Our modern society is increasingly dependent on embedded computing devices that process a vast amount of data. Common examples include visual computing on mobile phones, automotive embedded system, wireless sensor networks, implantable medical devices, and mobile virtual reality games. The content, communication, and processing algorithms can be overwhelming on small platforms. What exacerbates the problem are the real-time constraints set by certain applications, which prohibits outsourcing to the cloud due to the incurred delay and uncertainty.

To be viable, there are at least two major sets of technical challenges that need to be addressed for small form-factor platforms that enable present and pending Internet-of-Everything (IoE) systems. One set of hurdles has to do with resource and/or application constraints such as real-time, available energy, or memory. Another set of barriers arises due to security, reliability or safety requirements. Attacks on these systems go far beyond destruction of data, as they have the potential to impact physical assets and people’s lives. In this context, classic solutions for resource-efficiency and/or security are of limited effectiveness. The research in ACES lab is centered on two interrelated thrusts that address the respective efficiency and security challenges:

Massive data analytics in constrained settings Security and privacy for data-intensive computing



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