Recent advancement in physics and nanotechnology have paved the way for manufacturing of processor, memory, batteries, transceiver, antenna and sensing units at nano-scale. A nanomachine is an integrated device with dimensions in nano-scale, and able to do simple tasks. By networking of nano-machines, they are able to perform the more complicated tasks by the cooperative manner and can play an important role in applications such as biomedical, environmental monitoring, industrial and military. Many novel nano-scale communication options have been currently proposed. However, there are four main nano-scale communication techniques: nanomechanical, acoustic, chemical or molecular and electromagnetic communications. However, this paper focuses on the molecular and electromagnetic communications as the promising nanoscale communication approaches, and then will be reviewed the novel nano-scale communication paradigms that are currently presented.

Software Defined Networking (SDN) is an emerging architecture which decouples networks control plane and data plane physically. It makes control plane programmable trough a centralized controller, and builds intelligent and flexible networks. The OpenFlow is one of the most famous SDN protocols, which acts as a southbound interface between control plane and data plane. In this survey, SDN implementation approaches and different southbound interfaces, beside different version of OpenFlow, are introduced. In addition to general architecture of SDN, different wireless architectures are discussed. Here, also potential SDN's applications and research areas including hot topics such as Information Centric Networks, Cloud and datacenters, multimedia, wireless and mobile networks over SDN are reviewed.

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Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network.