Naixue Xiong

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Environmental monitoring is one of the most important applications of wireless sensor networks (WSNs), which usually requires a lifetime of several months, or even years. However, the inherent restriction of energy carried within the battery of sensor nodes brings an extreme difficulty to obtain a satisfactory network lifetime, which becomes a bottleneck in scale of such applications in WSNs. In this paper, we propose a novel framework with dedicated combination of data prediction, compression, and recovery to simultaneously achieve accuracy and efficiency of the data processing in clustered WSNs. The main aim of the framework is to reduce the communication cost while guaranteeing the data processing and data prediction accuracy. In this framework, data prediction is achieved by implementing the Least Mean Square (LMS) dual prediction algorithm with optimal step size by minimizing the mean-square derivation (MSD), in a way that the cluster heads (CHs) can obtain a good approximation of the real data from the sensor nodes. On this basis, a centralized Principal Component Analysis (PCA) technique is utilized to perform the compression and recovery for the predicted data on the CHs and the sink, separately in order to save the communication cost and to eliminate the spatial redundancy of the sensed data about environment. All errors generated in these processes are finally evaluated theoretically, which come out to be controllable. Based on the theoretical analysis, we design a number of algorithms for implementation. Simulation results by using the real world data demonstrate that our framework provides a cost-effective solution to such as environmental monitoring applications in cluster based WSNs.

Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things

Abstract Internet of Things will serve communities across the different domains of life. Tracking mobile targets is one important system engineering application in IOT, and the resource of embedded devices and objects working under IoT implementation are constrained. Thus, building a scheme to make full use of energy is key issue for mobile target tracking applications. To achieve both energy efficiency and high monitoring performance, an effective Knowledge-aware Proactive Nodes Selection (KPNS) system is proposed in this paper. The innovations of KPNS are as follows: 1) the number of proactive nodes are dynamically adjusted based on prediction accuracy of target trajectory. If the prediction accuracy is high, the number of proactive nodes in the non-main predicted area will be decreased. If prediction accuracy of moving trajectory is low, large number of proactive nodes will be selected to enhance monitoring quality. 2) KPNS takes full advantage of energy to further enhance target tracking performance by properly selecting more proactive nodes in the network. We evaluated the efficiency of KPNS with both theory analysis and simulation based experiments. The experimental results demonstrate that compared with Probability-based target Prediction and Sleep Scheduling strategy (PPSS), KPNS scheme improves the energy efficiency by 60%, and can reduce target missing rate and tracking delay to 66%, 75% respectively.

QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks

Millions of sensors are deployed to monitor the smart grid. They consume huge amounts of energy in the communication infrastructure. Therefore, the establishment of an energy-efficient medium access control (MAC) protocol for sensor nodes is challenging and urgently needed. The Quorum-based MAC protocol independently and adaptively schedules nodes’ wake-up times and decreases idle listening and collisions, thereby increasing the network throughput and extending the network lifetime. A novel Quorum time slot adaptive condensing (QTSAC)-based MAC protocol is proposed for achieving delay minimization and energy efficiency for the wireless sensor networks (WSNs). Compared to previous protocols, the QTSAC-based MAC protocol has two main novelties: 1) It selects more Quorum time slots (QTSs) than previous protocols in the area that is far from the sink according to the energy consumption in WSNs to decrease the network latency and 2) It allocates QTSs only when data are transmitted to further decrease the network latency. Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols. For intermediate-scale wireless sensor networks, the method that is proposed in this paper can enhance the energy efficiency by 24.64%–82.75%, prolong the network lifetime by 58%–27.31%, and lower the network latency by 3.59%–29.23%.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Integrated collaborative filtering recommendation in social cyber-physical systems:

Cyber-physical systems are becoming part of our daily life, and a large number of data are generated at such an unprecedented rate that it becomes larger than ever before in social cyber-physical systems. As a consequence, it is highly desired to process these big data so that meaningful knowledge can be extracted from those vast and diverse data. Based on those large-scale data, using collaborative filtering recommendation methods to recommend some valuable clients or products for those e-commerce websites or users is considered as an effective way. In this work, we present an integrated collaborative filtering recommendation approach that combines item ratings, user ratings, and social trust for making better recommendations. In contrast to previous collaborative filtering recommendation works, integrated collaborative filtering recommendation approach takes full advantage of the correlation between data and takes into consideration the similarity between items, the similarity between users and two kinds of trust among users to select nearest neighbors of both users and items providing the most valuable information for recommendation. On the basis of neighbors selected, integrated collaborative filtering recommendation provides an approach combining two aspects to recommend valuable and suitable items for users. And the concrete process is illustrated as following: (1) the potentially interesting items are obtained by the shopping records of neighbors of a certain user, (2) the potentially interesting items are figured out according to the item neighbors of those items of the user, and (3) determine a few most interesting items combining the two sets of potential items obtained from previous process. A large number of experimental results show that the proposed integrated collaborative filtering recommendation approach can effectively enhance the recommendation performance in terms of mean absolute error and root mean square error. Integrated collaborative filtering recommendation approach could reduce mean absolute error and root mean square error by up to 27.5% and 15.7%, respectively.

A Game-Based Localized Multi-Objective Topology Control Scheme in Heterogeneous Wireless Networks

In wireless networks, network topology may change at any time. Therefore, topology control is one of the effective methods to get and keep the desired topology performance. The most existing topology control methods assume that nodes are altruistic. Although there are some game-based topology control schemes to stimulate cooperation between nodes, they only consider a single objective (e.g., energy consumption or network lifetime), which cannot be adaptive to the variation of demand on topology performance. To address these weaknesses, we present the notion of link lifetime and model the multi-objective weight sum of any link as the function with respect to transmission power, link delay and link lifetime. Then the proposed game-based localized multi-objective topology control ensures that the desired topology property exists in resulting topology, in which the presented Improved LOCAL

Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs

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A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments

-Improvement Algorithm (LDIA) algorithm not only stimulates nodes’ cooperation on topology control operation and ensures network’s convergence to a steady state, but also has the better performance with respect to executing time and communication overhead than a classic algorithm, i.e., LDIA. Finally, the simulation results show that, by employing appropriate weight values, when compared with some typical schemes considering only energy efficiency, the proposed scheme is the most efficient in regard to average link delay and link lifetime. When compared with a typical scheme considering only network lifetime, the proposed scheme has advantage over average link lifetime, but it is slightly worse in terms of average link delay. Although the proposed scheme is less efficient in terms of average transmission power, where the shortage may be alleviated by adjusting weight values, it satisfies diversified demands for applications due to its flexibility.

Scalable parameter tuning for AVQ

Existing methods for tracking mobile sinks in Wireless Sensor Networks (WSNs) often incur considerable energy consumption and overhead. To address this issue, we propose a Detour-Aware Mobile Sink Tracking (DAMST) method via analysis of movement angle changes of mobile sinks, for collecting data in a low-overhead and energy efficient way. In the proposed method, while a mobile sink passes through a region, it appoints a specific node as a region agent to collect information of the whole region, and records nodes near or on its trajectory as footprints. If it needs information from the region agent in a future time it will construct an energy efficient path from the region agent to itself by calculating its own movement angles according to the footprints, as well as getting rid of detours by analyzing these movement angles. Finally, the performance of the tracking method is evaluated systematically under different trajectory patterns and footprint appointment intervals. The simulation results consolidate that DAMST has advantages in reducing energy consumption and data overhead.

A Stochastic Control Approach to Maximize Profit on Service Provisioning for Mobile Cloudlet Platforms

The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle big data problems. One of the biggest challenges in multiclouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the existing research works either fail to satisfy the quality of service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called multiclouds partial critical paths with pretreatment (MCPCPP), for big data workflows in multiclouds is presented. This algorithm incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into consideration the essential characteristics of multiclouds such as the charge per time interval, various instance types from different cloud providers, as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.