Robert Martin Computer Science & Engineering Department The University of Connecticut

1. CSE 5810 Individual Research Project: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

2. Motivation • 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.

3. Large Scope • Keep up with change • Larger data files • Mobile devices • Real-time data availability • Conform to busy hospital environment • Revamp current network • Transparent infrastructure

4. Overall Goal • 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

5. Named Data Networking (NDN) Image adapted from: Tsudik, Gene. NSF FIA PI meeting: “NDN team presentation.” Berkeley, CA, May 25, 2011.

6. Hospital Setting NDN

7. NDN vs. IP Networking • Named Data Networking • Data centric approach • IP Networking • Looks at where data is located Image adapted from: Jacobson et al. (full reference in notes)

8. Interest and Data Packets • 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 • Data Packet • Content is of arbitrary data size • Signature is used to verify the packet’s producer and its integrity throughout transmission

9. Pending Interest Table (PIT) • 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)

10. Forwarding Information Base (FIB) • 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)

11. Content Storage • 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.

12. Data Naming • Application specific • Flexible standards • Classifications and standards can be adjusted

13. Security • Nurse fetching data which is unrelated to her role in the hospital (e.g. Patent’s social security number) • Security integrated into data packet • Authentication process

14. Fetching Data • 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” • 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

15. Discovery Service • Maps out data on network (similar to DNS)

16. Mobility with IP Networking • Illustration through example: • Pre-loading patient’s data • Large data files • Based on IP network

17. Mobility with IP Networking • Provider must request file again • Additional stress to hospital network

18. Mobility with Named Data Networking • Illustration through example: • NDN based network

19. Mobility with Named Data Networking • Data content cached

20. Mobility with Named Data Networking • Data requested again • Reduced redundant data packets

21. Simulation Settings • Ns3 and ndnSIM extension used • Regular IP based network vs. NDN integrated network • Focus: • Transmission times • Network stress

22. Preliminary Simulation Data Network Stress Overall Transmission Time

23. Conclusion • 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

24. Thank You