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Research at the Pervasive and Invisible Computing (PICo) lab started with a funding from the NSF (NSF-ANI- 0129682) for the development of middleware services in pervasive environments through the creation of Pervasive Information Community Organization (PICO). PICO is a framework to create mission-oriented dynamic communities of software agents that perform tasks on behalf of users/devices. Project contributions include modeling, analysis, simulation, creation of static and dynamic services and prototyping. The novel features of PICO include: i) creation of mission oriented dynamic communities of software agents, ii) just-in-time communication and proactive collaboration among communities, and iii) adaptability to hardware and software changes and application requirements. PICO has applications in many domains such as telemedicine, military, crisis management, manufacturing and many day-to-day activities.
The activities in the lab are funded by the ongoing NSF funded project, Pervasively Secure Infrastructures (PSI): Integrating Smart Sensing, Data Mining, Pervasive Networking, and Community Computing, (NSF- IIS-0326505). The goal of PSI is to develop a robust security framework that makes 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. Specific goals include - efficient data collection and aggregation from heterogeneous sensors and monitors, novel techniques for real-time, secured, authenticated information transmission and sharing, and situation awareness through new learning, data mining, and knowledge discovery techniques.
The research work has led to 2 PhDs, 10 MS Theses, 4 ongoing PhDs, several refereed publications.
Current Research activities at the PICo lab include:
- Development of models for provisioning of services in pervasive, mobile, sensor and P2P systems
- Service creation, composition, and maintenance in pervasive computing environments
- Secure and efficient information flow in dynamic, heterogeneous systems
- Parallel and distributed algorithms for efficient utilization of resources in heterogeneous environments
- Temporal and spatial Information fusion in sensor systems
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