I-SENSE: A Light-Weight Middleware for Embedded Multi-Sensor Data-Fusion

In our I-SENSE project we demonstrate the combination the scientific research areas multi-sensor data fusion and pervasive embedded computing. The main idea is to provide a generic architecture which supports a distributed data fusion on an embedded system. Due to the high onboard processing and communication power of the used hardware, our proposed architecture is designed to perform sophisticated data fusion tasks. Another goal of I-SENSE research project addresses the reconfiguration of a distributed system at runtime, thus, to be able to react to changes in the system's environment dynamically. This paper though gives an overlook of our developed middleware which eases the development of distributed fusion applications on embedded systems and which includes reconfiguration facilities. We further present some experimental results obtained using our middleware and give an outlook of our ongoing research.

[1]  Umakishore Ramachandran,et al.  DFuse: a framework for distributed data fusion , 2003, SenSys '03.

[2]  Bernhard Rinner,et al.  Enhanced Least Squares Support Vector Machines for Decision Modeling in a Multi-Sensor Fusion Framework , 2007, Artificial Intelligence and Pattern Recognition.

[3]  Bernhard Rinner,et al.  An improved genetic algorithm for task allocation in distributed embedded systems , 2007, GECCO '07.

[4]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[5]  C. Siva Ram Murthy,et al.  Optimal task allocation in distributed systems by graph matching and state space search , 1999, J. Syst. Softw..

[6]  Hermann Kopetz,et al.  Real-time systems , 2018, CSC '73.

[7]  Bernhard Rinner,et al.  Intelligent Traffic Video Sensor: Architecture and Applications , 2003 .

[8]  Edward A. Lee,et al.  Software Synthesis from Dataflow Graphs , 1996 .

[9]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[10]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[12]  Bernhard Rinner,et al.  I-SENSE: Intelligent Embedded Multi-Sensor Fusion , 2006, 2006 International Workshop on Intelligent Solutions in Embedded Systems.

[13]  小倩,et al.  Fusion Rings for Degenerate Minimal Models , 2002 .

[14]  Bernhard Rinner,et al.  Distributed embedded smart cameras for surveillance applications , 2006, Computer.

[15]  Johan A. K. Suykens,et al.  Least squares support vector machine classifiers: a large scale algorithm , 1999 .

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  M. J. Dalgleish Vehicle detection for advanced transport telematics , 1994 .

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .