Research On Energy Management Strategy For Cognitive Radio Sensor Networks Based On Energy Harvesting

Cognitive radio sensor network(CRSN)is a new type of network that incorporates wireless sensor network(WSN)and cognitive radio(CR)technology,which effectively alleviates the scarcity of the spectrum resource in WSN,but it causes more energy consumption.In order to make up for the excessive energy consumption and prolong the network lifetime,energy harvesting technology is introduced into CRSN,and then another network is formed: energy harvesting aided CRSN(EH-CRSN).With the purpose of prolonging the network lifetime and improving the nodes' energy efficiency as possible,it is necessary to study the energy management strategy of sensor nodes.Firstly,energy allocation management of EH-CRSN is studied in this thesis.To conduct the collaborative work between CR module and EH module,a spectrum sensing protocol based on energy harvesting is proposed.According to its own EH ability and residual power,each node adapts itself to participate in spectrum sensing by a certain probability so as to make use of energy in a balanced way,thus greatly extending network lifetime and improving system performance.At the same time,this thesis designs a low power consumption strategy for vulnerable nodes.The simulation results demonstrate that the proposed solution allows low-cost devices to meet the requirement of long term continuous operation in the sensor network.Secondly,in multi-channel environment,spectrum sensing performance of EHCRSN is considered and the C-E advanced algorithm is proposed focusing on the optimization of the available channel and the total available time.The C-E advanced algorithm,on the basis of heterogeneous nodes structure,aims to maximize the available time of the detected channel under the premise of ensuring the nodes' energy continuity and sufficient protection of authorized users from interference.Compared with the general C-E algorithm,C-E advanced algorithm considers more factors in optimization.On the one hand,energy consumption is balanced by compromising a small part of the detection performance.On the other hand,by optimizing the penalty mechanism,the efficiency of the algorithm is enhanced.The simulation results show that C-E advanced algorithm achieves the continuity of the network,obtains better sensing performance,and reduces the computational complexity.In this thesis,deep studies are carried out on the energy management for energy harvesting aided CRSN,and a number of results are obtained which have some theoretical value for future research of EH-CRSN.