Due to the widespread popularity and usage of Internet of things (IoT)-enabled devices, there is an exponential increase in the data traffic generated from these IoT devices. Most of these devices communicate with each other using heterogeneous links having constraints such as latency, throughput, and interference from concurrent transmissions. This results in an extra burden on the underlying communication infrastructure to manage the traffic within these constraints between source and destination. However, most of the existing applications use different Transmission Control Protocol (TCP) variants for traffic management between these devices and are dependent on the stage of the sender, irrespective of the application types and link characteristics. Each operating system (OS) has different TCP variant for all applications, irrespective of path characteristics.Hence, a single TCP variant cannot select the best suitable link, which results in degradation in throughput compared to the existing default. Moreover, it cannot use the full capacity of the available link for different applications and network links, especially in heterogeneous network such as IoT. To cope up with these challenges, in this paper, we propose an Adaptive and Dynamic TCP Interface Architecture (ADYTIA). ADYTIA allows the usage of different TCP variants based on application and link characteristics, irrespective of the physical links of the entire path. It allows the usage of different TCP variants based on their design principle across heterogeneous technologies, platforms, and applications.ADYTIA is implemented on NS-2 and Linux kernel for real testbed experiments.Its ability to select the best suitable TCP variant results in 20% to 80% improvement in throughput compared with the existing default and single TCP variant on Linux and Windows. KEYWORDS heterogeneous networks, internetworking, operating systems, TCPIP, transport protocols Int J Commun Syst. 2019;32:e3855.wileyonlinelibrary.com/journal/dac
Foreign and Visually disable people in India often find difficulties in recognizing different currency notes. Even if some time it is also difficult for Indian healthy people to identify same amount of currency notes with different-new designs. Human eye has also some limitation so some time fake currency not identifiable by them. In this paper using deep learning technique, detection model trained with dataset and tested it with different Indian currency with good accuracy.
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