Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment
Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision-making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which unc...
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| Veröffentlicht in: | IEEE wireless communications letters 2018-08, Vol.7 (4), p.518-521 |
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| container_title | IEEE wireless communications letters |
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| creator | Daxin Tian Ziyi Dai Jianshan Zhou Xuting Duan Zhengguo Sheng Min Chen Qiang Ni Leung, Victor C. M. |
| description | Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision-making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations. |
| doi_str_mv | 10.1109/LWC.2018.2792023 |
| format | Article |
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(IEEE) 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c286t-9dcea8f01afd7563d4d8061430b9702ccec5551485798345fffd9226d99a25ad3</cites><orcidid>0000-0002-4987-4695 ; 0000-0002-4593-1656 ; 0000-0003-3529-2640 ; 0000-0001-5931-8310 ; 0000-0003-2143-4003 ; 0000-0002-0960-4447 ; 0000-0001-7796-5650</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8254377$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8254377$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Daxin Tian</creatorcontrib><creatorcontrib>Ziyi Dai</creatorcontrib><creatorcontrib>Jianshan Zhou</creatorcontrib><creatorcontrib>Xuting Duan</creatorcontrib><creatorcontrib>Zhengguo Sheng</creatorcontrib><creatorcontrib>Min Chen</creatorcontrib><creatorcontrib>Qiang Ni</creatorcontrib><creatorcontrib>Leung, Victor C. M.</creatorcontrib><title>Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision-making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations.</description><subject>Computer simulation</subject><subject>Decision making</subject><subject>Dynamic programming</subject><subject>Electronic mail</subject><subject>Epidemic mechanisms</subject><subject>Epidemics</subject><subject>Information dissemination</subject><subject>Manganese</subject><subject>Markov analysis</subject><subject>Markov chain</subject><subject>Markov chains</subject><subject>Markov processes</subject><subject>Mathematical model</subject><subject>Messages</subject><subject>Network reliability</subject><subject>Optimization</subject><subject>Reliability</subject><subject>Reliability analysis</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UE1LAzEQDaJgqb0LXhY8b83nJjlKW7VY6MXiMcR8QEo3uyZbof_elC2dy7wZ3pvhPQAeEZwjBOXL5nsxxxCJOeYSQ0xuwASjBteYUHZ7xYTfg1nOe1iqgQgjMQGf234IrT5Uqz5Y1wZTraPvUquH0MVqGXIuyzhOIVa7aFwadEHLU9Rn-ir-hdTF1sXhAdx5fchudulTsHtbfS0-6s32fb143dQGi2aopTVOCw-R9pazhlhqBWwQJfBHcoiNcYYxhqhgXIpiwHtvJcaNlVJjpi2Zgufxbp-636PLg9p3xxTLS4UR4ohKimlhwZFlUpdzcl71qThNJ4WgOqemSmrqnJq6pFYkT6MkOOeudIEZJZyTfxZQZ-Y</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Daxin Tian</creator><creator>Ziyi Dai</creator><creator>Jianshan Zhou</creator><creator>Xuting Duan</creator><creator>Zhengguo Sheng</creator><creator>Min Chen</creator><creator>Qiang Ni</creator><creator>Leung, Victor C. M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-4987-4695</orcidid><orcidid>https://orcid.org/0000-0002-4593-1656</orcidid><orcidid>https://orcid.org/0000-0003-3529-2640</orcidid><orcidid>https://orcid.org/0000-0001-5931-8310</orcidid><orcidid>https://orcid.org/0000-0003-2143-4003</orcidid><orcidid>https://orcid.org/0000-0002-0960-4447</orcidid><orcidid>https://orcid.org/0000-0001-7796-5650</orcidid></search><sort><creationdate>20180801</creationdate><title>Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment</title><author>Daxin Tian ; Ziyi Dai ; Jianshan Zhou ; Xuting Duan ; Zhengguo Sheng ; Min Chen ; Qiang Ni ; Leung, Victor C. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c286t-9dcea8f01afd7563d4d8061430b9702ccec5551485798345fffd9226d99a25ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Computer simulation</topic><topic>Decision making</topic><topic>Dynamic programming</topic><topic>Electronic mail</topic><topic>Epidemic mechanisms</topic><topic>Epidemics</topic><topic>Information dissemination</topic><topic>Manganese</topic><topic>Markov analysis</topic><topic>Markov chain</topic><topic>Markov chains</topic><topic>Markov processes</topic><topic>Mathematical model</topic><topic>Messages</topic><topic>Network reliability</topic><topic>Optimization</topic><topic>Reliability</topic><topic>Reliability analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Daxin Tian</creatorcontrib><creatorcontrib>Ziyi Dai</creatorcontrib><creatorcontrib>Jianshan Zhou</creatorcontrib><creatorcontrib>Xuting Duan</creatorcontrib><creatorcontrib>Zhengguo Sheng</creatorcontrib><creatorcontrib>Min Chen</creatorcontrib><creatorcontrib>Qiang Ni</creatorcontrib><creatorcontrib>Leung, Victor C. M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Daxin Tian</au><au>Ziyi Dai</au><au>Jianshan Zhou</au><au>Xuting Duan</au><au>Zhengguo Sheng</au><au>Min Chen</au><au>Qiang Ni</au><au>Leung, Victor C. M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2018-08-01</date><risdate>2018</risdate><volume>7</volume><issue>4</issue><spage>518</spage><epage>521</epage><pages>518-521</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision-making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. 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| source | IEEE Electronic Library (IEL) |
| subjects | Computer simulation Decision making Dynamic programming Electronic mail Epidemic mechanisms Epidemics Information dissemination Manganese Markov analysis Markov chain Markov chains Markov processes Mathematical model Messages Network reliability Optimization Reliability Reliability analysis |
| title | Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment |
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