Delay Tolerant Networks Arezu Moghadam Ph D Candidacy

Delay Tolerant Networks Arezu Moghadam Ph. D Candidacy Talk 12/18/2007 1

Networking expansion CDN Pub/sub P 2 P Pervasive Sensor overlays wireless computing nets Internet DTN 2000 Applications rule! 1990 ATM 1980 B-ISDN Internet OSI 1970 2

Interplanetary communication Ref: [1] Picture: http: //www. intel-research. net/berkeley 3

Zebra. Net (a real life application) Data Store-and-forward communications Data Tracking node with CPU, FLASH, radio and GPS Data First deployment in 2004 in Kenya Sensor Network Attributes Zebra. Net Other Sensor Networks Node mobility Highly mobile Static or moderate mobile Communication range Miles Meters Sensing frequency Constant sensing Sporadic sensing Sensing device power Hundreds of m. W Tens of m. W http: //www. princeton. edu/~mrm/zebranet. html 4

DTN characteristics o Internet environment n n End-to-end RTT is not large. Some path exists between endpoints. E 2 E reliability using ARQ works well. Packet-switching is the right abstraction. Ref: [2], [3] o DTN characteristics n n n Very large delays. Intermittent and scheduled links. Different network architectures. Conversational protocols fail. No ARQ. 5

Agenda o o o Architecture Routing Multicast Implementation Conclusion 6

Architectural requirements o o Asynchronous message delivery. Naming n o o No ARQ Reliability n o At least Hop-by-hop. Type of links n o Tuples (names): ordered pairs (R, L) Scheduled vs. non-scheduled. Contact, an opportunity to transfer the data. n Predictable vs. opportunistic. Ref: [2], [3], [6] 7

Reliability o Persistent message storage A o Data S End-to-end vs. perhop reliability. Custody transfer n R Intermittent link B “Always on” link o Head of line blocking. n R provides store-and-forward service Ref: [4] Not delete a message until delivery to another custodian. Even “always on” link is blocked. 8

Suggested architectures o Interplanetar y or satellite GW Internet GW o Sequential heterogeneous regions interconnected by gateways. Para. Net n n GW Sensors Bus route Ref: [2] n Users access more than one network over one device. Different paths for signaling and data. Challenges o Routing, transport protocol, naming, security over multiple paths and etc. 9

Suggested architectures o Data or control information over satellite o Sequential heterogeneous regions interconnected by gateways. Para. Net n DTN n Store and forward n Users access more than one network over one device. Different paths for signaling and data. Challenges o Lightweight cellular network for signaling Ref: [5] Routing, transport protocol, naming, security over multiple paths and etc. 10

Agenda o o o Architecture Routing Multicast Implementation Conclusion 11

Routing Challenges o o o Routing objectives: n Minimize delay n Maximize throughput Per-hop routing vs. source routing. n No end-to-end path MANET’s routing protocols fail. n Proactive and reactive Store-carry-forward n Storage constraints No Topology knowledge n Time varying connectivity graph Ref: [8] 12

Routing Models o Flooding based protocols n o [18] , Erasure coding [11] Knowledge based routing n o Epidemic Oracle [8], Message Ferrying Practical routing [9] [15], [16] , Probabilistic routing n PROPHET [13], RPLM Moby. Space [10] [12] , Max. Prop [14] , 13

Flooding based routing o Epidemic n o [18] Exchanging summary vectors (hash values). Erasure coding n n [11] Use r relays wait for one or rxk relays and wait for k > Message can be decoded if k relays make it to the destination. 14

Knowledge based routing Each edge is a contact meaning an opportunity to transfer data. u v o D x An oracle which provides topology info. n w S o Partial topology info. n o Contacts, buffer constraints, traffic demands… [8] Message ferrying [15], [16] Using history to predict future topology. n Practical routing [9] 15

Routing with global knowledge o Oracle; source of knowledge about topology n n n How much knowledge to achieve an acceptable delay. Modified Dijkstra with time varying edge costs. Source routing. The more knowledge the better performance. (too obvious!) Not realistic! MED (Minimum Expected Delay) Ref: [8] Modified Dijkstra alg with time varying costs based on average edge waiting time. Contact summery (avg. waiting time until next contact) >> 16

Routing with partial knowledge MF: Sparse MANETs with different deployment areas o Message ferrying n 1 n 2 o Ferries broadcast their situation. Ferry route design to minimize drops NP hard reduced to TSP. Practical routing Instead of contact 3 schedules uses contact history. 4 n Per-contact routing vs. Scalability: How increasing number of mobileper-hop routing. n nodes affects number of ferries? Ref: [15] , [16] >> 17

Routing with partial knowledge o Practical routing: Source: A dest: D Per-hop or per-source: A-B-D Per-contact: A-C-D (don’t wait for B) D 2 2 o 2 Message ferrying n n Practical routing n B C 3 8 A Ref: [9] B C 3 0 A Ferries broadcast their situation. Ferry route design to minimize drops NP hard reduced to TSP. n n Instead of contact schedules uses contact history. Per-contact routing. Update the graph upon contact changes. >> 18

Probabilistic routing A B C . 5 o Estimate delivery likelihood. n C A B D . 4 . 2 . 4 n B A C D . 4 . 1 . 5 D B C . 6 . 4 Ref: [10], [12], [13], [14] n Initially assign a delivery probability to each node. Update upon meeting a node based on some criteria. Link state routing to disseminate probability tables. 19

Probabilistic routing A B C . 5 o Estimate delivery likelihood. n C A B D . 4 . 2 n B A C D . 4 . 2 D B C . 6 . 4 Ref: [10], [12], [13], [14] n Initially assign a delivery probability to each node. Update upon meeting a node based on some criteria. Link state routing to disseminate probability tables. 20

Probabilistic routing Moby. Space [10] PROPHET [13] Closest mobility pattern. How? (Dimension could grow dramatically!? ) Delivery predictability > RPLM [12] Routing with persistent link modeling Cost window > Max. Prop [14] Delivery probability Cost of using each node as relay >> 21

Issues of the probabilistic routing. High rank Packets with hop counts < thresh Sorted by hop count Packets transmitted from here o Packets with hop counts > thresh Sorted by delivery likelihood Packets deleted from here Covered n n n o Low rank No a priori knowledge of contacts. Storage constraint and buffer management. Network wide acks to free up buffer space or provide reliable delivery. Not covered n n n What initial values to start with to converge to reasonable delivery probabilities? What if nodes change their habits. How adaptive? No mathematical proof of efficiency of the routing algorithms. Ref: [14], [12] 22

Mobility model and performance analysis o o Node mobility characteristic better performance analysis. Algorithms developed for specific scenarios. n n n Random with core aided nodes. Community based. Mixture of RWP and ferries. Ref: [17], [7] 23

Performance evaluation Delivery ratio Delay Message redundancy Knowledge Flooding High Low (the least) High Buffer congestion Zero Knowledge based MF the highest (even higher than ER) Moderate Low Provided to the algorithm Probabilistic Close to ER with tendency in mobility Moderate Memory (learning from past) Model objective Ref: [7], … , [17] 24

Agenda o o o Architecture Routing Multicast Implementation Conclusion 25

Multicast requirements and challenges. o Disaster recovery, battlefield… n o Who is the recipient? n o o Distribution of news to a group of users; Group membership changes during data transfer. Routing is the most challenging problem. Multicast semantics n n n Ref: [19] Temporal membership: each message contains a membership interval. Delivery interval as well as membership interval. Current member: receiver should be a member at delivery time. 26

Routing models. Broadcast-based routing (BBR) R 2 Group-based routing (UBR) R 1 R 2 R 1 S Epidemic routing to all nodes [19] S Forwarding group [19] 27

Routing models contd… R F Tree-based routing (TBR) R R R 1 R 2 MFER; MF with Epidemic routing [20] R F R S R R R Along the spanning tree containing all receivers [19] MFGR; MF with group routing [20] >> 28

Performance Delivery ratio Delay Message redundancy Topology Knowledge Flooding High (the best) Low High Buffer congestion Not required Tree based Moderate High Low Required ER close to ER Low High Ferry location Moderate Low Ferry location GR Moderate (large group close to ER) Model objective MF Ref: [21] , [20] , [19] 29

Agenda o o o Architecture Routing Multicast Implementation Conclusion 30

TEK system o Remote TEK Client Web Browser ISP TEK Proxy Req o o Storeandforwar d o o Rep TEK Server MIT Ref: [23] , [25], [22] WW W Searching WWW using email. Email-based communication protocol. TEK server located at MIT. TEK client a Java proxy server. Batched requests are emailed to the server. 31

7 DS o o o Internet Ref: [26] Based on epidemic routing. Utilizing opportunistic contacts to pass email messages. Basic platform to develop store-andforward applications. 32

Agenda o o o Architecture Routing Multicast Implementation Conclusion 33

Conclusions and future directions o o o o A killer application! n Implementation efforts have been limited to specific not everyday life applications. n When Joint tactical radio system becomes available? [25] n Para. Net!? Challenges: topology estimation and routing. So far research focus on predictable network topologies. Knowledge based approaches requiring a global view of the network are unrealistic. Hybrid of MF with probabilistic routing!? Absence of real world mobility patterns in algorithms evaluations. Security issues still not discussed! Lack of common APIs to abstract DTN. 34

References o Papers list 35

Back up slides… 36

Probabilistic routing criteria o PROPHET n o Routing with Persistent Link Modeling (RPLM) n n o Monitors link connectivity to calculate its cost. Dijkstra to find a minimum cost path. Max. Prop n n o Delivery predictability calculation. Assigning a cost value to each destination based on probability. Priority queue younger messages higher chances. Moby. Space n n Moby. Point each node’s coordinates or mobility pattern. Distance on each axes probability of contacts or presence in a location. 37

Routing with global knowledge MED ED (Earliest delivery) EDLQ (ED with local queue) EDAQ LP Dijkstra with time varying costs based on average edge waiting time. Contact summery (avg. waiting time until next contact) Dijkstra’s with time varying costs based on edge waiting time. Contacts ED with local queuing information. ED with global queuing information. Linear programming (no knowledge of queues) Contacts + (data queues for the contact at the current time) Contacts + Buffer (queue sizes across entire topology) All + traffic o o Message arrival time at a node must be predicted. Predicted arrival time is used to determine the cost At light load ED performance comparable to EDAQ and EDLQ. Heavy traffic results in congested queues Algorithms with queue knowledge are the winners. 38

VANETS o o West H Propagation of location specific information. Directional propagation protocol n T H T n H East o Custody transfer protocol Inter-cluster routing protocol Intra-cluster routing protocol Routing based on local parameters and TTL n n Routing in the absence of a global naming scheme. Ex: traffic data to cars 5 miles away… 39

PROPHET o o o Delivery predictability is calculated at each node for all destinations B; P(A, B) When node A encounters node B the parameter P(A, B) is updated. Packet transfer if delivery predictability at new node is higher than current one. 40

Link Cost History o o o Idea is cost is related to the duration of connectivity. Link with high transitions will get connected soon. Compared with PROPHET n n o Single forwarding Multi-forwarding PROPHET doesn’t differentiate between carriers X and Y. 41

Erasure Routing q q q Transforms a message of n blocks to a message of > n blocks. Receiver can recover the original message from a subset of blocks Fraction of the required blocks is the ratio r. q q 1/r blocks are necessary Instead of propagating among r relays as in srep distributes them among rk Whether to use r relays and wait for one to succeed or to use rk relays and wait for k to succeed? Worst case scenario 42

Practical routing o o o MEED Minimizing estimated expected delay. Using the contact history instead of contact schedule. Nodes record connection and disconnection periods over a sliding window. Propagating link state table. Per-contact routing instead of source or per-hop routing. D 2 B 2 C 3 8 A 2 B C 3 0 A 43

Practical routing simulation q q Wireless LAN traces converted into a DTN scenario Nodes are connected when associated to the same AP 44

Message Ferrying – Single o Node Initiated MF n n The ferry moves according to a specific route Nodes make proactive movement to meet up with ferry Message drops: buffer overflow or message time out Nodes task time vs. meeting the ferry Ferry Initiated MF n n n o o Long range radios in nodes. Service_Request Location_Update Ferry trajectory control based on minimizing message drop rate along the path. NP-hard problem n n Nearest Neighbor Traffic aware 45

Message Ferrying – Multiple o o o To allow scalability in traffic load Single ferry single point of failure Different scenarios n n n o No interaction Ferry relaying Node relaying Designing the ferry routes to minimize weighted delay. 46

Ferry route design o o o Assigning nodes to ferries to minimize weighted delay. Optimization problem with BW constraints The higher the data rate the longer the route length. 47

Multicasting with MF q q Long-duration partitions makes multicast forwarding structure spanning all group members difficult. Hybrid approach for Ferry initiated MC q q q Message Ferry with Epidemic Routing Message Ferry with Group Routing Adaptive Scheme 48