Research On Reliable Data Transfer For Underwater Wireless Sensor Networks

About 71% of the earth's surface is covered by the ocean.With the continuous exploitation of land resources,the demand for marine resources has gradually increased.Ocean observation is the beginning of all ocean activities.As an effective ocean observation method,the Underwater Wireless Sensor Network(UWSN)has been used in many fields,such as disaster prevention and early warning,scientific research,pollution monitoring,submarine environmental exploration,military surveillance,and positioning navigation.Unlike terrestrial wireless sensor networks(WSN)based on electromagnetic wave,UWSN,which uses sound waves as the main communication medium,has the characteristics of high energy consumption,long delay,low bandwidth,and high bit error rate,which makes the reliable data transfer in UWSN face a great challenge.Sex faces great challenges.The challenge is exacerbated by topology changes caused by node mobility:1.In the link layer,the change of link state caused by node mobility makes the static configuration or the globally unified FEC coding method designed based on experience unable to guarantee the reliability of data transmission.At the same time,in the reliability guarantee mechanism based on retransmission,retransmission packets and control packets occupy valuable underwater acoustic channels,which exacerbates the conflict and congestion in the network.2.In the network layer,the key nodes frequently participate in the routing,which leads to the imbalance of energy consumption and the reduction of the whole network life cycle.At the same time,the topology change caused by node mobility leads to the routing void,which exacerbates the packet loss in the network and reduces the packet delivery rate.Based on the above challenges,this paper proposes a reliable link layer communication algorithm based on BCH dynamic coding.The algorithm calculates the BER expectation of current data transmission by sensing / estimating the channel state,and then designs a BCH coding set that can tolerate the BER.In order to improve the reliability of end-to-end transmission,the algorithm uses the combination optimization method to select the BCH coding combination with the minimum number of redundant bits.Based on the above algorithm,the dynamic BCH coding problem of underwater cognitive radio(UCAN)is studied.An E-DPSO algorithm is proposed.By sensing the channel state and modeling the energy consumption,the combination of the frequency,power and BCH code of the transmitted signal can be configured dynamically,reducing the energy consumption of each data packet on the premise of ensuring normal data decoding and error correction capabilities.In the network layer,a routing protocol EBDRP based on energy bottleneck and depth information is proposed.The energy bottleneck is the best value of the energy limit of all the positive paths passing through the node.The consideration of energy bottlenecks allows nodes to consider the energy limit in the range of single hops or even multi-hop when making routing decisions.It is more foresight to choose the node path with more energy(instead of directly selecting the node with more energy)as the next hop,which greatly avoids the premature death of key nodes due to energy imbalance and improves the network life cycle and transmission reliability.In this paper,the proposed algorithms are verified in turn.By comparing the effective data ratio of the BCH dynamic coding algorithm with the fixed BCH coding,the high transmission efficiency of the dynamic algorithm is verified.The E-DPSO algorithm in the underwater cognitive radio network is used to calculate the optimal transmission power frequency combination.Compared with other power frequency combinations,the optimal combination has the lowest energy consumption per data bits.The algorithm can converge quickly.By comparing the energy consumption,delivery rate and delay of EBDRP routing protocols with DBR and VBF routing protocols,the efficiency of EBDRP is verified.In addition,this paper also discusses the impact of the trade-off between energy bottleneck and depth information on the performance of EBDRP.