Congestion Prediction in Internet of Things Network using Temporal Convolutional Network A Centralized Approach

The unprecedented ballooning of network traffic flow, specifically, Internet of Things (IoT) network traffic, has big stressed of congestion on todays Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, the network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices which have unlike format of data and also manifest complex correlations, so the generated data is heterogeneous and nonlinear in nature. Conventional machine learning approaches unable to deal with nonlinear datasets and suffer from misclassification of real network traffic due to overfitting. Therefore, it also becomes really hard for conventional machine learning tools like shallow neural networks to predict the congestion accurately. Accuracy of congestion prediction algorithms play an important role to control the congestion by regulating the send rate of the source. Various deeplearning methods (LSTM, CNN, GRU, etc.) are considered in designing network traffic flow predictors, which have shown promising results. In this work, we propose a novel congestion predictor for IoT, that uses Temporal Convolutional Network (TCN). Furthermore, we use Taguchi method to optimize the TCN model that reduces the number of runs of the experiments. We compare TCN with other four deep learning-based models concerning Mean Absolute Error (MAE) and Mean Relative Error (MRE). The experimental results show that TCN based deep learning framework achieves improved performance with 95.52% accuracy in predicting network congestion. Further, we design the Home IoT network testbed to capture the real network traffic flows as no standard dataset is available

Spectrophotometric Determination of Cilostazol in Tablet Dosage Form

Purpose: To develop simple, rapid and selective spectrophotometric methods for the determination of cilostazol in tablet dosage form. Methods: Cilostazol was dissolved in 50 % methanol and its absorbance was scanned by ultraviolet (UV) spectrophotometry. Both linear regression equation and standard absorptivity were calculated and both methods were validated as per ICH guidelines. Cilostazol was determined in tablet dosage form using these validated methods. Results: The λmax of cilostazol was 258.2 nm in 50 % methanol. Beer-Lambert’s law was obeyed in the concentration range of 0 – 25 μg/ml and standard absorptivity was 420.2 dL.g-1.cm-1 . The numerical values for all the validation parameters were within acceptable limits. The results of cilostazol tablet determination by linear regression equation and standard absorptivity methods indicate purity of 100.0 - 102.4 and 98.7 - 101.1 % with standard deviations of 0.611 and 0.592, respectively. Comparing the methods at 99 % confidence limit, the F-test value was found to be 1.065. Conclusion: These validated methods may be useful for routine analysis of cilostazol as bulk drugs, in dosage forms as well as in dissolution studies in the pharmaceutical industry