摘要
针对现有水下传感器网络(Underwater Wireless Sensor Networks,UWSN)中机会路由(Opportunistic Routing,OR)存在转发节点优先级固定,导致节点能量消耗过快、传输率低,不适用于对网络生存时间和可靠性要求较高应用场景(如海底勘探)的问题,提出了基于轮转优先级的机会路由算法(RPOR),结合节点剩余能量、与源节点之间的深度差构建适度值作为选择最优转发节点的目标函数,选择剩余能量较多、传输成功率更高的节点完成分组的传输以轮转转发优先级、均衡节点能耗;增加ACK传输确认机制协调候选集,以单跳传输代替广播传输,减少了冗余分组的转发,降低了分组碰撞的概率.仿真结果表明该算法有效提升了数据分组传输率和活动节点数率,提高了分组传输可靠性、延长了网络生存时间.
Aiming at the opportunistic routing problem in the existing underwater wireless sensor networks(UWSN),this paper proposed a new routing strategy routing(OR)has the problem that the priority of forwarding node was fixed,which leads to the node energy consumption is too fast,the transmission rate was low,and it was not suitable for the application scenarios(such as seabed exploration)with high requirements for network survival time and reliability.This paper proposed an opportunistic routing algorithm based on rotation priority(RPOR).Firstly,it constructed an appropriate value by combining the residual energy of the node and the depth difference between the node and the source node.As the objective function of selecting the optimal forwarding node,the node with more residual energy and higher transmission success rate was selected to complete the packet transmission,so as to rotate the forwarding priority and balance the node energy consumption;at the same time,the ACK transmission confirmation mechanism was added to coordinate the candidate set,and the single hop transmission was used to replace the broadcast transmission,so as to reduce the forwarding of redundant packets and reduce the probability of packet collision.Simulation results showed that the algorithm can effectively improve the data packet transmission rate and the number of active nodes,improve the packet transmission reliability and prolong the network lifetime.
作者
杨磊
谢师丹
曹歌
高学健
YANG Lei;XIE Shi-dan;CAO Ge;GAO Xue-jian(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《哈尔滨商业大学学报(自然科学版)》
CAS
2022年第1期42-49,共8页
Journal of Harbin University of Commerce:Natural Sciences Edition