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Modeling of Sensor Packet Arrival in Simulation Environment

机译:仿真环境中传感器数据包到达的建模

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摘要

Today's Internet supports a very varied range of traffic based on the myriad types of applications that are part of the network. Within the last decade, a paradigm known as Internet of Things (IoT) has evolved that has added and continues to rapidly add a multitude of Internet aware devices to the network. Almost simultaneously, the concept of Cloud has emerged having virtually boundless compute and storage capacities and at the same time flexible based on user needs. In the last few years, these two emerging technologies are coming together. With Cloud being the global Internet, IoT promises to add an exciting amount of data to the Internet, both in terms of nature of traffic as well as in volume. Analysis of the traffic is necessary to design network and optimally manage the resources to be provisioned at the service provider end. While it is virtually impossible to analyze every kind of traffic generated by IoT devices, we can classify them based on their general characteristics and analyze each of them independently. In this work, we have selected a simulation based sensor traffic to analyze the distribution of inter-packet times of packets arriving at the receiver using the Cooja simulator. Multiple tests were conducted with varying number of senders from which we find that a high percentage of the packets arrive at the receiver very close to one another and the rest of the packets get spaced apart over time, which fits well with the Pareto distribution. We also see that this behaviour gets more pronounced as the number of senders increase.
机译:当今的Internet根据网络中无数种应用程序的类型来支持各种流量。在过去的十年中,称为物联网(IoT)的范式得到了发展,该范式已添加并继续迅速向网络中添加大量具有Internet意识的设备。几乎同时,已经出现了云的概念,它具有几乎无限的计算和存储能力,同时可以根据用户需求灵活地进行选择。在过去的几年中,这两种新兴技术融合在一起。随着云成为全球互联网,物联网有望在流量的性质和数量上为互联网增加令人兴奋的数据量。为了设计网络并优化管理要在服务提供商端提供的资源,必须对流量进行分析。虽然几乎不可能分析物联网设备产生的每种流量,但我们可以根据它们的一般特征对其进行分类,并分别进行分析。在这项工作中,我们选择了基于仿真的传感器流量,以使用Cooja模拟器分析到达接收器的数据包在数据包之间的时间间隔。对不同数量的发送者进行了多次测试,从中我们发现,很大比例的数据包到达接收器时非常接近,其余的数据包随时间间隔开,这与帕累托分布非常吻合。我们还看到,随着发件人数量的增加,这种行为变得更加明显。

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