Designing Smart and Secure Communities: from Sensors to Risk-based Reasoning

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This five-year Program focuses on the concepts and technologies that make a community secure, including sensors of human biometrics and artificial intelligence. The core of this emerging research is a risk-based probabilistic fusion of data such human biometrics (for example, face or gait), attributes (such as gait type), and contextual data (location, environment). This program is motivated by the need to achieve security and intelligence, and to balance them to address privacy aspects. This Program has four main goals: 1. To develop a framework that is based on machine reasoning, also known as inference, approach to system-level decision-making. The risks of decision-making in our approach are assessed using probabilistic inference. Then framework will also address the privacy aspects, which can actually be greatly enhanced through usage of contextual data and de-personalization by emphasizing on behavioral and attributed biometrics. 2. To further extend the theory of probabilistic inference by developing a Multi-metric Inference Engine that deals with uncertainty of biometric sensory and contextual data. The Multi-metric Inference Engine is based on probabilistic causal models with novel integrated mechanism for multiple metric including point, interval, and fuzzy probabilities, as well as conflict resolution based on Dempster-Shafer approach. This will become the core of the risk-based framework for decision-making. 3. To propose the proof-of-concept solutions and prototypes for the practical applications such as: a) Safe and secure cities, mass-transit hubs, public events, as well as public buildings. An example includes the current pilot project conducted in the group, on developing smart shelter access kiosks with integrated risk-based assessment of medical emergency and security threats. b) Risk assessment in automated border crossing infrastructure including concealed weapon detection. An example is the project started with an industrial partner, on risk-based analysis of various scenarios that use body metrics with and without concealed objects. c) Risk-based monitoring of biometrics in healthcare, rehabilitation, and assisted living. In particular, the proposed Multi-metric Inference Engine will be applied to monitoring and risk inference for post-surgery patients, and prediction of risk of falls in the elderly. 4. To implement an HQP training and educational plan which incorporates industry internships for graduate students as well as practical research and design component in senior undergraduate curriculum, involves undergraduates in research, and promotes student international collaborative research. From a strategic perspective, this Program will contribute to (1) advancing in computer-assisted systems for both civil and military security applications, (2) developing of future generation risk assessment tools. (3) integrating the Canadian R&D workforce in collaboration within the academic and industrial sectors.