IEEE T-ASE Special Issue on Cloud Robotics and Automation - Deadline: March 15

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CALL FOR PAPERS
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Special Issue on Cloud Robotics and Automation

***NEW EXTENDED DEADLINE: March 15th***

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This Special Issue addresses the potential of the “Cloud” (Internet) to enhance automation and robotics for manufacturing, healthcare, transportation, logistics, security, agriculture, and many related industries by improving performance in at least five ways: 1) Big Data: indexing a global library of maps and object data; 2) Cloud Computing: parallel grid computing on demand for automation; 3) Open-Source/Open-Access: humans sharing code, data, algorithms and hardware designs; 4) System Learning: machines sharing parameters, control policies and outcomes; and 5) Crowdsourcing/call centers: offline and ondemand human guidance for evaluation, learning and error recovery.

Cloud Robotics and Automation is attracting increased interest from academia, governments, and industry worldwide. General Electric’s “Industrial Internet” aims to create a “convergence of machine and intelligent data” across industries. Germany’s “Industry 4.0” project and IBM’s “Smarter Planet” initiative are closely related. The “Internet of Things” project considers the potential where many passive physical objects such as boxes and pills have processors and/or unique RFID tags. The “RoboEarth” project is pioneering the idea of a World Wide Web for robots.

Topics to be covered include:

- Scalable parallelization: How can parallel grid-based computing on demand change the current paradigm in automation science? How can parallelization schemes scale with the size of automation infrastructure?

- Effective load balancing: Balancing operations between local and cloud computation. Where should we compute for sensing, planning and actuation?

- Knowledge bases and representations: Reuse and interoperability of databases. How should online knowledge bases be shared and grow?

- Collective learning: How can an automation infrastructure search for relevant data? How can robots share and learn from experienced outcomes?

- Infrastructure/Platform or Software as a Service: To what extent can existing cloud technologies be adapted for automation and robotics? What algorithmic or technical advances are needed to allow systems to use the powerful computational, storage, and network infrastructure of data centers?

- Internet of Things: What advances complement the IoT’s sensor technologies with a physical layer for actuation? As sensors are finding their way into more mobile devices, will this increase the demand for cloud based applications?

- Big Data: Data, collected and/or disseminated over large, accessible networks can enable decisions for classification problems or reveal patterns.

- Wireless communication, security and privacy issues: How can cloud-based automation be robust to latency, bandwidth limits, network failures and attacks?

- System architectures: What architectures optimize trade-offs between content aggregation and caching vs. accessibility and scalability vs. response time forautomation and robotics applications?

- Open-source, open-access infrastructures: Algorithms and interfaces that introduce human feedback in automation through crowdsourcing. How can human guidance be used for evaluation, learning, and error recovery?

Important dates:
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March 15th, 2014: Paper submission.
October 1st, 2014: Final Revision.
January, 2015: Publication date.

Guest Editors:
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Dr. Javier Civera, Univ. of Zaragoza, Spain (Lead)
Dr. Matei Ciocarlie, Willow Garage,CA, USA
Dr. Alper Aydemir, NASA/JPL, CA, USA
Dr. Kostas Bekris, Rutgers University, NJ, USA
Dr. Sanjay Sarma, MIT, MA, USA

Submission Instructions
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Submissions through IEEE’s T-ASE Manuscript Central