[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] PhD Candidate [an error occurred while processing this directive] University of North Carolina at Chapel Hill [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]

Control, Intelligent Systems, and Robotics

Cyber-Physical Systems and Design Automation

Energy

[an error occurred while processing this directive] Scheduling Tasks on Intermittently-Powered Time-Aware Intelligent Systems [an error occurred while processing this directive] Advancement in batteryless or energy-constrained systems is necessary to realize continuous and pervasive sensing as replacing batteries is not only cumbersome but also unscalable. To illustrate, even with a battery life of 10 years, 274 million batteries would need to be replaced daily when the number of IoT (internet of thing) devices reaches one trillion by 2035. While existing works on intermittent computing systems concentrate preliminary on the lower-level goals, e.g., execution progress and memory consistency, the potential of such systems under timing constraints is yet to be explored. This limits the opportunities to use batteryless systems in a wide range of necessary application domains, from infrastructure monitoring to wildlife tracking and long-term health monitoring. The focus of my research is to understand and enhance the usability and processing capabilities of batteryless sensing and computing devices by introducing timeliness and learning capabilities.

I leverage the data processing and control layer of batteryless systems to ensure timely response by developing novel frameworks that (1) integrates energy harvesting and real-time systems and (2) rethinks machine learning and computer vision algorithms to enable imprecise computing. To develop these novel frameworks, it is necessary to understand and redesign the sophisticated machine learning and signal processing algorithms and to utilize the multimodal sensing opportunities of embedded systems. I also employ similar strategies to explore the potential of sensing using resource-constrained systems in diverse application domains. The interdisciplinary nature of my research blends diverse domains, including embedded systems, mobile computing, machine learning, mobile health, signal processing, and ubiquitous computing. [an error occurred while processing this directive] Bashima Islam is currently pursuing a Ph.D. degree in the Department of Computer Science at University of North Carolina at Chapel Hill, working with prof. Shahriar Nirjon. Her research interests include mobile and pervasive computing, machine learning for cyber- physical systems and mobile health. She is especially interested in intermittent computing. The goal of her research is to understand and enhance the usability and processing capabilities of tiny energy-constrainted computing devices to realize their full potential in our daily lives. She became a finalist of the Gaetano Borriello Outstanding Student Award, UbiComp 2020, for her extensive contributions to ubiquitous computing. She is also a recipient of the N2Women Young Researcher Fellowship Award in 2017. [an error occurred while processing this directive] Personal home page [an error occurred while processing this directive] [an error occurred while processing this directive]