تشخیص ناهنجاری خودمختار و چارچوب سیگنالینگ مولکولی برای نانوابزارهای مصنوعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|76948||2014||8 صفحه PDF||سفارش دهید||5249 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Nano Communication Networks, Volume 5, Issue 3, September 2014, Pages 55–62
The usage of biological nanodevices, i.e. bacteria cell, is problematic because of the unpredictability and possible dangers Akgül and Canberk (2014). There is not any complete deterministic model of these devices and there exist many unobservable parameters in bacteria action determination. So most of the experiments with biological nanodevices are not repeatable. Additionally, the usage of bacteria cells can be dangerous as they can become dangerous after they encountered with other bacteria cells Akgül and Canberk (2014). The idea of synthetic nanodevices is proposed as a solution to the uncertainty and safety problems of biological nanodevices. However, the most basic attribute of this concept is still a mystery. The data-gathering process in synthetic nanodevices is also essential for biological nanodevices. In the existing studies, usually the user directly presents the data to the nanodevice. However, as most of the nanonetwork applications are intra-body applications, the idea of collecting data from the environment is crucial for nanodevices. To the best of our knowledge any data-gathering process from the environment is not covered yet. Collecting the data from the surroundings is essential to determine the state of the environment and to determine the action of the nanodevice. In this study, we are presenting a knowledge harvesting framework that is designed for synthetic nanodevice model presented in Akgül and Canberk (2014). As the model is applied for mobile synthetic devices, the transmission of the harvested data becomes a challenge. A molecular flooding algorithm is also proposed to help the spread of the detected anomalies. In particular, we focus on the blood sugar anomaly, which leads us the performance metric of capability of regulation. The effects of interference and sampling time are investigated in the performance evaluation part.