is not credible enough, the overall indirect trust value will be calculated. The Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model. Simulation experiments are provided to assess the performance of the proposed trust management in terms of attack–defeat ability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.
Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. Lightweight trust management based on Bayesian and Entropy adopts two strategies to make the model lightweight. One is using effective history records rather than all the records when updating trust value, which saves nodes' memory. The other is using indirect trust only when the direct trust is not credible enough to be the integrated trust, which reduces the network energy consumption. In addition, the Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model. |
| doi_str_mv | 10.1002/sec.969 |
| format | Article |
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With the rapid development of wireless sensor networks, the issue of designing a reasonable trust management has attracted more and more research attention. Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. First, the evaluated node's direct trust value is calculated by Bayesian and periodically updated according to the combination of effective history records and adaptive decay factor. We use effective history records rather than all the records to save nodes’ memory, and the adaptive decay factor enhances the algorithm's accuracy and dynamic. Then, according to the confidence level of the direct trust value, we decide whether the direct trust is credible enough to be the integrated trust. This can reduce the energy computation and make the algorithm lightweight. Last, if the direct trust is not credible enough, the overall indirect trust value will be calculated. The Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model. Simulation experiments are provided to assess the performance of the proposed trust management in terms of attack–defeat ability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.
Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. Lightweight trust management based on Bayesian and Entropy adopts two strategies to make the model lightweight. One is using effective history records rather than all the records when updating trust value, which saves nodes' memory. The other is using indirect trust only when the direct trust is not credible enough to be the integrated trust, which reduces the network energy consumption. In addition, the Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1002/sec.969</identifier><language>eng</language><publisher>London: Blackwell Publishing Ltd</publisher><subject>Bayesian ; Bayesian analysis ; confidence level ; Entropy ; Lightweight ; Mathematical models ; Remote sensors ; trust management ; trust value ; Trustworthiness ; Weight reduction ; Wireless networks</subject><ispartof>Security and communication networks, 2015-01, Vol.8 (2), p.168-175</ispartof><rights>Copyright © 2014 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3939-a379227a209e3664276dfa388f20989fa1b5a378b41791f528ffd11110abc6663</citedby><cites>FETCH-LOGICAL-c3939-a379227a209e3664276dfa388f20989fa1b5a378b41791f528ffd11110abc6663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Che, Shenyun</creatorcontrib><creatorcontrib>Feng, Renjian</creatorcontrib><creatorcontrib>Liang, Xuan</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><title>A lightweight trust management based on Bayesian and Entropy for wireless sensor networks</title><title>Security and communication networks</title><addtitle>Security Comm. Networks</addtitle><description>ABSTRACT
With the rapid development of wireless sensor networks, the issue of designing a reasonable trust management has attracted more and more research attention. Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. First, the evaluated node's direct trust value is calculated by Bayesian and periodically updated according to the combination of effective history records and adaptive decay factor. We use effective history records rather than all the records to save nodes’ memory, and the adaptive decay factor enhances the algorithm's accuracy and dynamic. Then, according to the confidence level of the direct trust value, we decide whether the direct trust is credible enough to be the integrated trust. This can reduce the energy computation and make the algorithm lightweight. Last, if the direct trust is not credible enough, the overall indirect trust value will be calculated. The Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model. Simulation experiments are provided to assess the performance of the proposed trust management in terms of attack–defeat ability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.
Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. Lightweight trust management based on Bayesian and Entropy adopts two strategies to make the model lightweight. One is using effective history records rather than all the records when updating trust value, which saves nodes' memory. The other is using indirect trust only when the direct trust is not credible enough to be the integrated trust, which reduces the network energy consumption. In addition, the Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model.</description><subject>Bayesian</subject><subject>Bayesian analysis</subject><subject>confidence level</subject><subject>Entropy</subject><subject>Lightweight</subject><subject>Mathematical models</subject><subject>Remote sensors</subject><subject>trust management</subject><subject>trust value</subject><subject>Trustworthiness</subject><subject>Weight reduction</subject><subject>Wireless networks</subject><issn>1939-0114</issn><issn>1939-0122</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp10F1LwzAUBuAiCs4p_oWAFwqymY82aS_nmFMYE_xAvQpZdzK7denM6Zj792ZUdiGYi-Sc8JAc3ig6Z7TLKOU3CHk3k9lB1GKZyDqUcX64r1l8HJ0gzimVLFZxK_rokbKYfdYb2O2k9musydI4M4MluJpMDMKUVI7cmi1gYRwxbkoGrvbVakts5cmm8FACIkFwGHoH9abyCzyNjqwpEc5-z3b0ejd46d93Ro_Dh35v1MnFbiYjVMa5MpxmIKSMuZJTa0Sa2nCTZtawSRJMOomZyphNeGrtlIVFzSSXUop2dNW8u_LV1xqw1ssCcyhL46Bao2YyYTGNU5YEevGHzqu1d2G6oMIfNE04C-qyUbmvED1YvfLF0vitZlTvItYhYh0iDvK6kZuihO1_TD8P-o3uNLrAGr732viFlkqoRL-Nh3osxv0nppR-Fz_l9opb</recordid><startdate>20150125</startdate><enddate>20150125</enddate><creator>Che, Shenyun</creator><creator>Feng, Renjian</creator><creator>Liang, Xuan</creator><creator>Wang, Xiao</creator><general>Blackwell Publishing Ltd</general><general>Hindawi Limited</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7U5</scope></search><sort><creationdate>20150125</creationdate><title>A lightweight trust management based on Bayesian and Entropy for wireless sensor networks</title><author>Che, Shenyun ; Feng, Renjian ; Liang, Xuan ; Wang, Xiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3939-a379227a209e3664276dfa388f20989fa1b5a378b41791f528ffd11110abc6663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Bayesian</topic><topic>Bayesian analysis</topic><topic>confidence level</topic><topic>Entropy</topic><topic>Lightweight</topic><topic>Mathematical models</topic><topic>Remote sensors</topic><topic>trust management</topic><topic>trust value</topic><topic>Trustworthiness</topic><topic>Weight reduction</topic><topic>Wireless networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Che, Shenyun</creatorcontrib><creatorcontrib>Feng, Renjian</creatorcontrib><creatorcontrib>Liang, Xuan</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Solid State and Superconductivity Abstracts</collection><jtitle>Security and communication networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Che, Shenyun</au><au>Feng, Renjian</au><au>Liang, Xuan</au><au>Wang, Xiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A lightweight trust management based on Bayesian and Entropy for wireless sensor networks</atitle><jtitle>Security and communication networks</jtitle><addtitle>Security Comm. Networks</addtitle><date>2015-01-25</date><risdate>2015</risdate><volume>8</volume><issue>2</issue><spage>168</spage><epage>175</epage><pages>168-175</pages><issn>1939-0114</issn><eissn>1939-0122</eissn><abstract>ABSTRACT
With the rapid development of wireless sensor networks, the issue of designing a reasonable trust management has attracted more and more research attention. Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. First, the evaluated node's direct trust value is calculated by Bayesian and periodically updated according to the combination of effective history records and adaptive decay factor. We use effective history records rather than all the records to save nodes’ memory, and the adaptive decay factor enhances the algorithm's accuracy and dynamic. Then, according to the confidence level of the direct trust value, we decide whether the direct trust is credible enough to be the integrated trust. This can reduce the energy computation and make the algorithm lightweight. Last, if the direct trust is not credible enough, the overall indirect trust value will be calculated. The Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model. Simulation experiments are provided to assess the performance of the proposed trust management in terms of attack–defeat ability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.
Based on Bayesian and Entropy, this paper proposes a lightweight trust management for wireless sensor networks. Lightweight trust management based on Bayesian and Entropy adopts two strategies to make the model lightweight. One is using effective history records rather than all the records when updating trust value, which saves nodes' memory. The other is using indirect trust only when the direct trust is not credible enough to be the integrated trust, which reduces the network energy consumption. In addition, the Entropy Theory is adopted to distribute weights to different trust values, which can improve the problems caused by distributing weights subjectively and also enhance adaptability of the model.</abstract><cop>London</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/sec.969</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Bayesian Bayesian analysis confidence level Entropy Lightweight Mathematical models Remote sensors trust management trust value Trustworthiness Weight reduction Wireless networks |
| title | A lightweight trust management based on Bayesian and Entropy for wireless sensor networks |
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