机会网络的混合性能建模
Hybrid performance modelling of opportunistic networks
AI总结：
使用随机HYPE过程代数对机会网络进行建模，通过连续流、随机接触和离散事件分别描述网络流量、节点接触和瞬时决策，展示了该方法的灵活性和表达能力。
摘要：
我们展示了使用过程代数随机HYPE对机会网络进行建模。网络流量被建模为连续流，网络中节点之间的接触被随机建模，瞬时决策被建模为离散事件。我们的模型描述了一个由固定视频传感器组成的网络，其中有一个移动摆渡车从传感器收集数据并将其传送至基站。我们考虑了不同的移动模型和不同的摆渡车缓冲区大小。该案例研究说明了随机HYPE的灵活性和表达能力。我们还讨论了用于描述随机HYPE模型并进行仿真的软件。
英文摘要：
We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.
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