Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment

Tian, Daxin and Dai, Ziyi and Zhou, Jianshan and Duan, Xuting and Sheng, Zhengguo and Chen, Min and Ni, Qiang and Leung, Victor C.m. (2018) Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment. IEEE Wireless Communications Letters, 7 (4). pp. 518-521. ISSN 2162-2337

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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. 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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Wireless Communications Letters
Additional Information:
©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2207
Subjects:
?? control and systems engineeringelectrical and electronic engineeringphysics and astronomy (miscellaneous) ??
ID Code:
124457
Deposited By:
Deposited On:
09 Apr 2018 08:14
Refereed?:
Yes
Published?:
Published
Last Modified:
10 Jan 2024 00:24