Theoretical aspects of cyber-physical systems.
Announcement
ACM Workshop: SafeThings 2017 Call for Papers (Co-located with SenSys)


Dear Colleagues,

Please see below the Call for Papers for ACM SafeThings 2017. We enthusiastically look forward to your submissions on advancements in the safety of the Internet of Things ecosystem.  

1st ACM Workshop on the Internet of Safe Things (SafeThings 2017)
https://www.safethings.info/
November 5, 2017 at TU Delft, The Netherlands
Co-located with ACM SenSys 2017

As the traditionally segregated systems are brought online for next generation connected applications, we have an opportunity to significantly improve the safety of legacy systems. For instance, insights from data across systems can be exploited to reduce accidents, improve air quality and support disaster events. Cyber-physical systems (CPS) also bring new risks that arise due to the unexpected interaction between systems. These safety risks arise because of information that distracts users while driving, software errors in medical devices, corner cases in data-driven control, compromised sensors in drones or conflicts in societal policies.

Accordingly, the Internet of Safe Things workshop (or SafeThings, for brevity) seeks to bring researchers and practitioners that are actively exploring system design, modeling, verification, authentication approaches to provide safety guarantees in the Internet of Things (IoT). The workshop welcomes contributions that integrate hardware and software systems provided by disparate vendors, particularly those that have humans in the loop. As safety is inherently linked with the security and privacy, we also seek contributions in security and privacy that address safety concerns. With the SafeThings workshop, we seek to develop a community that systematically dissects the vulnerabilities and risks exposed by these emerging CPSes, and create tools, algorithms, frameworks and systems that help in the development of safe systems.

SafeThings workshop covers safety topics as it relates to an individual’s health (physical, mental), the society (air pollution, toxicity, disaster events), or the environment (species preservation, global warming, oil spills). The workshop considers safety from a human perspective, and thus, does not include topics such as thread safety or memory safety in its scope.

Our workshop will cover, but not limit itself to, the following subject categories:
- Verification of safety in IoT platforms
- Privacy preserving data sharing and analysis
- Compliance with legal, health and environmental policies
- Integration of hardware and software systems
- Conflict resolution between IoT applications
- Safety in human-in-the-loop systems
- Support for IoT development - debugging tools, emulators, testbeds
- Usable security and privacy for IoT platforms
- Resiliency against attacks and faults
- Secure connectivity in IoT

Our workshop will cover, but not limit itself to, the following domains: autonomous vehicles and transportation infrastructure; medical CPS and public health; smart buildings, smart grid and smart cities.

Submission Types:
We solicit two types of original submissions:
  * Regular papers for oral presentation (6 pages)
  * Technical posters and demos (1 page)

Important Dates:
Abstract Submission Deadline: July 23, 2017 (11.59 PM AoE)
Paper Submission Deadline: July 30, 2017 (11.59 PM AoE)
Poster and demo submission deadline: July 30, 2017 (11.59 PM AoE)
Acceptance Notification: August 21, 2017
Camera-ready versions: September 10, 2017

Organizing Committee:

General Chairs:
Patrick Tague (Carnegie Mellon University)
Bharathan Balaji (University of California, Los Angeles)

Program Chairs:
Mani Srivastava (University of California, Los Angeles)
Yuan Tian (Carnegie Mellon University)

Poster and Demo Chair:
Houssam Abbas (University of Pennsylvania)
Publication Chair:
Rasit Eskicioglu (University of Manitoba)

SenSys Workshop Chair:
Xiaofan (Fred) Jiang (Columbia University, USA)
Technical Program Committee:
Blase Ur (University of Chicago)
Xiao Feng Wang (Indiana Bloomington)
Xinyu Xing (Penn State)
Paulo Tabuada (University of California, Los Angeles)
Supriyo Chakraborty (IBM Research)
Muhammad Naveed (University of Southern California)
Yasser Shoukry (University of California, Berkeley)
Yuvraj Agarwal (Carnegie Mellon University)
Rajesh Gupta (University of California, San Diego)
Brad Campbell (University of Virginia)
John Stankovic (University of Virginia)
Madhur Behl (University of Virginia)
Houssam Abbas (University of Pennsylvania)
Insup Lee (University of Pennsylvania)
João Vilela (University of Coimbra)
Eric Wustrow (Colorado Boulder)
Richard Han (Colorado Boulder)
Lu Feng (University of Virginia)
Earlence Fernandes (University of Michigan)
Falko Dressler (Paderborn University)
Jie Liu (Microsoft Research)
Kay Roemer (TU Graz)
Nic Lane (University College London)
Fang-Jing Wu (NEC Lab)
Jyotrimoy Deshmukh (Toyota)
Saman Zonouz (Rutgers University)
Haixin Duan (Tsinghua University)
Yutaka Arakawa (Nara Institute of Science and Technology)
Ingrid Verbauwhede (KU Leuven)
Stefano Zanero (Politecnico di Milano)
Thorsten Holz (Ruhr University Bochum)
Amir Rahmati (University of Michigan)
Cong Zheng  (Palo Alto Networks)
Chenguang Shen (Facebook)
​Shaunak Mishra (Yahoo Research)

Steering Committee:
John Stankovic (University of Virginia)
Lorrie Faith Cranor (Carnegie Mellon University)
Srdjan Capkun (ETH Zurich)
Rupak Majumdar (Max Planck Institute for Software Systems)

General Announcement
Not in Slideshow
Submitted by Bharathan Balaji on July 18th, 2017
Project
CPS: Synergy: Collaborative Research: Support for Security and Safety of Programmable IoT Systems
This work examines how to get safety and security in Internet of Things (IoT) systems where multiple devices (things), each designed in isolation from others, are brought together to form a networked system, controlled via one or more software applications ("apps"). "Things" in an IoT environment can include simple devices such as switches, lightbulbs, smart locks, thermostats, and safety alarms as well as complex systems such as appliances, smartphones, and cars. Software IoT "apps" can monitor and control multiple devices in homes, cars, cities, and businesses, providing significant benefits such as energy efficiency, security, safety, and user convenience. Unfortunately, programmable IoT systems also introduce new risks, including enabling remote control by hackers of devices in smart homes, cars, and cities, via buggy IoT apps. Testing IoT apps to remove bugs is currently challenging due to a variety of physical devices with which such apps may interact, including devices that were not even available during app development. The proposed work will help develop techniques for testing IoT apps efficiently and for enforcing safety and security constraints on their run-time behavior. More specifically, the proposed work is centered around three technical thrusts: 1) creating virtual device models to help efficiently test IoT apps systematically without knowing the precise details of physical devices that the apps will control in advance; 2) automating test development for an IoT app to check safety and security specifications against a flexible set of devices; and 3) providing support for enforcement of specifications at run-time for security and safety assertions. The work includes extensive experimentation and evaluation using diverse devices and will represent a significant advance in hardening this important spaces
1646392
Off
University of Michigan Ann Arbor
Atul Prakash
-
National Science Foundation
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646392&HistoricalAwards=false
Submitted by Atul Prakash on July 12th, 2017
Project
CPS: Breakthrough: Secure Interactions with Internet of Things
The objective of this research is to (1) gain insights into the challenges of securing interactions in Internet of Things (IoT)deployments, (2) develop a practical framework that mitigates security and privacy threats to IoT interactions, and (3) validate the proposed framework in a medium-scale IoT testbed and through user studies. The emerging IoT computing paradigm promises novel applications in almost all sectors by enabling interactions between users, sensors, and actuators. These interactions can take the form of device-to-device (e.g., Bluetooth Low Energy (BLE)) or human-to-device (e.g., voice control). By exploiting vulnerabilities in these interaction surfaces, an adversary can gain unauthorized access to the IoT, which enables tracking, profiling and posing harm to the user. With the thousands of diverse IoT manufacturers, developers, and devices, it is very challenging, if not impossible, to ensure all devices are properly secured at production and kept up-to-date after production. IoT users and administrators have to place their trust in a set of devices, with the least secure device breaking the security chain. By shifting the trust base from the various manufacturers and developers to a single framework under the user's control, deploying IoT devices will be more feasible and less vulnerable. The proposed framework will help advance the national health, prosperity and welfare, and also secure the national defense. Securing IoT interface surfaces as case studies will be integrated in graduate-level courses, and used to train (especially underrepresented and female) students with interdisciplinary topics that require a balanced mix of theory and practice, thus developing human resources in the nationally needed areas.The proposed research will also significantly advance the understanding of the challenges to secure IoT interaction surfaces in practice, thus promoting the progress of science. This project will establish a general direction to secure interactions in the current and future IoT deployments. It will offer an additional protection layer in the cases where security cannot be properly built-in and maintained.
1646130
Off
University of Michigan Ann Arbor
Kang Shin
-
National Science Foundation
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646130&HistoricalAwards=false
Kang Shin Submitted by Kang Shin on July 12th, 2017
Project
CPS: Synergy: Collaborative Research: Towards Dependable Self-Powered Things for the IoT
Scaling the Internet of Things (IoT) to billions and possibly trillions of "things" requires transformative advances in the science, technology, and engineering of cyber-physical systems (CPS), with none more pressing or challenging than the power problem. Consider that if every device in a 1 trillion IoT network had a battery that lasted for a full five years, over 500 million batteries would need to be changed every day. Clearly, a battery-powered IoT is not feasible at this scale due to both human resource logistics and environmental concerns. There is a need for a battery-less approach that dependably meets functionality requirements using energy harvested from the physical world. This project brings together experts in materials, devices, circuits, and systems to pursue a holistic approach to self-powered wireless devices deployed in real-world environments and IoT systems and applications. In addition, educational and outreach activities will help develop the workforce for this relatively new field with the holistic, materials-to-systems perspective that will be necessary to lead innovation in this space.A critical challenge that this project addresses is that both optimal device operation and energy harvester efficiency are heavily dependent on physical world dynamics, and thus, self-powered devices that are statically configured or that just respond to instantaneous conditions are unlikely to provide the dependability required for many IoT systems and applications. To address this fundamental and critically enabling challenge, data collections will be performed to study the physical world dynamics that impact device operation and harvester efficiency, such as ambient conditions, electromagnetic interference, and human behavior. This scientific study will lead to the development of dynamic models that will, in turn, be used to develop algorithms to dynamically configure devices and harvesters based not only on past and current conditions but also on predictions of future conditions. These algorithms will then be used to dynamically configure technological innovations in ultra-low power device operation and ultra-high efficiency energy harvesting to engineer and operate dependable self-powered things for the IoT.
1646399
Off
Pennsylvania State University
Susan Troiler-McKinstry
-
National Science Foundation
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646399&HistoricalAwards=false
Submitted by Susan Troiler-McKinstry on July 12th, 2017
Event
IWCR 2017
CALL FOR PARTICIPATION
Submitted by Anonymous on July 11th, 2017
Event
CyPhy'17
Seventh Workshop on Design, Modeling and Evaluation of Cyber Physical Systems (CyPhy'17) Held in conjunction with ESWEEK 2017 
Submitted by Anonymous on July 11th, 2017
Event
IUI 2018
ACM IUI 2018 is the 23rd annual meeting of the intelligent interfaces community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. ACM IUI is where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. We are also very interested in contributions from related fields, such as psychology, behavioral science, cognitive science, computer graphics, design, the arts, etc. 
Submitted by Anonymous on July 11th, 2017
Event
DSS 2017
The 3rd International Workshop on Data-driven Self-regulating Systems (DSS 2017) In conjunction with 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Proceedings appear in IEEE Digital Library
Submitted by Anonymous on July 11th, 2017
Event
SASO 2017
11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)  SASO is part of FAS*, a common umbrella for two closely related but independent conferences (SASO and ICCAC) with shared events including workshops, tutorials, doctoral symposia, etc.
Submitted by Anonymous on July 11th, 2017
Announcement
Critical Techniques, Technologies and Methodologies for Advancing Foundations & Applications of Big Data Sciences and Eng.

PROGRAM SOLICITATION: CRITICAL TECHNIQUES, TECHNOLOGIES AND METHODOLOGIES FOR ADVANCING FOUNDATIONS AND APPLICATIONS OF BIG DATA SCIENCES AND ENGINEERING (BIGDATA) - NSF 17-534

View the full solicitation at: https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504767

SYNOPSIS

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. 

The solicitation invites two categories of proposals:

Foundations (F): those developing or studying fundamental theories, techniques, methodologies, and technologies of broad applicability to big data problems, motivated by specific data challenges and requirements; and
Innovative Applications (IA): those engaged in translational activities that employ new big data techniques, methodologies, and technologies to address and solve problems in specific application domains. Projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc.

Proposals in both categories must include a clear description of the big data aspect(s) that have motivated the proposed approach(es), for example: the scalability of methods with increasing data volumes, rates, heterogeneity; or data quality and data bias; etc. Innovative Applications proposals must provide clear examples of the impacts of the big data techniques, technologies and/or methodologies on (a) specific domain application(s).

Proposals in all areas of sciences and engineering covered by participating NSF directorates and partnering agencies [the Office of Financial Research (OFR)], are welcome.

Before preparing a proposal in response to this BIGDATA solicitation, applicants are strongly urged to review other related programs and solicitations and contact the respective NSF program officers listed in them should those solicitations be more appropriate. In particular:

For the development of robust and shared data-centric cyberinfrastructure capabilities, applicants should consider the Data Infrastructure Building Blocks (DIBBs) program, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504776;
For computational and data science research not specifically addressing big data issues, applicants should consider the Computational and Data Enabled Science and Engineering (CDS&E) program, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813;
For work that is focused more on scaling of software, rather than data-related issues, applicants should consider the Scalable Parallelism in the Extreme (SPX) program, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505348;
Proposals that are specific to the geosciences, and respond to the community needs and requirements expressed by the geosciences community, should consider the NSF EarthCube program for Developing a Community-Driven Data and Knowledge Environment for the Geosciences, https://www.nsf.gov/geo/earthcube/;
Proposals that focus on research in mathematics or statistics that is not tied to a specific big data problem should be submitted to the appropriate program within the MPS Division of Mathematical Sciences (DMS); see a list of DMS programs at https://www.nsf.gov/funding/programs.jsp?org=DMS; and
Proposals that focus on research in the computer and information sciences not tied to a specific big data problem should be submitted to the appropriate CISE core program:

Computer and Network Systems (CNS) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=12765&ods_key=nsf16579;

Computing and Communication Foundations (CCF) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=503220&ods_key=nsf16578; and
Information and Intelligent Systems (IIS) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=13707&ods_key=nsf16581.

IMPORTANT INFORMATION FOR PROPOSERS

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 17-1), is effective for proposals submitted, or due, on or after January 30, 2017. Please be advised that, depending on the specified due date, the guidelines contained in NSF 17-1 may apply to proposals submitted in response to this funding opportunity.

Amazon Web Services (AWS), Google, and Microsoft are now participating in the solicitation by providing cloud credits/resources to qualifying projects. The solicitation provides details regarding the participation of these companies, and the use of their cloud resources.

Additionally, slight revisions of the Program Description have been introduced.

Any proposal submitted in response to this solicitation should be submitted in accordance with the revised NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 17-1), which is effective for proposals submitted, or due, on or after January 30, 2017.

AWARD INFORMATION

Anticipated Type of Award: Standard Grant or Continuing Grant or Cooperative Agreement

Estimated Number of Awards: 27 to 35

About 27-35 projects will be funded, subject to availability of funds.

Anticipated Funding Amount: $26,500,000

Up to $26,500,000 will be invested by NSF and the Office of Financial Research (OFR), in proposals submitted to this solicitation, subject to the availability of funds. Up to $9,000,000 will be invested by Amazon Web Services (AWS), Google, and Microsoft (up to $3,000,000 each) in the form of cloud credits/resources.

Projects will typically receive NSF funding in the range of $200,000 to a maximum of $500,000 per year, for 3 to 4 years of support. The minimum award size will be $600,000 of total NSF/OFR funding, reflecting the minimum expected level of effort for BIGDATA projects, which are expected to be multidisciplinary in nature and include significant student involvement. Any allocation of cloud credits/resources from AWS, Google or Microsoft will be in addition to the NSF/OFR funding.

Submission Window Date(s): March 15, 2017 - March 22, 2017

General Announcement
Not in Slideshow
Katie Dey Submitted by Katie Dey on June 30th, 2017
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