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Collaborative Distributed Systems & Networks Lab

Faculty Name
Dongman Lee
Research Area
Computing Theory, Social Computing, Interactive Computing
Website
http://cds.kaist.ac.kr/cdsn/
E-mail
Phone
042-350-3559
Office
#803, N1

Introduction to the Collaborative Distributed Systems & Networks Lab
As ubiquitous computing paradigm is realized and the Internet of Things (IoT) environments are deployed, the importance of providing composed services by collaborating multiple smart objects is increasing. The Collaborative Distributed Systems & Networks (CDS&N) lab focuses on solving problems using context-awareness when various services and smart objects collaborate to run a service. CDS&N lab develops a spontaneous service provision framework in order to provide an appropriate service to users and tests the platform in IoT testbeds in N1.
In order to provide appropriate services for users in IoT environments, We research how to recognize users' activities with smart objects based machine learning methods (presented at COMPSAC 2016) and how to provide customized services through collaboration among smart objects (presented at SCC 2017).
Smart Interaction with Things that Think Smart appliances are equipped with cutting-edge technologies but not well utilized due to the difficulty of adopting new functionalities. We study on interactive smart appliances in order to provide personalized interactions as a service and improve user experience. We also study on the semantic communication which finds the way to communicate between unknown entities and run new services. (presented at HCII 2013 and ISUVR 2012) Spontaneous Service Provision Framework The challenging issue on integrating cyber-physical services is to solve an adaption loss problem when multiple services are connected. We study on the adapter chaining framework to minimize the adaptation loss between smart objects. (presented at IEEE PerCom 2008 and SCC 2011)
Semantic meanings of urban places varies on users and time. In order to provide proper urban place and its environment, we analyze social big data from SNS and develop machine learning techniques to extract different meanings of urban places and formulate them.
Service-aware Networking Architecture Various services are deployed on mobile devices, and the demand of each service on the network is diversified and differentiated. By exploiting context-awareness, we develop a service-aware networking architecture in order to convert service requirements into network parameters and apply them to the current network in a cross-layer manner (routing, MAC, and PHY layers). (presented at IEEE AINA 2012, ICC 2014, and LCN 2014, ICC 2016)IoT device are equipped with certain capability different from traditional computing devices. These resources cannot be managed as existing computing resources. Therefore, we develop an edge IoT cloud which can express resources accordingly to the service characteristics and allocate accordingly (IEEE CAN 17)

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