A Development of Network Topology of Wireless Packet Communications for Disaster Situation with Genetic Algorithms or with Dijkstra's
A Development of Network Topology of Wireless Packet Communications for Disaster Situation with Genetic Algorithms or with Dijkstra's
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DOI：
10.1109/icc.2011.5962439
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
H. Juzoji;I. Nakajima;T. Kitano
中科院分区：
文献类型：
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作者：
H. Juzoji;I. Nakajima;T. Kitano
本文讨论了使用遗传算法（GAs）和Dijkstra算法优化分布式分组通信系统中的负载网络拓扑结构。这些算法是完全分布式的，其中信息几乎实时地在分组终端的每次移动中动态更新。采用多个分布式范例，使得每个终端在整个无线和卫星网络中发送关于网络拓扑的信息。当网络配置有足够大数量（N）的单元时，GA模型是有效的。然而，在海洋应用中或用于灾难现场，单元的数量（N）可以是低的，诸如7或8。在这种情况下，Dijkstra算法比遗传算法更有效。在Dijkstra算法中，邻接矩阵行列式中不使用0或1的系统，但可以为每条路径分配一个权重（对应于每个终端的距离）。基于现场实验，我们将寻求通过双向传输邻接矩阵行列式来管理网络拓扑。
This paper discusses the use of genetic algorithms (GAs) and Dijkstra's algorithm to optimize load network topologies in distributed packet communication systems. These algorithm is fully distributed in which information is dynamically updated at each movement of packet terminal almost realtime. Multiple distributed paradigms are adopted so that each terminal transmits information on the network topology throughout the wireless and satellite network. A GA model is effective when a network is configured with a sufficiently large number (N) of units. However, in marine applications or for use at a disaster site, the number (N) of units may be low, such as 7 or 8. In such cases, Dijkstra's algorithm is more efficient than genetic algorithms. With Dijkstra's algorithm, a system of 0 or 1 is not used in the adjacency matrix determinant, but each path can be assigned a weight (corresponding to the distance of each terminal). Based on field experiments, we will seek to manage network topologies by transmitting the adjacency matrix determinant bilaterally.