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Method for Intelligent Routing Within Ad-Hoc Networks with Complex Topology

机译:具有复杂拓扑的Ad-Hoc网络中的智能路由方法

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The most common problem for all sensory IOT networks is routing inefficiency. “Effective” here means that wandering packets shouldn’t pass excessive or overloaded hops as well as shouldn’t generate extra official traffic to coordinator. These are critical conditions in low-end networks, such as networks, formed of sensors, connected both together and to the internet. This work proposes solution for “effective routing” problem by use of machine learning through genetic algorithms. This reduces both computational time and energy draining in all weak hops through network. But here comes out the first valuable weakness: stable coordinator with an ability to simulate neural network, i.e. to perform complex parallel computations, is required.
机译:所有感官物联网的最普遍问题是路由效率低下。这里的“有效”是指漫游的数据包不应通过过多或过载的跃点,也不应为协调员产生额外的官方流量。这些是低端网络(例如由传感器组成的网络)连接在一起并连接到Internet的关键条件。这项工作提出了通过遗传算法使用机器学习来解决“有效路由”问题的解决方案。这样可以减少网络中所有弱跳的计算时间和能量消耗。但是这里出现了第一个有价值的弱点:需要稳定的协调器,它具有模拟神经网络的能力,即执行复杂的并行计算的能力。

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