Multi-level Data Fusion of Environmental Data in Future Internet Applications

The rapid increase in environmental observations which are conducted by SMEs, communities and volunteers using affordable in situ sensors at various scales, together with the more established observatories set up by environmental and space agencies using airborne and space-borne sensing technologies is generating serious amounts of BIG data at ever increasing rates. Furthermore, the emergence of Future Internet technologies and the urgent requirements for the deployment of specific enablers for the delivery of processed environmental knowledge in real-time with advanced situation awareness to citizens has reached greater imminence. It is now highly critical to build and provide services which automate the aggregation of data from various sources, while surmounting the semantic gaps, conflicts and heterogeneity in data sources. The early stages of aggregation of data enable the preprocessing of data generated from multiple sources with the reconciliation between temporal gaps in observation time series, and alignment of their respective asynchronicities. As a result, multi-level processes of fusion need to be implemented and made accessible to large communities of users using future internet services. This paper presents the process and the preliminary results using RBF networks methods for the spatial fusion of water quality observations and measurements from asynchronous space-borne, in situ and validated models simulation data sources in the Irish Sea.

[1]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[2]  Yongzhong Shi,et al.  A novel feature-based and application-oriented approach to Marine Sub-bottom Acoustic spatial data fusion , 2012, 2012 20th International Conference on Geoinformatics.

[3]  Adrian G. Bors,et al.  Introduction of the Radial Basis Function (RBF) Networks , 2000 .

[4]  Stuart E. Middleton,et al.  Knowledge-Based Service Architecture for Multi-risk Environmental Decision Support Applications , 2011, ISESS.

[5]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[6]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[7]  Weiming Xu,et al.  A Fusion Method of Heterogeneous Information for Sea-surface Wireless Sensor Networks Positioning , 2012, 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring.

[8]  Ioannis Pitas,et al.  Multimodal decision-level fusion for person authentication , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[9]  Robert M. Lewitt,et al.  Practical considerations for 3-D image reconstruction using spherically symmetric volume elements , 1996, IEEE Trans. Medical Imaging.

[10]  Zhengzhi Han,et al.  A New Disaster Monitor and Forecast System Based on RBF Neural Networks , 2010, 2010 International Conference on Electrical and Control Engineering.