摘要
为解决机会网络(opportunistic networks,ONs)中链路的频繁断开、节点的高速移动以及稀疏的网络密度等问题,提出了基于节点移动的历史数据和节点的社会性预测节点轨迹的方法,选取中继节点完成消息传递的机会网络路由协议。首先利用条件熵分析节点轨迹的可预测性;然后根据速度和预测单元,对节点进行单位活动区域的划分;最后利用节点移动概率及其社会性,对节点的下一位置进行预测。仿真结果表明:轨迹预测可以有效解决因节点高速移动,位置不断变化而导致的网络连通性时好时坏、极易断开的问题;基于轨迹预测的通信协议达到了较好的传递成功率,实现了对消息的高效传递。
According to the characteristics such as frequent disconnection of links,the high-speed movement of nodes and the density of sparse network in opportunistic networks(ONs),this paper proposes a method based on the historical data of node movement and the nodes’sociality to predict trajectory of nodes,and then to select relay nodes to complete the routing protocol of the opportunity network for message transmission.First,conditional entropy is used to analyze the predictability of node trajectories.Then,the node’s unit active area is divided according to the node’s speed and prediction unit.Finally,using the node movement probability and its sociality,the next position of the node is predicted.The simulation results show that the trajectory prediction can effectively solve the problem that the network connectivity is easily disconnected due to the high-speed movement of the nodes and the constant change of nodes’position.The communication protocol based on trajectory prediction has a good delivery success rate and achieves high-efficiecy delivery of messages.
作者
王桐
单欣
郑欣蕊
WANG Tong;SHAN Xin;ZHENG Xinrui(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Department of Mechanical and Industrial Engineering,University of Toronto,Toronto M5S 1A1,Canada)
出处
《应用科技》
CAS
2020年第3期94-99,共6页
Applied Science and Technology
基金
国家自然科学基金项目(51779050)。
关键词
机会网络
轨迹预测
历史数据
条件熵
网格划分
社会性
概率计算
消息传递
opportunity network
trajectory prediction
historical data
conditional entropy
meshing
sociality
probability calculation
message delivery