Mobile Networks and Applications

, Volume 23, Issue 4, pp 828–839 | Cite as

A Low Duty Cycle Efficient MAC Protocol Based on Self-Adaption and Predictive Strategy

  • De-gan ZhangEmail author
  • Shan Zhou
  • Ya-meng Tang


In the medium access control layer (MAC) of WSN, the scheduling mechanism of nodes based on the periodical listen/sleep is an effective way of saving node energy consumption. In the case of data transmission which is not affected reduces the nodes’ proportions, we improve the protocols based on asynchronous MAC. This paper discusses a protocol which is a low duty cycle energy-efficient MAC protocol for WSN and can be adaptively updated based on the prediction nodes’ wake-up time. We call it AP-MAC protocol. In AP-MAC protocol, the nodes will not wake up or send data in the same period, and they will wake up in random time according to the algorithm that has been set. In this case, the network can avoid the problem of collision, cross-talk, etc. caused by all the nodes’ wake-up in the same time, and save more energy. To ensure the reliable transmission of network data, the node which sends data will predict the wake-up time of receiving nodes and ensure the receiving nodes wake up timely and establish a connection with sending note. At the same time, we join several adaptive update mechanisms in the network according to the dynamic changes of it. The experimental results show that the improved protocol not only can save the network energy consumption by effectively reducing the overall duty cycle of the network nodes and improving the reliable transmission of data but also can improve the adaptability of the network.


MAC layer Asynchronous Predict Duty cycle Update 



This research work is supported by National Natural Science Foundation fo China (Grant No.61571328), Major projects of science and technology in Tianjin (No.15ZXDSGX00050), Training plan of Tianjin University Innovation Team (No.TD12-5016), Major projects of science and technology for their service in Tianjin (No.16ZXFWGX00010), Training plan of Tianjin 131 Innovation Talent Team (No.TD2015-23).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Tianjin Key Lab of Intelligent Computing & Novel Software TechnologyTianjin University of TechnologyTianjinChina
  2. 2.Key Laboratory of Computer Vision and System, Ministry of EducationTianjin University of TechnologyTianjinChina
  3. 3.School of Electronic and Information EngineeringUniversity of SydneySydneyAustralia

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