Blockchain and Green Networking Analytics in 5G Networks and Beyond

The objective of green networking is to minimize greenhouse gas emissions while maintaining the same level of performance. Green networking refers to all processes used to optimize networking and inter-networking functions to make it more energy efficient. Green networking concepts can be extended to cover any method that reduces latency, save bandwidth, and/or decrease computation time, as a reduction in these factors invariably leads to power savings. These savings can directly translate into lowering greenhouse gas emissions and reduce computing's carbon footprint and its impact on the environment. Energy-awareness is critical in the networking infrastructure, especially in wireless 5G networks and beyond. Research on blockchain for 5G wireless networks is still in its infancy. But it is obvious that blockchain will significantly uplift the shape and experience of future mobile applications and services. Identifying the green networking analytics will lead to sustainable energy policy planning for the future.

VLSI Implementation of Green Computing Control Unit on Zynq FPGA for Green Communication

The issue of the energy shortage is affecting the entire planet. This is occurring because of massive population and industry growth around the world. As a result, the entire world is attempting to implement green networking systems and manufacture the power/energy efficient products. This research work discusses the green networking system technologies. This work introduces a power-efficient control unit (CU) design and implemented on the Zynq SoC (System on Chip) ultrascale field programmable gate array (FPGA). The VIVADO HLx Design Suite is used to simulate and analyze the CU model which is considered as one of the key components of central processing unit (CPU), used for data communication purposes. The CU is made suitable for the green communication by making it power-efficient. Therefore, the power consumption of the CU is analyzed for the various set frequency value ranging between 100 MHz and 5 GHz, and it is discovered that as the clock frequency rises up, the total power consumption also tends to get increased. The total power of the proposed model is reduced by 77.42%, 21.29%, and 17.93% from three models, respectively, being compared in the present paper. Final results shows that the CU is better suited to run at low frequencies to optimize power consumption.