CSE 5810 Individual Research Project:
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Integration of Named Data Networking for
Improved Healthcare Data Handling
Robert Martin
Computer Science & Engineering Department
The University of Connecticut
371 Fairfield Road, Box U-255
Storrs, CT 06269-2155
Robert.martin@engr.uconn.edu
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Motivation
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Technology limitations
 Larger data files
 Multiple databases
 Ever expanding healthcare network
Fast pace hospital environment
 Providers constantly moving
 Intolerant to delayed data access
Lack of communication between departments
 Clinical, technical, business management,
financial, etc.
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Large Scope
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Keep up with change
 Larger data files
 Mobile devices
 Real-time data availability
Conform to busy hospital environment
 Revamp current network
 Transparent infrastructure
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Overall Goal
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Apply Named Data Networking within a hospital
environment
 Data connectivity
 Improved transmission speeds (compared with
regular IP networking)
 Improved mobile device handling
 Interoperability between diverse departments
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Named Data Networking (NDN)
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Image adapted from:
Tsudik, Gene. NSF FIA PI meeting: “NDN team presentation.” Berkeley, CA, May 25, 2011.
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Hospital Setting NDN
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NDN vs. IP Networking
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Named Data Networking
 Data centric approach
IP Networking
 Looks at where data is located
Image adapted from:
Jacobson et al. (full reference in notes)
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Interest and Data Packets
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Interest Packet
 Data name in query
 Nonce is unique identifier
 Selectors help better match interest to data
 Scope and interest lifetime help guide
packet to intended data
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Data Packet
 Content is of arbitrary data size
 Signature is used to verify the
packet’s producer and its integrity
throughout transmission
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Pending Interest Table (PIT)
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Monitors all unsatisfied interest packets
Entry classified as unsatisfied until either a data packet
is received (to match its interest) or the interest
lifetime value is reached
“A Case for Stateful Forwarding Plane” by C. Yi et al. depicts a
great image for how node’s use PITs(see full reference in notes)
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Forwarding Information Base (FIB)
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Monitors downstream data location through next hop
neighbor
“A Case for Stateful Forwarding Plane” by C. Yi et al. depicts a
great image for how node’s use FIBs(see full reference in notes)
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Content Storage
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Cache data locally
Pushes data closer to consumer(s)
Allows network to become “data focused”
 Quicker fetching of data for consumer
Data architecture can vary
 FIFO, LRU, etc.
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Data Naming
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Application specific
Flexible standards
 Classifications and standards can be adjusted
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Security
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Nurse fetching data which is unrelated to her role in
the hospital (e.g. Patent’s social security number)
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Security integrated into data packet
 Authentication process
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Fetching Data
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Filtering naming system
 Adjust documentation standards for each
department
 E.g. Financial employee and patient see “heart attack”
vs. global view classification as “Myocardial
Infarction”
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Paths are dynamic while being transparent to end user
 Nodes can be added or removed without having an
effect on the user
 Robust among dense networks
We must make network aware of newly added data in an
efficient manner
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Discovery Service
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Maps out data on network (similar to DNS)
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Mobility with IP Networking
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Illustration through
example:
 Pre-loading
patient’s data
 Large data files
 Based on IP
network
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Mobility with IP Networking
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Provider must request
file again
Additional stress to
hospital network
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Mobility with Named Data Networking
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Illustration through
example:
 NDN based network
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Mobility with Named Data Networking
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Data content cached
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Mobility with Named Data Networking
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Data requested again
Reduced redundant data
packets
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Simulation Settings
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Ns3 and ndnSIM extension used
Regular IP based network vs. NDN integrated network
Focus:
 Transmission times
 Network stress
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Preliminary Simulation Data
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Network Stress
Overall Transmission Time
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Conclusion
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Apply NDN concepts in hospital infrastructure
 “What” data instead of “Where”
 Reduce stress on keynote features
 Less bandwidth usage
 Friendlier to mobile devices
Additional features
 Adaptability with discovery service
 Integrated security through data
Challenges
 Acceptability by healthcare
 Ensuring security of data
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THANK YOU
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