Graduate Information Science and Technology Program

    School of Information Sciences

    University of Pittsburgh

Network-Aware Data Management Group



Co-Adapt: Complex Adaptive Information Systems






Complex Adaptive System (CAS) is a large-scale and highly distributed environment, which can tune itself via simple rule-based interactions between its components. One of the most intriguing features of CAS is the ability of such simple interactions to form complex and “rational” system behavior. CASs manifested themselves in various disciplines ranging from life sciences  (e.g., organizational behavior of ants and patterns of neuronal activation) to social sciences and economics (e.g., formation of social networks and market regulations).  In this project we explore application of the CAS concepts in the context of information science and technology. Rapid evolution of Web and networked information systems strongly stimulates this research. Meanwhile, building and deploying industrial-strength Complex Adaptive Information Systems (CAIS) require more interdisciplinary research efforts. This project includes the following tasks:

Task 1:  Adaptive wireless sensor networks.

Task 2:  Adaptive online social networks.

Task 1:  Adaptive wireless sensor networks

In this task we explore the emergent complexity of Data Intensive Sensor Networks (DISNs) to adapt to application requirements via light-weight localized adjustments of the interaction between sensors. The successful delivery of information in DISNs is impaired by various problems such as congestion, collisions and no route available for data in the network. The combined effect of those factors is hard to estimate and this is one of the major reasons why existing optimization solutions have very limited applicability in DISN.  Our approach is based on considering the WSN as a complex adaptive system,   where decisions made locally by individual sensors can efficiently converge into desirable information processing patterns.  We investigate how such adaptation helps to meet high performance requirements of mission-critical applications. As an example of dealing with such application we introduced a novel strategy for sensor data processing that supports fire evacuation with stringent delay constraints. In this case sensornet performs distributed emergency assessment, continuous emergency monitoring, and dynamic selection of optimal evacuation strategies. A notable feature of our method is its scalability, which allows the sensornet to operate with sufficient quality of service under heavy information loads.

Task 2:  Adaptive online social networks

The evolution of information and communication technologies introduces new ways to utilize informationas it is getting more accessible. Wide proliferation of Internet, Web technologies and mobile devices facilitates further information consolidation in networked human-centered environments. Such environments provide us with a unique opportunity to observe, collect, and analyze communication and information processing patterns in large-scale collaborative communities. The emerging concept of soft sensing and collective intelligence contributes to efficient information fusion and sensemaking strategies, which are in the focus of  this task.  In particular, we explore adaptive techniques for automatic information reliability assessment in social networks. Our approach utilizes subjective logic and cognitive human traits to assess information reliability, as well as expertise of the information provider.  As a sub-project within this task we develop a scalable Self-Adaptive Learning through Teaching (SALT) technology that implements efficient social adaptive learning methodologies.  This is achieved by allowing students to be actively involved in the process of learning-through-teaching via lightweight social interactions. To sum up, SALT technology extends the concept of OSN utilizing collective intelligence to gain educational knowledge.

PhD Students:

Andrii Cherniak

Evgeny Karataev


Chih-Kang Lin (Bell Labs, Ireland)

Divyasheel Sharma (ABB, India)

Selected References:

  1. Ren, Y., Zadorozhny, V., Oleshchuk, V., Li, F. A Novel Approach to Trust Management in Unattended Wireless Sensor Networks. To appear in IEEE Transactions on Mobile Computing, 2013.

  2. Zadorozhny, V., Lewis, M. Information Fusion based on Collective Intelligence for Multi-Robot Search and Rescue Missions. To appear in Proceedings of the 14th  International Conference on Mobile Data Management (MDM’13), 2013.

  3. Ren, Y., Zadorozhny, V., Oleshchuk, V., Li, F. An Efficient, Robust, and Scalable Trust Management Scheme for Unattended Wireless Sensor Networks. Proceedings of the 13th  International Conference on Mobile Data Management (MDM’12), 2012. (Best Paper Award)

  4. Lin, C-K.,  Zadorozhny, V., Krishnamurthy, P.,  Park, H., Lee, C. A Distributed and Scalable Time Slot Allocation Protocol for Wireless Sensor Networks. IEEE Transactions on Mobile Computing, v. 10, N. 4, 2011.

  5. Pelechrinis, K., Zadorozhny, V.,Oleshchuk,V. A Cognitive-based Scheme for User Reliability and Expertise Assessment in Q&A Social Networks. Proc. of the International Workshop on Issues and Challenges in Social Computing (WICSOC'11).  In conjunction with the 12th IEEE International Conference on Information Reuse and Integration, 2011.

  6. Sharma, D. Zadorozhny, V. Adaptive Information Delivery in Data-Intensive Sensor Networks. Proc. of  the 12th International Conference on Mobile Data Management (MDM'11), 2011 .

List of publications