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Nature inspired node density estimation for molecular nanonetworks

机译：自然启发的分子纳米网络节点密度估计

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摘要

The problem of estimating the node density in ad hoc networks is a significant one for protocol design. In molecular nanonetworks, the node density estimation problem poses additional challenges due to the limited processing and communication capabilities of the network nodes which necessitate the design of simple to implement distributed solutions, and the diffusion based communication channel which is different from traditional electromagnetic networks. In this work, inspired by the quorum sensing process, we propose and analyze a new node density estimation scheme based on synchronous transmission of all network nodes and measurement of the received molecular concentration. We show that when the synchronous transmission is performed in infinite space, a linear parametric model of the node density can be derived which can be used for estimation purposes. When, however, the transmission is performed over a finite space the model becomes time varying. To overcome the difficulties associated with the time varying nature we propose the use of periodic transmission which for large enough values of the period transforms the linear model into a static one. An online parameter identification technique is then introduced to estimate the node density using the derived linear static parametric models. The utilization of the node density estimates to adaptively regulate probabilistic flooding in network structures relevant to nanonetworks is then considered. The random geometric graph model and uniform grid structures are used to demonstrate how the node estimates can be used to dictate the desired rebroadcast probabilities, through analysis and simulations.
机译：估计ad hoc网络中节点密度的问题对于协议设计是一个重要的问题。在分子纳米网络中，节点密度估计问题由于网络节点的有限处理和通信能力而带来了额外的挑战，这需要设计易于实现分布式解决方案的设计以及与传统电磁网络不同的基于扩散的通信通道。在这项工作中，受群体感应过程的启发，我们提出并分析了一种基于所有网络节点的同步传输和接收分子浓度测量的新节点密度估计方案。我们表明，当在无限空间中执行同步传输时，可以导出节点密度的线性参数模型，该模型可以用于估计目的。但是，当在有限的空间上执行传输时，模型将随时间变化。为了克服与时变性质相关的困难，我们建议使用周期性传输，对于周期足够大的值，该周期性传输会将线性模型转换为静态模型。然后引入在线参数识别技术，以使用导出的线性静态参数模型来估计节点密度。然后考虑利用节点密度估计值来自适应地调节与纳米网络相关的网络结构中的概率泛洪。随机几何图形模型和统一的网格结构用于通过分析和模拟来演示如何将节点估计值用于指示所需的重播概率。

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