HICSS-54 Minitrack Program
Smart (City) Application Development: Challenges and Experiences
Charlie Catlett, Senior Research Scientist at the Discovery Partners Institute of (DPI) of the University of Illinois, Senior Computer Scientist at the U.S. Department of Energy’s Argonne National Laboratory, visiting Senior Fellow at the University of Chicago’s Mansueto Institute for Urban Innovation, will present the keynote “Edge Computing for Artificially Intelligent Cities”.
Furthermore will have a panel discussion and structured discussion with the audience about current and future trends in smart city applications, especially from a software engineering point of view. We will include topics such as the current gaps and the influence of COVID-19 on the research area.
Edge Computing for Artificially Intelligent Cities
Abstract: For the past decade, ‘smart city’ projects have emphasized measurement, data analytics, and modeling—all of which are critical to new ways to optimize cities and more importantly, new applications to make cities “better” for their inhabitants. In Chicago, what began as an ambitious urban measurement initiative (The Array of Things, or “AoT”) almost immediately evolved to emphasize new types of measurements that typically require human observers. For instance, going beyond counting vehicles to understanding their flows and the mix of vehicle types. Borrowing from the concept of software-defined networks, our work emphasized programmable devices, or “software-defined sensors.” By supporting such platforms, we can begin to explore how intelligent sensors (and other devices) might improve our understanding of cities across a range of dimensions, from social sciences to transportation to environmental sciences. Catlett will discuss “life beyond AoT” including lessons learned and the expanded vision of the team’s current initiative, SAGE, funded through the National Science Foundation’s Mid-Scale Research Infrastructure program.
A Time-Sensitive IoT Data Analysis Framework
Prem Prakash Jayaraman, Ali Yavari, Dimitrios Georgakopoulos, Harindu Korala
Abstract This paper proposes a Time-Sensitive IoT Data Analysis (TIDA) framework that meets the time-bound requirements of time-sensitive IoT applications. The proposed framework includes a novel task sizing and dynamic distribution technique that performs the following: 1) measures the computing and network resources required by the data analysis tasks of a time-sensitive IoT application when executed on available IoT devices, edge computers and cloud, and 2) distributes the data analysis tasks in a way that it meets the time-bound requirement of the IoT application. The TIDA framework includes a TIDA platform that implements the above techniques using Microsoft’s Orleans framework. The paper also presents an experimental evaluation that validates the TIDA framework’s ability to meet the time-bound requirements of IoT applications in the smart cities domain. Evaluation results show that TIDA outperforms traditional cloud-based IoT data processing approaches in meeting IoT application time-bounds and reduces the total IoT data analysis execution time by 46.96%.