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

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.

[1]  Rekha Jain,et al.  Wireless Sensor Network -A Survey , 2013 .

[2]  Wen Hu,et al.  Efficient Computation of Robust Average of Compressive Sensing Data in Wireless Sensor Networks in the Presence of Sensor Faults , 2013, IEEE Transactions on Parallel and Distributed Systems.

[3]  Weifeng Liu,et al.  Kernel Adaptive Filtering , 2010 .

[4]  Omid Khayat,et al.  Gradient-based back-propagation dynamical iterative learning scheme for the neuro-fuzzy inference system , 2016, Expert Syst. J. Knowl. Eng..

[5]  Liam Paninski,et al.  Estimation of Entropy and Mutual Information , 2003, Neural Computation.

[6]  Chi-Tsun Cheng,et al.  A Delay-Aware Network Structure for Wireless Sensor Networks With In-Network Data Fusion , 2013, IEEE Sensors Journal.

[7]  Radislav Smid,et al.  Quality-Based Multiple-Sensor Fusion in an Industrial Wireless Sensor Network for MCM , 2014, IEEE Transactions on Industrial Electronics.

[8]  Victor C. M. Leung,et al.  Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integratedwith Mobile Cloud Computing , 2015, IEEE Transactions on Computers.

[9]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[10]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[11]  Claudia Canali,et al.  Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms , 2014, J. Parallel Distributed Comput..

[12]  Eduardo F. Nakamura,et al.  Information fusion for wireless sensor networks: Methods, models, and classifications , 2007, CSUR.

[13]  Xiong Luo,et al.  A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks , 2015 .

[14]  Wenzhong Shi,et al.  Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Joel J. P. C. Rodrigues,et al.  Wireless Sensor Networks: a Survey on Environmental Monitoring , 2011, J. Commun..

[16]  Xiong Luo,et al.  A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems , 2016, Future Gener. Comput. Syst..

[17]  S Craciun,et al.  Wireless Transmission of Neural Signals Using Entropy and Mutual Information Compression , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Wei Hong,et al.  Approximate Data Collection in Sensor Networks using Probabilistic Models , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[19]  Badong Chen,et al.  Universal Approximation with Convex Optimization: Gimmick or Reality? [Discussion Forum] , 2015, IEEE Computational Intelligence Magazine.

[20]  Yi Liang,et al.  A Survey on Topology Control in Wireless Sensor Networks: Taxonomy, Comparative Study, and Open Issues , 2008, Proceedings of the IEEE.

[21]  Badong Chen,et al.  Quantized Kernel Recursive Least Squares Algorithm , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Enzo Baccarelli,et al.  Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study , 2016, IEEE Network.

[23]  Chen Feiyan,et al.  Variable smoothing parameter of the double exponential smoothing forecasting model and its application , 2012, The 2012 International Conference on Advanced Mechatronic Systems.

[24]  Cui Li.  Forecast-based temporal data aggregation in wireless sensor networks , 2007 .

[25]  Xiaohui Chang,et al.  A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm , 2016, Comput. Networks.

[26]  Han-Chieh Chao,et al.  Verifiable, privacy-assured, and accurate signal collection for cloud-assisted wireless sensor networks , 2015, IEEE Communications Magazine.

[27]  Seungmin Rho,et al.  Intelligent context-aware energy management using the incremental simultaneous method in future wireless sensor networks and computing systems , 2013, EURASIP J. Wirel. Commun. Netw..

[28]  Lie-Liang Yang,et al.  Low-complexity noncoherent fusion rules for wireless sensor networks monitoring multiple events , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[29]  Badong Chen,et al.  Quantized Kernel Least Mean Square Algorithm , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  S. Haykin,et al.  Kernel Least‐Mean‐Square Algorithm , 2010 .

[32]  Badong Chen,et al.  Mean square convergence analysis for kernel least mean square algorithm , 2012, Signal Process..

[33]  Keechoo Choi,et al.  Solving the stochastic multi-class traffic assignment problem with asymmetric interactions, route overlapping, and vehicle restrictions , 2016 .

[34]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.