SIROM3 -- A Scalable Intelligent Roaming Multi-modal Multi-sensor Framework

Understanding the future transportation infrastructure performance demands a smart Cyber-Physical Systems (CPS) approach integrating heterogeneous sensors, versatile computing systems, and mobile agents. However, due to sensor versatility and computing intricacy, designing such systems faces challenges of immense complexity in mobile sensor fusion, big data handling, system scalability, and integration. This paper introduces SIROM3, a Scalable Intelligent Roaming Multi-Modal Multi-Sensor framework, for next generation transportation infrastructure performance inspection. SIROM3 offers a scalable and expandable framework through orthogonally abstracting software / hardware structures in a layered Run-Time Environment (RTE), which facilities sensor fusion, distributed computing, communication and mobile services. A Heterogeneous Stream File-system Overlay (HSFO) and a flexible plug-in system (PLEX) are embedded in SIROM3 to simplify big data storage, processing, and correlation. To evaluate the scalability of SIROM, we implemented a mobile sensing system of 30 heterogeneous sensors and 5 computing platforms coordinated by 1 data center. SIROM's expandability is highlighted by adding an advanced radar platform which required less than 50 lines of C++ code for integration. Over 20 terabytes of data covering 300 miles have been collected, aggregated, and fused using SIROM3 for comprehending the pavement dynamics of the entire city of Brockton, MA. SIROM3 offers a unified solution and ideal research platform for rapid, intelligent and comprehensive evaluation of tomorrow's transportation infrastructure performance using heterogeneous systems.

[1]  Yang Zhang,et al.  ICEDB: Intermittently-Connected Continuous Query Processing , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[2]  Ralf Birken,et al.  VOTERS: design of a mobile multi-modal multi-sensor system , 2012, SensorKDD '12.

[3]  Yiannos Manoli,et al.  Multi-modal sensor data and information fusion for localization in indoor environments , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[4]  Kameswari Chebrolu,et al.  Brimon: a sensor network system for railway bridge monitoring , 2008, MobiSys '08.

[5]  Christopher D. Gill,et al.  Design and performance of a fault-tolerant real-time CORBA event service , 2006, 18th Euromicro Conference on Real-Time Systems (ECRTS'06).

[6]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[7]  Kang Lee,et al.  IEEE 1588 standard for a precision clock synchronization protocol for networked measurement and control systems , 2002, 2nd ISA/IEEE Sensors for Industry Conference,.

[8]  Costin Barbu,et al.  Distributed multi-modal sensor system for searching a foliage-covered region , 2011, 2011 IEEE Conference on Technologies for Practical Robot Applications.

[9]  J S Moulthrop,et al.  PRINCIPLES OF PAVEMENT PRESERVATION: DEFINITIONS, BENEFITS, ISSUES, AND BARRIERS , 2003 .

[10]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[11]  Nenad Gucunski,et al.  Multiple Complementary Nondestructive Evaluation Technologies for Condition Assessment of Concrete Bridge Decks , 2010 .

[12]  Sanjiv Singh,et al.  The 2005 DARPA Grand Challenge , 2007 .

[13]  Francesco Flammini,et al.  Towards Wireless Sensor Networks for railway infrastructure monitoring , 2010, Electrical Systems for Aircraft, Railway and Ship Propulsion.

[14]  Maurizio Bocca,et al.  Structural Health Monitoring in Wireless Sensor Networks by the Embedded Goertzel Algorithm , 2011, 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems.

[15]  Yo-Ming Hsieh,et al.  A scalable IT infrastructure for automated monitoring systems based on the distributed computing technique using simple object access protocol Web-services , 2009 .

[16]  Hari Balakrishnan,et al.  Cabernet: vehicular content delivery using WiFi , 2008, MobiCom '08.

[17]  Christopher L. Barnes,et al.  EFFECTIVENESS OF GROUND PENETRATING RADAR IN PREDICTING DECK REPAIR QUANTITIES , 2004 .

[18]  Sanjiv Singh,et al.  The 2005 DARPA Grand Challenge: The Great Robot Race , 2007 .

[19]  Levent Gürgen,et al.  SStreaMWare: a service oriented middleware for heterogeneous sensor data management , 2008, ICPS '08.

[20]  Levent Gürgen,et al.  A scalable architecture for heterogeneous sensor management , 2005, 16th International Workshop on Database and Expert Systems Applications (DEXA'05).

[21]  Robert Kozma,et al.  Multi-modal sensor system integrating COTS technology for surveillance and tracking , 2010, 2010 IEEE Radar Conference.

[22]  Luke Fletcher,et al.  A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge , 2010, Int. J. Robotics Res..

[23]  Chenyang Lu,et al.  Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks , 2010, IEEE Transactions on Parallel and Distributed Systems.

[24]  Kenneth Maser BRIDGE DECK CONDITION SURVEYS USING RADAR: CASE STUDIES OF 28 NEW ENGLAND DECKS , 1991 .

[25]  Mitja Jurgele Life cycle cost analysis in pavement design , 2013 .

[26]  Douglas C. Schmidt,et al.  An overview of the Real-Time CORBA specification , 2000, Computer.

[27]  L B Stevens ROAD SURFACE MANAGEMENT FOR LOCAL GOVERNMENTS. RESOURCE NOTEBOOK , 1985 .

[28]  Shaojie Tang,et al.  Distributed Sensing for High Quality Structural Health Monitoring Using Wireless Sensor Networks , 2012, 2012 IEEE 33rd Real-Time Systems Symposium.

[29]  Ralf Birken,et al.  Characterization and detection of bridge deck deterioration , 2012 .

[30]  David L. Mills,et al.  Network Time Protocol (Version 3) Specification, Implementation and Analysis , 1992, RFC.

[31]  Aaron Helsinger,et al.  Cougaar: a scalable, distributed multi-agent architecture , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).