TEA-MAC: Traffic Estimation Adaptive MAC Protocol for Underwater Acoustic Networks

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@InProceedings{Chen:2018:ICSESS,

• author = "Xianyi Chen and Guolan Lin",
• booktitle = "2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)",
• title = "{TEA-MAC:} Traffic Estimation Adaptive {MAC} Protocol for Underwater Acoustic Networks",
• year = "2018",
• month = nov,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICSESS.2018.8663928",
• ISSN = "2327-0594",
• abstract = "Underwater acoustic sensor networks (UASNs) have been applied dramatically in many activities, such as ocean exploration and tsunami warning. However, due to the characteristics of underwater acoustic channel is quite difference from the radio and optical channel, media access control (MAC) is a crucial issue in underwater acoustic sensor networks. In this paper, we propose a Traffic Estimation Adaptive :MAC Protocol (TEA-MAC) based on a changeable duty cycle according to the traffic load. As the better the duty cycle matches the traffic of UASNs, the less energy and delay the nodes consume for data transmission, it is very import to sense the network load correctly. To address this issue, a traffic estimation algorithm based on nodes clustering and Genetic Programming is proposed in TEA-MAC, which can predict the network load successfully and set the duty cycle desirably. The Simulation results show that TEA-MAC performs better than the existing representative MAC protocols in terms of network throughput, end-to-end delay and energy efficiency.",
• notes = "Also known as \cite{8663928}",

}

Genetic Programming entries for Xianyi Chen Guolan Lin

Citations