This project addresses homeland security, an issue of the highest national priority, with a goal of monitoring, preventing, and recovering from natural and inflicted disasters. In particular, in collaboration with team from UT Arlington, Penn State University and the University of Kentucky, the project will create a novel technology-enabled security framework, called Pervasively Secure Infrastructures (PSI), that will make use of such advanced technologies as smart sensors, wireless networks, pervasive computing, mobile agents, data mining, and profile-based learning in an integrated, collaborative and distributed manner. The uniqueness of this multi-disciplinary, multi-university proposal lies in the synergistic combination of the proposed research in (i) efficient data collection and aggregation from heterogeneous sensors and monitors; (ii) novel techniques for real-time, secured, authenticated information transmission and sharing, and (ii) intelligent situation awareness (e.g., threat detection and security services) through new learning, data mining, and knowledge discovery techniques. The project will mainly focus on authentication and secure data transmission in wireless networks. The project will integrate these research efforts in the novel paradigm of pervasive community computing that can efficiently handle dynamically changing information, adapt to changing situations, and provide scalability in terms of the number of users, devices, and data sizes.
This NSF project addressed several important challenges related to safety and security of people, assets and buildings, which are of of high national importance. The broader goals of the project included proactive monitoring, early detection, prevention, and recovery from natural and inflicted disasters. Significant results were accomplished in terms of the design and development of novel technology-enabled security framework, called Pervasively Secure Infrastructures. The underlying technologies were the use of smart sensing, wireless networking, pervasive computing, mobile agents, data mining and learning in an integrated, collaborative and distributed manner. The developed solutions were based on fundamental mathetical frameworks such as graph theory, information theory, game theory, online learning, advanced algorithms and optimization technuques. Intellectual Merit: The novel contributionsof this project were to design and anslysis of energy-efficient architectures, algorithms and protocols for data collection, fusion and dissemination from heterogeneous sensors; novel techniques for secure information transmission and sharing; and intelligent situation awareness (e.g., threat detection and security services) through new learning, data mining, and knowledge discovery techniques. The obtained results were integrated through pervasive community and middleware platforms that can efficiently handle dynamic environments, adapt to changing situations, and provide scalability in terms of the number of users, devices, and data sizes. The underlying methodologies esploited solid mathematical understanding, modeling, tools and frameworks. Extensive experiments were also conducted on real sensor network test bed or through simulation study to validate the propsoed architectures, algorithms and protocols. Broader Impacts: This project had tremendous impacts in terms of high quality training of more than two dozen graduate students and more than one dozen REU students. The project led to 18 PhD grauates (7 female) and 15 MS thesis (6 female) students. The project findings were incorporated in more than 5 course teachings and also led to numerous high quality confirence and journal papers including two books and two US patents. The reseacrh results also have broader impacts in science and engineering - the developed solutions have potential for deployment in a variety of safety, security, and surveillance of national and international borders, transportation and utility networks, public and private buildings.
