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Advanced Technology for Sensor Clouds

Advanced Technology for Sensor Clouds. Geoffrey Fox gcf@indiana.edu http://www.infomall.org http://www.futuregrid.org Director, Digital Science Center, Pervasive Technology Institute Associate Dean for Research and Graduate Studies,  School of Informatics and Computing

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Advanced Technology for Sensor Clouds

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  1. Advanced Technology for Sensor Clouds Geoffrey Fox gcf@indiana.edu http://www.infomall.orghttp://www.futuregrid.org Director, Digital Science Center, Pervasive Technology Institute Associate Dean for Research and Graduate Studies,  School of Informatics and Computing Indiana University Bloomington Ball Aerospace Dayton OH November 5 2010

  2. Indiana University Tasks • Core SCGMMS (Sensor Centric Grid Middleware Management System) System Integration for applications, trust and cloud • Evolve SCGMMS for new client and cloud technologies • Cloud and Grid Computing • Track rapidly changing technology • Develop layered cloud for geographic and security reasons • Vulnerability analysis: Exposure and Defense

  3. Core SCGMMS • Indiana University has obtained a license for source of current SCGMMS software developed by Anabas and will base its work on this. • Given the size and complexity of the software, we need Anabas support for this and this is funded through the Anabas subcontract. • Using this software ensures that SCGMMS will be operational throughout the next phase. • First step is develop expertise in this software and understand key areas where we can expect changes. • These includes exploitation of new backend (cloud and grid) features, client interface, security issues in communication and services, and integration of new sensor features. • Note one core component of SCGMMS is the NaradaBrokering messaging system which was developed at Indiana University and here we have expertise. We will look at use of other messaging systems ActiveMQ, RabbitMQ etc.

  4. SCGMMS Evolution • Rework a few key features of the core SCGMMS to enable it to exploit modern technology and architectures more easily. • Note Anabas SGX has removed unnecessary Impromptu server side components • These include the client interface where will move away from the heavy weight Anabas custom client to a light weight browser based approach suitable for tablets and smartphones (light weight clients of future). Desktops/laptops still supported • Further we will exploit HTML5 and its supported codecs including MPEG4 H.264 and WebM/VP8 to build a better multimedia support. • We will move key computing tasks to the (layered) cloud.

  5. Hybrid/Layered Clouds • Layered clouds needed to support hierarchical computing with basic analysis near sensor and back ends for • Additional resources if local system overloaded • Integrated analysis of multiple sensor constellations • Hybrid private-public cloud for security reasons • Sensitive computations on private cloud producing anonymized/non-sensitive results sent to public cloud • Support with layered Twister/MapReduce runtime and Sawzall open source language

  6. Research on Side-channel Detection & Mitigation • White-box vulnerability analysis (with source code) • Improve our prototype, making it work on web apps built upon different platforms • Black-box vulnerability analysis (without source code) • Automatic detection and quantification of side-channel leaks without access to the source code • Defense: Traffic obfuscation infrastructure • Need infrastructure on the platform layer (browser/web server) to automatically obfuscate web app traffic. • Defense: Source-to-source transformation • Automatically convert a program to avoid side-channel leaks

  7. Future Sensory Malware Projects • Distributed sensory mining • Sound mining to locate persons of interest based on speech • Activity mining with accelerometers to detect group activity patterns • Video mining for sensitive video • Defensive architectures • Context sensitive sensor access to learn when sensor access should be blocked

  8. Sensor to Sensor Infection Dynamics • Vulnerability Analysis: Determine the plausibility of malware to transmit from sensor to sensor via wireless signals and create an epidemic assuming human dynamics in dense metropolitan settings. • Understand Epidemic Dynamics • Effects of infection time, initial infected nodes, metropolitan density, circadian rhythms, etc…. • Builds on work using smartphones to geolocate phones not in the sensor network.

  9. Sensor Theft & Loss Prevention • Aggregate Risk Engine Structure: • SVM or other non-linear classifier • Empirically evaluate benefits of multiple sensors in risk analysis • Determine which sensor information is most useful to aggregator. • Other Sensors • Phone call & Application use patterns • (stays within Reality Mining Data Set)

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