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Information propagation modeling in a drone network using disease epidemic models
| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yoojoong Kim Byoung Du Kim Jong-Kook Kim Junhee Seok |
| Copyright Year | 2016 |
| Abstract | Drones transmit and receive packets with each other in a wireless network setting. Packet transmission among drones fails for various reasons. The pattern of information propagation through the packet transmission in a drone network can be considered similar to the pattern of infectious disease transmission in a human interaction network. In this work, we use a Microscopic Markov Chain Approach (MMCA), which has been applied to model the patterns of disease epidemics in a human network, to investigate the packet transmission pattern in a microscopic scale. Throughout the simulation studies, we investigated the usefulness of MMCAs for a drone network. |
| Starting Page | 79 |
| Ending Page | 81 |
| Page Count | 3 |
| File Format | HTM / HTML |
| ISBN | 9781467399913 |
| ISSN | 21658536 |
| Journal | 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN) |
| DOI | 10.1109/ICUFN.2016.7536986 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Computational modeling Disease epidemic model Transmission Packet transmission Drone Real time Infectious disease transmission pattern Drone network Diseases Human interaction network Monte Carlo methods Wireless network setting Microscopy Network Markov processes Information propagation modeling Unmanned aerial vehicles Real-time systems MMCA Radio networks Telecommunication security Microscopic Markov chain approach |
| Content Type | Text |
| Resource Type | Preprint Article |
