BigDataCPS 2019 : International Workshop on Big Data Analytics of Cyber-Physical Systems
Call For Papers
IEEE International Workshop on Big Data Analytics of Cyber-Physical Systems
Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) are an increasingly important class of systems that integrate computation and communication with physical processes. CPSs usually monitor and control physical processes in a feedback loop where physical elements affect computation and vice versa. Modern society now relies on CPSs to perform critical operations in sectors ranging from assisted living, power generation and distribution, building design and automation, transportation, manufacturing, health monitoring, and emergency management, all of which serve critical functions in our daily lives. Advances in CPSs promise to enable adaptability and scalability that will far exceed the current embedded engineering systems.
The tight coupling between the cyber and physical worlds in CPS is enabling the accumulation of large amounts of data, which can be analyzed, interpreted, and appropriately leveraged for optimizations. Especially, when multiple CPSs are interacting and cooperating with each other, this big data analysis becomes a critical requirement to improve CPSs’ performance. Due to the widespread deployment of CPSs, our physical environment is constantly monitored in detail by millions of Internet-connected sensors, including smart meters, satellites, and radars, with much of the data made publicly available. Thus, sensor data is growing at a faster rate than ever before.
As one example, utilities have deployed ∼70 millions “smart” meters, which record building energy usage at fine-grained intervals in the US. The state-of-the-art meters are able to read energy data every second, and thus every million meters generate 86.4 billion readings per day. Recent studies estimate that data processed annually by CPSs will reach 44 zettabytes, or 44 trillion gigabytes by 2020. This increase will become overwhelming if the big data is not properly managed.
The workshop will consist of invited and peer-reviewed research papers about Cyber-Physical (Energy) Systems. We invite original contributions including, but not limited to the following:
Big Data Management:
Sensor systems and applications that enhance energy efficiency, energy reliability, durability, and comfort;
Distributed generation, alternative energy, renewable sources, and energy storage in buildings;
Emerging standards for data collection, energy control, or interoperability of disparate devices or systems;
Sensing, modeling, and predicting the urban heartbeat including sounds, movements, and radio spectrum;
Human in the loop sensing and control for efficient usage of electricity, gas, heating, water;
Sensor systems for reliable occupancy counting;
Long-lived and energy harvesting sensor systems;
Scalable indoor localization and contextual computing;
Security, privacy, safety, and reliability in built systems;
Empirical studies of city-scale wireless communications;
Vehicle technologies and Traffic.
Big Data Security, Privacy and Trust:
Intrusion Detection for Smart Grid and Smart Buildings;
Anomaly and APT Detection in Very Large Scale Systems;
Visualizing Large Scale Security Data;
Threat Detection using Big Data Analytics;
Privacy Threats of Big Data in Energy Systems;
Privacy-Preserving Big Data Collection/Analytics;
HCI Challenges for Big Data Security & Privacy;
Sociological Aspects of Big Data Privacy;
Trust management in IoT and other Big Data Systems.
All submissions will be evaluated on their originality, technical soundness, significance, presentation, and interest to the conference attendees. Please refer to the website (https://cps.cis.fiu.edu) for specific instructions related to the paper submission.
All accepted papers will be published in IEEE Xplore as part of the IGSC proceedings.
Paper submission: August 23, 2019
Notifications to authors: September 9, 2019
Camera-ready papers due: September 23, 2019
Dong Chen, Florida International University (email@example.com)
Dingwen Tao, The University of Alabama (firstname.lastname@example.org)
Shiqiang Wang, IBM Thomas J. Watson Research (email@example.com)