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PERVASIVE AND MOBILE COMPUTING LABORATORY
Pervasive computing explores the task of integrating technology into an environment, such that a multitude of computing devices are available to proactively perform services for users, thereby lightening the users’ workload. The emergence of pervasive systems has been characterized as the third wave in computing, after the mainframe era (one computer, many users) and the personal computer era (one computer, one user). Pervasive computing is the next natural step, as a single user is in control of numerous computing elements.
The terms “ubiquitous computing” and “pervasive computing” have been used somewhat interchangeably in the literature. We draw a distinction between the two: “pervasiveness” refers to invisibility and proactiveness - where the computer dissolves into the fabric of the surroundings and requires minimal stimulus from the user, while “ubiquity” characterizes availability. In other words, ubiquitous computing facilitates pervasive computing. Advances in mobile computing have been pivotal to pervasive computing, as mobile devices provide users with continual access to computing resources. To be truly pervasive, computing should be seamlessly and invisibly integrated into the lives of its users, necessitating computational intelligence that predicts the needs and desires of the user and can independently carry out services for the user, based on surrounding context. Pervasive computing also leverages distributed computing, to delegate computational tasks to remote and/or heterogeneous computing resources.
Not withstanding the availability of the required technology, truly pervasive computing environments have not yet been realized; only prototypes and theoretical designs have been developed by the research community. A major open field is related a delicate issue faced by pervasive computing: which choices can be delegated to the system (in the form of local clients, neighboring peers, or a central server) and which must be performed by the user. It is evident that the goal of pervasive computing is to maximize the former and minimize the latter. This often demands more intelligent algorithms, architectures and technologies than are presently available. In order to create a system that proactively carries out tasks, yet remains mostly transparent to the user, the following challenges should be addressed:
The computing agents need to be able to predict the user’s intent based on history and context-awareness.
A reliable method for integrating all the computing agents into a seamless entity needs to be designed. The scope of these challenges is very broad.
In the Pervasive and Mobile Computing laboratory, our focus is on two specific areas that are crucial to the design of a proactive yet transparent system:
Techniques for resource management in a pervasive environment, considering the practical constraint that proactive issue of tasks may exhaust available resources and/or distract the user.
Security and privacy in discovery and authentication of users and services, again focusing on solutions that minimize human-machine interaction, yet provide the required level of security.
Current Lab Faculty Researchers
- Privacy-preserving location information publishing
- Constraint-based indexing and querying of moving objects
- Active sensor networks and interoperability of sensor networks
- Mobile agent security
- Data broadcasting in traffic control
- Pervasive cyberinfrastructure for personalized learning and instructional support (PERCEPOLIS)
- Model-driven Microscopy Image Analysis for Stem Cell Discovery
- Accurate Object Segmentation and Detection for Wide-Area Persistent Surveillance
- A Quadcopter with Heterogeneous Sensors for Autonomous Bridge
- Brokerage Services for the Next Generation Cloud
- An Infrastructure-free Vehicle Management Architecture to Support Secure Service Provisioning in VANETs
- Data Compression in Wireless Sensor Networks
- Secure Data Aggregation in Wireless Sensor Networks
- Information Fusion in Networked Controlled System
Current Funding Sources:
- National Science Foundation (NSF)
- Department of Education (DOE)
- Air Force Research Lab (AFRL)
- Army Research Lab (ARL)
- National Institute of Standards and Technology (NIST)
- Missouri University of Science and Technology Intelligent System Center (ISC)
- University of Missouri Research Board
- Lockheed Martin
- Mid-America Transportation Center
Brokerage Services for the Next Generation Cloud
Cloud computing is a means by which highly scalable, technology-enabled services can be easily consumed over the Internet on an as-needed basis. Commercial and individual cloud computing services are already available from Amazon, Google, Microsoft, etc. We speculate that future cloud services will increasingly involve multiple clouds, and will utilize and synthesize capabilities from multiple clouds. Applications from different organizations will establish collaborative relationships and share information dynamically in cloud computing. In this increasingly complex scenario, both consumers and service providers are facing new challenges that cannot be easily conquered by themselves. Specifically, consumers will need to be able to identify the best service providers from a potentially huge pool, which could be computationally demanding. Service providers will need to be able to ensure the security and privacy of data shared among the loosely connected collaborators and subcontractors. It is not trivial for each single service provider to manage and monitor security and privacy issues throughout the cloud service provisioning that involves multiple parties.
The objective of this project is to design a novel brokerage-based architecture to serve as a middle-man between the consumers and cloud service providers to promote the cloud provisioning by: (i) providing consumers with automated service selection mechanisms that help obtain cost-effective cloud services tailored for the specific consumers needs; and (ii) reducing the security and privacy management burden on service providers caused by the existing relationships among providers and their subcontractors.
- Smitha Sundareswaran, Anna Squicciarini, Dan Lin, "A Brokerage-Based Approach for Cloud Service Selection”, In Proceedings of IEEE International Conference on Cloud Computing, 2012.
- Smitha Sundareswaran, Anna Squicciarini, Dan Lin, Shuo Huang, “Ensuring Distributed Accountability for Data Sharing in the Cloud”, in IEEE Transactions on Dependable and Secure Computing (TDSC), 2012.
- Dan Lin, Anna Squicciarini, “Data Protection Models for Service Provisioning in the Cloud”, 15th ACM symposium on access control models and technologies (SACMAT), 2010.
- White S., Sedigh S., and Hurson A.R., “Security and Privacy in Cloud Computing”, In K. X. Yang and L. Liu, editor, Service Oriented Methodology and Technologies for Cloud Computing, IGI Global, 2012.
Personalized Education with the PERCEPOLIS Platform
Stimulating transformative changes in STEM education has been identified as the focus of numerous federal initiatives. Most specifically, President Obama’s “Educate to Innovate” campaign aims at increasing STEM literacy, improving the standing of the United States in STEM achievement and preparing the next generation of American scientists, and expanding STEM education opportunities for underrepresented groups.
Dire and unprecedented economic constraints have severely impeded the ability of both public and private institutions of higher learning to respond to this urgent need. Constraints on physical capacity, teaching resources (exacerbated by widespread hiring freezes), and laboratory facilities seriously threaten the ability of universities and community colleges to keep abreast of advances in science and technology.
Advances in databases, computational intelligence, and pervasive computing, which allow “any-time, anywhere” transparent access to information, provide fertile ground for radical changes in pedagogy. Recent studies of undergraduate education have identified “linearity” and “static trajectory” of the dominant curricular as contradictory model to the body of knowledge on how students learn. A networked model and personalization of trajectory have been proposed as potential solutions. Cyber infrastructure leveraging aforementioned technological advances can yield improvements in both instruction and learning, through personalization and support of networked curricula. This research is an attempt in that direction. It offers an innovative, practical, and comprehensive alternative to the traditional linear curriculum and lecture-based static pedagogy. The cornerstone and pillar of the proposed activities is Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support (PERCEPOLIS), which leverages a collection of enabling technologies to facilitate transformative changes to higher education, by enabling the effective use of learning technology and the personalization of courses and curricula.
Fundamental to PERCEPOLIS is the modular approach to course development. Modules in different courses can be linked to each other, facilitating implementation of a networked curricular model. Furthermore, blended instruction, where students are responsible for perusing certain learning objects outside of class, allows the focus of face-to-face meetings to shift from lecture to active learning, interactive problem-solving, and re¿ective instructional tasks, i.e. flip classroom. The novelty of PERCEPOLIS lies in its ability to consider a student’s academic pro¿le, interests, and learning style, to customize the trajectory through a course or curriculum. Finally, PERCEPOLIS facilitates the collection of data on student performance and learning at a resolution that far exceeds what is currently available. Knowledge discovery from this rich data set can yield invaluable insights, such as the efficacy of particular instructional techniques.
- Bahmani, A., Sedigh S., and Hurson A.R., “Ontology-based recommendation algorithms for personalized education”. International Conference on Database and Expert Systems Applications (DEXA ‘12), pages 111-120, Vienna, Austria, 2012.
- Hurson A.R., Sedigh S., Shirazi, B., and Miller, L., “Enriching STEM Education through Personalization and Teaching Collaboration”, Invited Paper, PerEL, March 2011, Seattle, WA.
- Bahmani, A., Sedigh S., and Hurson A.R., “Context-Aware Recommendation Algorithms for the PERCEPOLIS Personalized Education Platform”, Frontiers in Education Conference, October 2011.
- Hurson A.R. and Sedigh S., “PERCEPOLIS: Pervasive cyber-infrastructure for personalized learning and instructional support”. Intelligent Information Management, Vol. 2, No. 10, pp. 583-593, 2010.
- Hurson A.R. and Sedigh S., “Transforming the Instruction of Introductory Computing to Engineering Students”. Transforming Engineering Education conference, Dublin, Ireland, 2010.