As nanotechnology advances, it is possible to build nanomachines to conduct tasks on the nano-scale. Since the EM wave, the traditional communication medium does not apply in nano-scale, Molecular Communication (MC) has been recently given increasing attention for its excellent biocompatibility and low energy consumption within the human body. Diffusion channel-based MC(DbMC) is mainly studied in the aquatic environment. However, due to the randomness of diffusion, DbMC suffers high losses, low propagation speed, and limited communication range. Directional Molecular Communication(DMC) is proposed by introducing chemotaxis. DMC can significantly improve the efficiency of molecular communication because it keeps molecules moving along a predetermined path. It is particularly suitable in the scenario of target tracking related to some applications such as drug delivery. In this paper, we have proposed a novel massive beacon coordinates system model to aid target tracking. Beacons in this system navigate nanomachines, and the beacon system can uniquely determine their position coordinates. Each nanomachine carries a lot of bacteria carrier (E.coli) to share information. Information is encoded in DNA molecules and transferred to other nanomachines by bacteria carriers. With the help of bacteria carriers, nanomachines can share their current position information with others to realize cooperated fast target tracking. We have evaluated its performance in target tracking through simulation by comparison with the diffusion-based model. Some key factors that may influence target tracking are also taken into consideration.

中文翻译：

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定向分子通信中基于大信标坐标系的协同目标跟踪算法

随着纳米技术的进步，有可能建造纳米机器来执行纳米尺度的任务。自从电磁波出现以来，传统的通信介质不再适用于纳米级，分子通信（MC）由于其优异的生物相容性和人体内的低能耗而受到越来越多的关注。基于扩散通道的MC(DbMC)主要在水环境中进行研究。然而，由于扩散的随机性，DbMC 损失高、传播速度低、通信范围有限。定向分子通信(DMC)是通过引入趋化性提出的。DMC 可以显着提高分子通讯的效率，因为它使分子沿着预定的路径移动。特别适用于与药物输送等一些应用相关的目标跟踪场景。在本文中，我们提出了一种新的大规模信标坐标系统模型来辅助目标跟踪。该系统中的信标导航纳米机器，信标系统可以唯一确定它们的位置坐标。每台纳米机器都携带大量细菌载体（大肠杆菌）来共享信息。信息被编码在 DNA 分子中，并通过细菌载体转移到其他纳米机器上。在细菌载体的帮助下，纳米机器可以与他人共享其当前位置信息，以实现协同快速目标跟踪。我们通过与基于扩散的模型进行比较，通过模拟评估了其在目标跟踪中的性能。