Parkinson’s disease (PD) is a chronic neurodegenerative disorder resulting from the progressive loss of dopaminergic nerve cells. People with PD usually demonstrate deficits in performing basic daily activities, and the relevant annual social cost can reach about $25 billion in the United States. Early detection of PD plays an important role in symptom relief and improvement in performance of activities in daily life (ADL), which eventually reduces societal and economic burden. However, conventional PD detection methods are inconvenient in daily life (e.g., requiring users to wear sensors). To overcome this challenge, we propose and identify the nonspeech body sounds as the new PD biomarker, and utilize the data in smartphone usage to realize the passive PD detection in daily life without interrupting the user. Specifically, we present PDVocal, an end-to-end smartphone-based privacypreserving system towards early PD detection. PDVocal can passively recognize the PD digital biomarkers in the voice data during daily phone conversation. At the user end, PDVocal filters the audio stream and only extracts the non-speech body sounds (e.g., breathing, clearing throat and swallowing) which contain no privacy-sensitive content. At the cloud end, PDVocal analyzes the body sounds of interest and assesses the health condition using a customized residual network. For the sake of reliability in real-world PD detection, we investigate the method of the performance optimizer including an opportunistic learning knob and a long-term tracking protocol. We evaluate our proposed PDVocal on a collected dataset from 890 participants and real-life conversations from publicly available data sources. Results indicate that nonspeech body sounds are a promising digital biomarker for privacy-preserving PD detection in daily life.

With the emergence of edge computing in various applications domains, end users are now surrounded by a fast growing volume of data from edge devices belonging to different stakeholders. However, these edge devices cannot cooperate to share big data because of the distrust among them. In this paper, the blockchain is deployed in collaborative edges by exploiting the non-repudiation and non-tampering properties to enable trust. First, we develop a blockchain based big data sharing framework in collaborative edges for adapting to the limited computational and storage resources in edge devices. Then, a consensus mechanism called Proof-of-Collaboration (PoC) is proposed for computational resources reduction in our proposed framework, where edge devices offer their credits of PoC to compete for the block generation. Moreover, we put forward a futile transaction filter algorithm for transaction offloading, greatly reducing the storage resources occupied by the blockchain in edges. Extensive experiments are performed to demonstrate the superior performance of our proposal.

Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps in forecasting. To address this issue, we first propose a new electricity price forecasting framework. It is significant to design a system tool chain based on big data analytics for ensuring that the users can make appropriate decisions. To this end, three algorithms are proposed integratedly. First, feature redundancy elimination is implemented by the fusion of Grey Correlation Analysis (GCA) and ReliefF algorithm. Second, a combination of Kernel function and Principle Component Analysis (KPCA) is designed to achieve dimensionality reduction. Finally, Support Vector Machine (SVM) optimization algorithm based on differential evolution (DE) is proposed to forecast price classification. These three modules jointly power the price forecasting system. Simulation results show the superiority of our proposed framework.

With increase demand on wireless services, equipment supporting multimedia applications has been becoming more and more popular in recent years. With billions of devices involved in mobile Internet, data volume is undergoing an extremely rapid growth. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive study has been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this paper, a hybrid-stream big data analytics model is proposed to perform big data video analysis. This model contains four procedures, i.e., data preprocessing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to obtain the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. Then, a reliable key frame extraction mechanism will recognize the importance of each frame or clip and then decide whether to abandon it automatically by a series of correlation operations. Simulation results illustrate that the size of the processed video has been effectively reduced. The simulation also shows that proposed model performs steadily and is robust enough to keep up with the big data crush in multimedia era.

The advances of network technology and mobile communication technology are making eHealth possible. In eHealth systems, physiological data and relevant context-aware data are acquired continuously and in real time. At the same time, such large-scale data results in huge challenges in the aspect of real-time big data processing since eHealth data appears in the form of data stream. Therefore, we propose a novel incremental learning algorithm, namely α-SVMSGD, which improves the SVMSGD (Support Vector Machine-Stochastic Gradient Descent) algorithm by updating the training data with the continuous data stream. Besides, this α-SVMSGD may handle the problem that original SVMSGD cannot further mine the useful information in unclassified data. In α-SVMSGD, the process of training data updating is completed by introducing the concept of forgetting mechanism, in which the forgetting factor α is introduced to weed out useless training data. α-SVMSGD is applied into ambient assisted living communications, and further incorporated into the data filtering layer of a local data processing architecture (LDPA) to reduce data redundancy. Simulation results confirm that the proposed algorithm is a promising data redundancy solution for classification without loss of accuracy in the case of real-time data stream.