A State of Art Survey of Nano Technology: Implementation, Challenges, and Future Trends
Zainab Salih Ageed, Awder Mohammed Ahmed, Naaman Omar, Shakir Fattah Kak, Ibrahim Mahmood Ibrahim, Hajar Maseeh Yasin, Zryan Najat Rashid, Azar Abid Salih, Nareen O. M. Salim
Asian Journal of Research in Computer Science · pp. 65–82 · Published 13 Jul 2021
10.9734/ajrcos/2021/v10i330245Abstract
Nanotechnology is a field of study that aims to make our lives easier, safer, and more environmentally friendly. With current upgrades and alterations to available networking and communication paradigms, incorporating Wireless Nano Sensor Networks (WNSN) with various products, sensors, and devices would introduce new network paradigms. The Internet of Nano Things is a term for this concept (IoNT), To achieve seamless interconnection between Nano networks and existing communication networks and the Internet, many topologies and communication paradigms must be developed while technological hurdles are addressed. The amount of data accessible limits how much research and decision-making can be done. This research visualizes a wide range of nanotechnology applications. The goal of this project was to show in more than way this research method may be used to information recommendation services. The routing protocol is critical in WNSN and IoNT because of the many nanoscale constraints. While ensuring the flow of data and information, this routing protocol must take into account the specific features of nanoscale communication.This research aims to provide insight into the WNSN (Wireless Nano Sensor Networks) and IoNT (The Internet of Nano Things) paradigms, as well as a detailed assessment of a large number of current routing protocols that are tailored to the characteristics and features of nano communication. Big data applications with their features and characteristics in general also use cloud computing. This paper explains different hands based on neural networks and implemented on FPGAs (which is Field-Programmable Gate Array) and other genetic algorithms and neural networks. Many more approaches and models compare.
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