A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Luo, Xiong; Zhang, Dandan; Yang, Laurence T.; Liu, Ji; Chang, Xiaohui; Ning, Huansheng

doi:10.1016/j.future.2015.10.022

Cited by 101 publications

(13 citation statements)

References 31 publications

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“…With the supervised autonomous system design, we believe machine learning and artificial intelligence [161][162][163][164][165], in conjunction with multimodal sensing, will play much more important roles in the next generation autism diagnosis and treatment programs. Considering that good therapists are always in very high demand for the autism population, we envisage that a therapist could operate multiple supervised autonomous systems remotely at different sites, which could benefits more children with ASD while reducing the cost of autism care.…”

Section: Discussionmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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Section: Discussionmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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“…As mentioned above, because these structural, semistructured, and unstructured multimodal geological big data are integrated together, the data heterogeneity is obvious in big data analysis. Then, data aggregation as the key technology in achieving data extraction and transformation [20] enables data sharing and data fusion between heterogeneous data sources. Through Characteristics: "4V" the use of heterogeneous information aggregation technologies, the unified data retrieval and data presentation could be achieved.…”

Section: Aggregation Of Geological Big Datamentioning

confidence: 99%

Big Data Management for Cloud-Enabled Geological Information Services

Zhu

Tan

Luo

et al. 2018

Scientific Programming

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Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

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“…However, its precision would be worse than GM-LSSVM in some applications. Although those schemes improve the performance relating to some metrics, it may be difficult to achieve a good tradeoff between accuracy and computational efforts in WSN [23].…”

Section: Prediction Schemes In Wsnmentioning

confidence: 99%

“…With E-KLMS, the computational complex could be accordingly improved while achieving high-quality solutions. It is worth to note that, different from our previous works [11,23] in which much time are spent in data training because those methods need to construct training set at every sampling point, the samples are only trained once with the proposed method in this article, which will save much time. In consideration of the above reasons, E-KLMS may perform better in data prediction in WSN.…”

Section: Introductionmentioning

confidence: 96%

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

Luo

Zhang

et al. 2016

Entropy

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Abstract:With the recent emergence of wireless sensor networks (WSNs) in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS) algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What's more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time.

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A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Cited by 101 publications

References 31 publications

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Big Data Management for Cloud-Enabled Geological Information Services

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

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