Ubiquitous sensing and computing, leading to rapid growth of big data analysis, will potentially transform the world. That vision creates new challenges for pervasive sensory interfaces to enable the always-on feature, rapid analysis of information, and design for security to prevent cyberattacks. In the meantime, however, significant power will be consumed to run machine learning and complex cryptography algorithms. Critical challenges also exist in integrating classifiers and security measures into sensors to enable continuous monitoring. This project proposes an integrated program of research, education, and outreach to develop low-power sensory systems with theory, algorithms and architectures to enable in-sensor intelligence and security. The transformative aspects of this research project include fundamental understanding of bio-inspired computing, discovering useful intrinsic device characteristics, analysis of real-time data with adaptive machine learning, and exploring chaotic behaviors for efficient encryption. This research will have a significant impact on the needs of society for secure and continuous real-time monitoring to improve health, transportation, and environment through the developments of ubiquitous sensing and computing. This project also incorporates an integrated education plan to inspire and motivate younger generations with diverse backgrounds, in particular women and underrepresented minorities, to pursue education in Science, Technology, Engineering and Mathematics (STEM) fields. The plan will introduce the concepts of secure ubiquitous sensing and computing to undergraduate and graduate students, and create strong outreach activities to local K-12 students by illustrating easily-understood concepts of fundamental electronics and mathematics with compelling examples.
The goal of this project is to develop ultra-low-power sensory interfaces that integrate autonomous sensing, classification, and secure measures into a single hardware platform. The bio-inspired classifiers incorporating combinatorial intrinsic characteristics emulate sophisticated biological systems where sensing, learning, and decision making are carried out through nonlinear and adaptive analog computing. The proposed architecture is driven by fast regeneration to extract relative timing information for hierarchical classification. Instead of using linear amplification and fine integration, inherent device mismatch and nonlinearity are exploited in time domain to achieve energy-efficient computation under low supply voltages. To process real-time data in sensors, Bernoulli variational distributions are employed for approximating the posterior to develop a computationally-efficient multi-layer neural network with Bayesian methods. The algorithm integrates medical knowledge and statistical analysis into the training process for adaptation to incoming signals. The proposed algorithm explores maximum sparsity in both sample and feature spaces, where regularizations of hardware constraints are included in the model to ensure robustness. Moreover, to perform encryption in sensors, the information will be randomized into deterministic noise for transmission. The pipeline chaotic system can be trained with time-varying maps to enhance the strength of the security without creating observable patterns to counter side-channel attacks. The transformation function is built with combinatorial intrinsic characteristics, which are physically unclonable to ensure complete security measures. This ensures data integrity and basic authentication for multi-layer security schemes from the edge sensors to the cloud while classification algorithms are performed locally in sensors to achieve rapid analysis and data reduction for wireless communications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
