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Wireless sensor networks (WSNs) consist of numbers of small nodes that can sense, collect, and disseminate information for many different types of applications. One of these applications is subject tracking and monitoring, in which the monitored subjects often need protection. For instance, a WSN can be deployed to monitor the movement of a panda in a large park. The panda needs protection from different adversaries. An adversary might trace the messages in the WSNs to find the source node that sensed the panda, with the final aim of killing the panda. Hence the question is: how do we hide the location of the source node from the adversary? In other words, the problem is to provide privacy to the source node: Providing source location privacy (SLP) is complicated by the fact that there are many factors that influence the effectiveness of a solution. The problem with most of the existing solutions is that they only provide SLP in a few specific scenarios. We propose a distributed solution for source location privacy using the Fake source and Phantom routing (FSAPR) protocol.

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This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson's telemonitoring dataset to predict Parkinson's disease (PD) progression. PD is a chronic and progressive nervous system disorder that affects body movement. PD is assessed by using the unified Parkinson's disease rating scale (UPDRS). In this paper, firstly, principal component analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset and to reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN model with a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predicting Motor and Total-UPDRS score. The model's performance is evaluated by conducting several experiments and the result is compared with the result of previously developed methods on the same dataset. The model's prediction accuracy is measured by fitness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R 2 ). The MAE, RMSE, and R 2 values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221, and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and efficacy of the proposed method for predicting the UPDRS score in PD progression.

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