Energy-Efficient Transmission Strategy For Wireless Sensor Networks

The wireless sensor network technology, as one of the key technologies of internet of things, is leading a revolution of information technology. However, the energy of sensor network node is limited, which is a"bottleneck"of the wireless sensor network technology and has restricted it's applications in various fields. According to current theories and practical applications, most energy of the sensor network node is consumed in transmission. Therefore, how to improve the transmission strategy, which can avoid unnecessary energy consumption and use the energy efficiently, became an important research topic.On the one hand, most current transmission protocols can not achieve energy-efficient of Wireless Sensor Networks (WSNs), an energy-efficient adaptive transmission based on channel and buffer state is proposed, including Channel and Buffer Based Transmission (CBT) and Channel and Buffer Based Fragment Transmission (CBFT). The adaptive transmission based on the current channel state to decide whether to transfer, avoid the energy waste caused by failed transmission. CBFT based on CBT, and combine with virtual fragment transmission technology. Data transfer problem of sensor node is modeled as Markov decision process, Q learning algorithm is proposed to solve the problem. The simulation results show that the usage of sensor node's energy of sensor node is efficient. The lifetime of wireless sensor network also can be prolonged.On the other hand, wireless sensor network channel side information is not fully known often, and only partially information can only be observed. In this paper, based on the observation of information which feedbacked from last transmission, the data transmission problem can established as a discrete time partially observable Markov decision processes model. Linear-Q learning algorithm is proposed to solve the problem. The simulation results also show that the transmission scheduling based on partially observable Markov decision processes model can improve the throughput and reduce the buffer overflow.