Xinheng Wang

Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

Mobile E-Health: the Unwired Evolution of Telemedicine

The movement of telemedicine from desktop platforms to wireless and mobile configurations may have a significant impact on future health care. This paper reviews some of the latest technologies in wireless communication and their application in health care. The new technologies can make the remote medical monitoring, consulting, and health care more flexible and convenient. But, there are challenges for successful wireless telemedicine, which are addressed in this paper.

Microarray image enhancement by denoising using stationary wavelet transform

Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. Its well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.

Integration of hybrid wireless networks in cloud services oriented enterprise information systems

This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

A Continuous Biomedical Signal Acquisition System Based on Compressed Sensing in Body Sensor Networks

The emerging compressed sensing (CS) holds considerable promise for continuously acquiring biomedical signals in body sensor networks (BSNs), which enables nodes to employ a much lower sampling rate than Nyquist while still able to accurately reconstruct signals. CS-based BSNs are expected to significantly enhance the quality of healthcare and improve the ability of prevention, early diagnosis, and treatment of chronic diseases. However, existing BSNs are still unable to support long-term monitoring in healthcare, as well as providing an energy-efficient low communication burden and inexpensive scheme. Capitalizing on the sparsity of biomedical signals in transfer domains, this paper develops a continuous biomedical signal acquisition system, which explores a sparsification model to find the sparse representation of biomedical signals. The sparsified measurements of signals are wirelessly transmitted to a fusion center through BSNs. Meanwhile, a weighted group sparse reconstruction algorithm is proposed to accurately reconstruct the signals at the fusion center. Simulation results show that, on random sampling over BSN, the proposed group sparse algorithm shows good efficiency, strong stability, and robustness.

A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks

The issues inherent in caring for an ever-increasing aged population has been the subject of endless debate and continues to be a hot topic for political discussion. The use of hospital-based facilities for the monitoring of chronic physiological conditions is expensive and ties up key healthcare professionals. The introduction of wireless sensor devices as part of a Wireless Body Area Network (WBAN) integrated within an overall eHealth solution could bring a step change in the remote management of patient healthcare. Sensor devices small enough to be placed either inside or on the human body can form a vital part of an overall health monitoring network. An effectively designed energy efficient WBAN should have a minimal impact on the mobility and lifestyle of the patient. WBAN technology can be deployed within a hospital, care home environment or in the patients own home. This study is a review of the existing research in the area of WBAN technology and in particular protocol adaptation and energy efficient cross-layer design. The research reviews the work carried out across various layers of the protocol stack and highlights how the latest research proposes to resolve the various challenges inherent in remote continual healthcare monitoring.

Application of wavelet modulus maxima in microarray spots recognition

This paper presents a novel approach to recognize the microarray image spots. The approach is based on the detection of wavelet modulus maxima in the microarray images. The detected maxima is actually the contour of the spots and thus the spots are recognized precisely. Then, the intensities within the contour of the spots can be obtained with low error rate. The test results on example image show this is an effective approach, especially for those spots with low intensities.

Adaptive and Secure Load-Balancing Routing Protocol for Service-Oriented Wireless Sensor Networks

Service-oriented architectures for wireless sensor networks (WSNs) have been proposed to provide an integrated platform, where new applications can be rapidly developed through flexible service composition. In WSNs, the existing multipath routing schemes have demonstrated the effectiveness of traffic distribution over multipaths to fulfill the quality of service requirements of applications. However, the failure of links might significantly affect the transmission performance, scalability, reliability, and security of WSNs. Thus, by considering the reliability, congestion control, and security for multipath, it is desirable to design a reliable and service-driven routing scheme to provide efficient and failure-tolerant routing scheme. In this paper, an evaluation metric, path vacant ratio, is proposed to evaluate and then find a set of link-disjoint paths from all available paths. A congestion control and load-balancing algorithm that can adaptively adjust the load over multipaths is proposed. A threshold sharing algorithm is applied to split the packets into multiple segments that will be delivered via multipaths to the destination depending on the path vacant ratio. Simulations demonstrate the performance of the adaptive and secure load-balance routing scheme.

QoS scheme for multimedia multicast communications over wireless mesh networks

A quality of service (QoS) scheme for multimedia multicast communications in wireless mesh networks (WMNs) is proposed in this study. It uses a new bandwidth calculation scheme to provide rate-adaptive admission control. It relies on information it receives from the network and application layers to calculate the network bandwidth consumption and operates independently of the media access control (MAC) layer. Using the proposed QoS scheme, the network layer provides feedback on network congestion to the application layer. The multimedia multicast sender adapts the real-time data transmission rate based on the network congestion feedback it receives. In this study, the authors describe the detailed architecture of the proposed QoS scheme. Furthermore, the authors have implemented the QoS scheme in our previously developed uni-directional link aware multicast extension to AODV (UDL-MAODV) routing protocol. The authors present validation tests to ensure the correct functionality of the QoS algorithm using our SwanMesh WMN testbed. The authors have also performed simulation tests to evaluate the performance of the proposed scheme. The simulation results show the effectiveness of the proposed QoS scheme.

Adaptive Task Scheduling Strategy Based on Dynamic Workload Adjustment for Heterogeneous Hadoop Clusters

The original task scheduling algorithm of Hadoop cannot meet the performance requirements of heterogeneous clusters. According to the dynamic change of load of each task node and the difference of node performance of different tasks in the heterogeneous Hadoop cluster, a novel adaptive task scheduling strategy based on dynamic workload adjustment (ATSDWA) is presented. With ATSDWA, tasktrackers can adapt to the change of load at runtime, obtain tasks in accordance with the computing ability of their own, and realize the self-regulation, while avoiding the complexity of algorithm, which is the prime reason to make jobtracker the system performance bottleneck. Experimental results show that ATSDWA is a highly efficient and reliable algorithm, which can make heterogeneous Hadoop clusters stable, scalable, efficient, and load balancing. Furthermore, its performance is superior to the original and improved task scheduling strategy of Hadoop, from the aspects of the execution time of tasks, the resource utilization, and the speed-up ratio.