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					            Delay-Tolerant Networks

Acknowledgements: Most materials presented in the slides are based on the tutorial
slides made by Dr. Ling-Jyh Chen, Dr. Kevin Fall and Dr. Thrasyvoulos Spyropoulos.
             “Legacy” Networks

   Internet, Telephone network
   Wired or fixed links

          Wireless Networks: Cellular

   Cellular Networks: Wired backbone + wireless last link

                                  Wireless Last Hop


   A SUCCESS STORY for voice/SMS!
   Internet? (GPRS): not really (low bandwidth + high price)
Wireless Networks: WiFi

                 802.11, wimax
                 Still: only wireless
                 Higher bandwidth
                  than cellular:
                 Much cheaper/KB
        Wireless Networks: WiFi (2)

   Only Partial Coverage: HOTSPOTS

   No real “mobile computing”!
            Wireless Networks:
        Ad-hoc and Sensor Networks
   Self-organized: no wired infrastructure
   Peer-to-peer: nodes are routers
   Examples: sensor nets; disaster recovery, etc.
    Disaster Recovery             Target Tracking
            Wireless Networks
      Ad Hoc and Sensor Networks (2)
   The past approach: “apply the successful and well
    understood Internet paradigm to ad hoc networks also”
   Assume existence of explicit links (strong enough SINR)
   Establish end-to-end paths
                          End-to-end path

                   S                        D


   Mobility might change these paths: re-establish them
            Wireless Networks
      Ad Hoc and Sensor Networks (3)
   Ad-hoc Networks: A success story?


   No real ad hoc application (killer app) out there
     except maybe some military networks

   Why? Most wireless networks are NOT like the
       The “Internet” Assumptions

 E2E path doesn’t have really long delay
     Reacting to flow control in ½-RTT effective
     Reacting to congestion in 1-RTT effective
 E2E path doesn’t have really big, small, or
  asymmetric bandwidth
 Re-ordering might happen, but not much
 End stations don’t cheat
 Links not very lossy (<1%)
 Connectivity exists through some path
     Even MANET routing usually assumes this
         More Internet Assumptions

   Nodes don’t move around or change addresses
     Easy to assign addresses in hierarchy
     Thought to be important for scalability
   In-network storage is limited
     Not appropriate to store things long-term in network
   End-to-end principle
     Routers are “flakier” than end hosts
         Non-Internet-Like Networks

   Random and predictable node mobility
     Military/tactical networks (clusters meet clusters)
     Mobile routers w/ disconnections
   Big delays, low bandwidth (high cost)
     Satellites
     Exotic links (deep space comms, underwater
   Big delays, high bandwidth
     Busses, mail trucks, delivery trucks, etc.
             Challenged Networks

   Intermittend/scheduled/opportunistic links
   High error rates/low usable capacity
   Very large delays
   Different network architectures
       Characteristics 1: Path and Link
   High latency, low data rate
     e.g. 10 kbps, 1-2 second latencies
     Asymmetric data rates
   Disconnection
     Non-faulty disconnections
        • Motion
        • Low-duty-cycle operation
     Routing subsystem should not treat predictable disconnections as
      faults and can use this information to pre-schedule messages
   Long queueing times
     Conventional networks rarely greater than a second
     Challenged network could be hours or days due to disconnection
Characteristics 2: Network Architectures

     Interoperability considerations
       Networks may use application-specific framing
        formats, data packet size restrictions, limited node
        addressing and naming etc.
     Security
       End-to-end approach not attractive
         • Require end-to-end exchanges of keys
         • Undesirable to carry traffic to destination before
           authentication/access control check
Characteristics 3: End System Characteristics

     Limited longevity
       Round-trip time may exceed node’s lifetime making
        ACK-based policies useless
     Low duty cycle operation
       Disconnection affects routing protocols
     Limited resources
       Affects ability to store and retransmit data due to
        limited memory
           IP Routing May Not Work

   E2E path may not exist
     Lack of many redundant links
     Path may not be discoverable (e.g., fast oscillations)
     Traditional routing assumes at least one path exists,
      fails otherwise
   Routing algorithm solves wrong problem
     Wireless broadcast media is not an edge in a graph
     Objective function does not match requirements
       • Different traffic types wish to optimize different criteria
       • Physical properties may be relevant (e.g., power)
           IP Routing May Not Work

   E2E path may not exist
     Lack of many redundant links
     Path may not be discoverable (e.g., fast oscillations)
     Traditional routing assumes at least one path exists,
      fails otherwise
   Routing algorithm solves wrong problem
     Wireless broadcast media is not an edge in a graph
     Objective function does not match requirements
       • Different traffic types wish to optimize different criteria
       • Physical properties may be relevant (e.g., power)
        Inter-Planetary Internet (IPN)
            Networking in Space
   Existing satellite networks for deep space
     Proprietary, not that efficient, one for each mission

   NASA/JPL: “Extend the idea of Internet in outer
     One reusable network for all missions
     Gain from experience already acquired
Extending the Internet in Space
       Long Propagation Delays vs.
        “Chatty” Internet Protocols
  Propagation Delay is much larger than transmission time!
   (minutes around our solar system)
 Internet protocols are “chatty”

S: “Hi! You want to talk?” (SYN) 20min
R: “Sure! Let’s establish a session” (SYN+ACK) 20min
S: “Ok, let’s go for it!” (ACK) 20 min
(slow start phase)
S: “Can you send me the pic of Mars?”
              TCP chatiness

More than 3h for one 1MB pic!
transmission time (1MB/128Kbps) = 1min !!!
                 Idea: “Bundles”

   Bundle: Application-meaningful message
     Contains all necessary info packed inside one
      “bundle” (atomic message)
     Next hop has immediate knowledge of storage and
      bandwidth requirements
   Optional ACKs
     Depending on class service
   Goal: Avoid chattiness
     Minimize number of propagation delays “paid”
           Intermittent Connectivity

   No more links! Now we have “contacts”

Contact 1:
“Dish A sees earth Sat B from 12:30h to 12h:45h”
Contact 2:
“Sat B sees rover C on mars from 17:30h to 18:30h”
          Idea: Store-Carry-and-Forward

   Store a bundle for a looong period of time.
   Forward when the next contact is available
     Hours or even days until appropriate contact.
   Postal system: “move packages from one storage place to
    another (switch intersection), along a path that eventually
    reaches the destination”
   How is this different from Internet routers’ store-and-
    1) Persistent storage (hard disk, days) vs memory storage
    (few ms)
    2) Wait for next hop to appear vs. wait for table-lookup and
    available outgoing routing port
        Store-Carry-and-Forward (2)

                    12           13                  D


    2                                                    16
                                 11             15
4                        7
                             8        10
Store-Carry-and-Forward (3)
Store-Carry-and-Forward (4)
DTN vs End-to-end Internet Operation
                 Networking in Space
   Heterogeneous networks to interconnect
       Link delay, asymmetry, error rate, reliability mechanism

   Different protocol stack + Different node capabilities

Earth’s Internet: short delays, low error rate, TCP reliability
Sensor network at Mars: short delays, high error rate, data
   aggregation at sink(s)
Satellite backbone: long delays, high error rate, LTP
   (lightweight transport protocol)
Boundles: A Store and Forward Overlay
        What About Retransmission?
             Custody Transfers
   Error rates can be high in wireless links
   What if a retransmission is needed?

Contact 1: “Dish A sees earth Sat B from 12:30h to 12:45h”
Contact 2: “Sat B sees rover C on mars from 17:30h to
Contact 3: “Dish A sees Sat B again in one week”

It’s better that B takes “custody” of message and retries
    sending it itself
Custody Transfer (2)
         Custody Transfer (3)
Moving the Retransmission Point Closer
   Benefits of hop-by-hop vs. end-to-end error
   For paths with many lossy links re-Tx
    requirements are much higher for end-to-end
    (linear vs. exponential)
      E.g. 3 links each with error 1-p:
     (hop-by-hop) 3/p extra bandwidth
     (end-to-end) 3/(p^3) extra bandwidth
   Retransmission overhead is increased by long
    propagation delays
        Regions and DTN Gateways
   DTN gateways are interconnection points between dissimilar
    network protocol and addressing families called regions
      e.g. Internet-like, Ad-hoc, Mobile etc.

   DTN gateways
      Perform reliable message routing & security checks
      Store messages for reliable delivery
      Resolve globally-significant name tuples to locally-resolvable names
       for internal destined traffic

   Name Tuples: two variable length portions
      Region name
        • Globally-unique hierarchically structured region name
        • Used by DTN gateways for forwarding messages
      Entity name
        • Resolvable within the specified region, need not be unique outside
      E.g. { internet.icann.int, http://www.ietf.org/ }
        Delay Tolerant Networks (DTN)

   Kevin Fall (~2002): “maybe these idea is not only
    useful for deep space networks”
           DTN: Very Brief History
   DTNRG chartered as IRTF research group (end of 2002)
     Chair: Kevin Fall (Intel Research Berkeley)

   Architecture evolved from deep-space-focused
    Interplanetary Internet project
     Funded by DARPA 1999-2002
     IRTF Group IPNRG retired when DTNRG formed

   DTN became a DARPA program in 2004

   11+ Internet draft

   Implementation: simulator (DTNSIM) and Linux codes
             Intermittent Connectivity:
              The Technical Argument
   Wireless links are not like wires!

                        End-to-end path

                  S                       D

            Intermittent Connectivity:
             The Technical Argument
   Intermittent Connectivity may appear because of: p
       propagation effects: shadowing, deep fades


                 A                     B
             Intermittent Connectivity:
            The Technical Argument(2)
   Intermittent Connectivity may appear because of:
      Propagation effects, shadowing, deep fades
      Mobility: paths change too fast; huge overhead for maintenance


                  A                  B
             Intermittent Connectivity:
            The Technical Argument(2)
   Intermittent Connectivity may appear because of:
      Propagation effects, shadowing, deep fades
      Mobility: paths change too fast; huge overhead for maintenance
      Power: nodes shut down to save power or “hide”

                              Save power
                              (e.g. sensor)
                  A                   B

                           Low probability of detection (LPD)
                           (e.g. army node)
          Intermittent Connectivity
         The Economical Argument
   Maybe it’s cheaper to not assume connectivity
    rather than enforce it
   Rural areas (countryside, freeways) :
     overprovision of base stations?
     OR just live with a sparse network and “episodic”
   Sensor Networks (attached on animals):
     Enough Tx power for connectivity? ($100/node)
     Very low power nodes? (e.g. RFID, $1/node)
Wireless Connectivity: A Different View

                  End-to-end path

            S     S
           S                             D
                  X      path
                           path     X

     Applications: Sensor Networks for
            Habitat Monitoring
   ZebraNet (Princeton)
   Biologists want to learn animal habits
     Size of herds
     Mobility patterns (running, sleeping, grazing)
     Daily habits (watering)
   Attach “tracking collars” on animals
   Current technology surprisingly inefficient
     Satellite trackers: high energy, low bit rate
     GPS trackers: often have to retrieve collar for data
     Sensor nodes with wireless radios?
      Applications: Sensor Networks for
            Habitat Monitoring (2)
                                                    Herd of zebras
    Herd of zebras
                                Z                   (range of few meters)
    (range of few meters)
                            Z       Z                   Z
                                                    Z       Z

                                            Z                   base station

    Increase power for connectivity?
       Considerably reduce lifetime of network! (power law)
       What about obstacles?
    Live with a sparse network (connected clusters)
    Use DTN principles to carry traffic towards sink
                Vehicular Networks
               “Drive-Thru Internet”
   Vehicle-to-roadside (base station, sensors)
                     Vehicular Networks
                   “Drive-Thru Internet” (2)
              email reply              send email

                                              send email
email reply
                                                           write email

    Asynchronous operation: OK for e-mail!
    Web caching; Local information; download news
    Enough bandwidth even at high speeds!
       Vehicular Networks (VANETs)
        Vehicle-to-Vehicle Networks

   Accident Prevention
   Traffic Reports
   Can be combined with Vehicle-to-Roadside
       Why Vehicular DTN Networks?

   Gradual deployment => initially sparse network

   Even dense deployments: Paths change too
    fast! Before enough time to be discovered
                 An example

   UCLA’s Vehicular Sensor Network
       Internet to Remote Communities

   Internet to underdeveloped countries/remote
   Rural Kiosks (shared among villagers)
     Sell/buy agricultural products
     Banking/Transactions with government
     Land Titles (Hernando Soto)
   Satellite: low bandwidth, expensive
   Microwave links: expensive, unreliable(?)
   Dial-up: low bandwidth, unreliable (?)
   Power network: UNRELIABLE!
    Internet to Remote Communities (2)

   Email, cached/asynchronous services
   Use: Village bus, postman’s vehicle, passing cars
     Equip with radio, antenna, and storage
   Use: dial-up, satellite, microwave links when available
    Internet to Nomadic Communities

   The SAAMI nomadic community of Lapland
     Application: Underwater Networks

   Acoustic signal: short range; longer prop delays
   Environmental sensors: Information collected
    by mobile base stations, or even animals
    equipped with transceivers (e.g. whales)
         Tactical (military) Networks

   Communicating beyond enemy lines
   Need to retain connectivity despite jamming,
   Powering down nodes (LPD/LPI)
        Ad-Hoc Networks (revisited)

   DTN is not only for “extreme” networks
   Maybe it can be used to achieve real “mobile
    computing” without the need for a connected

   Hotspots
     Now we have to “look for” the hotspot
     Mobile computing = the user moves until he can
     Extend Access Point (WiFi) connectivity with ad-hoc
   Data maybe available at local peers
     Establish a peer-to-peer network between local
     Local news/info may be available at a node nearby
     Peer-to-peer wireless
         Pocket Switched Networks

   HAGGLE project (www.haggleproject.org)
   Conference
   Campus
 Delay Tolerant Architecture for Wireless
A necessity:
   Deep space communications, underwater
   Remote, underdeveloped areas

A choice:
   Sensor networks
   Vehicular networks

   Peer-to-peer wireless
     Protocol Design: A Paradigm Shift

   Current protocols are problematic for
    “challenged environments”
   Too many assumptions do not hold
   Need new protocols that take the realities of
    these emerging wireless environments as
    starting points; no ad-hoc fixes
    Security and Application Issues

Security: avoid using infrastructure
 Public Key: need a connected server which will
  map name-to-public-key
 Reputation Systems: revoking a certificate
  might take a very long time

Application: must be delay tolerant
 Network is delay tolerant; what about users??

 Applications, interfaces with persistence
            More about Security Issue

   Problems:
       Secure opportunistic channel establishment
       Mutual opportunistic authentication
       Protection from overrun entities
       PKI works poorly if connectivity is poor
   Approach using Hierarchical Identity Based
    Crypto (HIBC)
     IBC: generate public key based on a string (e.g.,
      address) but private key must be generated by
      private key generator
     HIBC: cooperating hierarchy of PKG’s
     No lookup required to find disconnected node’s
      public key
       More about Security Issue (2)

   Bootstrap
     New user communicates w/PKG over secure
      channel to get initial key pair
     Can also used tamper-resistant device
     Reversal of accumulated source route used for PKG
      to reach new node
   Use of Time
     Add datastamp to public key ID’s helps to minimize
      compromise time if device is lost
     Time-based keys instead of CRL’s (Certificate
      Revocation List)
       • Fail-safe vs fail-insecure (CRLs)
                    Legacy Routing

   Graph: G = {V,E}
   V: set of nodes
   E: set of links
   w(e): E→
     cost function (capacity, energy, queue size)

   Routing (S,D):
    path        PSD = {v0,…,vi,…,vN: vi V, v0 = S, vN = D}
    such that eii+1E
    and                 
                min w(e ii1 )
                Legacy Routing
       Proactive Protocols (table-driven)
   Link-state, distance vector
     Obtain global topology information (Topology Updates)
   Dijkstra’s, Bellman-Ford algorithm
     Calculate minimum cost paths
     Distributed algorithms
   Dijkstra’s algorithm
     Shortest paths from A to V-{A}

    Initialization: cost C(A)=0, C(v) = ; set Q = {empty}
    Loop:     pick v  Q: C(v) is minimum; Q = Q + {v}
              if C(v) + wvj < C(j) => C(j) = C(v) + wvj
    Terminate: when Q = V
             Example of Dijkstra’s Algorithm
Step 1           L(B)=4                    Step 2           L(B)=4
                   B                                          B
             4            2                             4            2

L(A)=0              1             L(D)=   L(A)=0              1             L(D)=6
         A                    D                     A                    D
             6            3                             6            3
                   C                                          C
                 L(C)=6                                     L(C)=5

Step 3           L(B)=4                    Step 4           L(B)=4
                   B                                          B
             4            2                             4            2

L(A)=0              1             L(D)=6   L(A)=0              1             L(D)=6
         A                    D                     A                    D
             6            3                             6            3
                   C                                          C
                 L(C)=5                                     L(C)=5
                      Legacy Routing
                     Reactive Protocols
Step 1) Flood Route Request message (RREQ)
Step 2) Nodes that forward RREQ append their ID on header
Step 3) The path that reaches D first = “shortest path”
Step 4) Send back Route Reply (RREP) with reverse path
  from that found in header
   If path breaks
     Repeat route discovery
     Or fix locally if other subpaths available are known (route
               Legacy Routing for DTN

     Proactive Routing                  S
          (DSDV, OLSR)
                                                  UPD   D     UPD
   Flood Periodic Topology
    Updates (UPD)
                                                    UPD UPD
   S learns next hop to D                                    UPD

   UPD reaches only
    same cluster as D!
      Reactive Routing                  S
           (DSR, AODV)
                                            REQ         D
   Flood Route Request (REQ)   REQ
   S waits for reply from D
   REQ reaches only
    same cluster as S!
                   DTN Routing

   Graph is disconnected and/or time-varying

   G(t) = {V, E(t)}

   G = {V,C}, C = set of contacts ci

   ci = {vi,vj,tstart,tfinish,bandwidth,prop. delay,…}
                Types of Contacts

   Scheduled contacts
     E.g. satellite links, message ferry
     All info known
   Probabilistic contacts
     Statistics about contacts known
     E.g. mobility model, or past observation+prediction
     Bus relay, sensors with random wake-up schedule
   Opportunistic contacts
     Not known before it occurs
     E.g. a tourist car that happens to drive by the village
Routing: Scheduled Networks
           DTN Routing for Scheduled
Problem Setting:
   Set of contacts ci
   Set of storage capacities bci:vi V →
   Set of messages mi = {s,d,t,m}
     Future traffic demand

Evaluation Metrics
   Messages Delivered
   Average Delay (why?)
     Connected with message drops
     Connected with throughput
                     Knowledge Oracles
Problem 1) Assume we know data about (“oracle”)
   Contacts Summary (Oracle)
     Statistics about all contacts (frequency, duration, capacity);
     e.g. contact time cij occurs every T minutes
   Contacts (Oracle)
     Specific info about all contacts;
     e.g. contact cij(t1), cij(t2), cij(tn)
   Queuing (Oracle)
     Info about all queue sizes Q(nij,t) (all nodes and all times)
   Traffic Demand Oracle
     Info about all future traffic demand
     m1 = {s1,d1,t1,m1}, m1 = {s2,d2,t2,m2},etc.

Problem 2) Implement each oracle (centralized/distributed)
         Routing Algorithm Classes

   Zero Knowledge
     No oracles used; only current/local view available
     Worst-case performance (baseline)

   Complete Knowledge
     All oracles used + buffer (resource) information
     Optimal performance (for comparison only)

   Partial Knowledge
     Explore tradeoffs of using only some of the
      available oracles
        Routing with Zero Knowledge

   Oracles used: None
   Algorithm: First Contact
     Look at currently available contacts
     Choose one in random or first that comes up

   Performance: Random Routing
     Random walk on time-varying connectivity graph
     Cycles, oscillate between nodes, dead-end
        Routing with Partial Knowledge

   Computing minimum cost (“shortest”) paths
   Delay:
     Transmission
     Propagation
     Queuing = Waiting for contact + waiting for queue
      to drain
   Link weight w(e,t) = message arriving at edge e at
    time t, is predicted to arrive at end of e at time t +
   Modify Dijkstra’s algorithm
      Minimum Expected Delay (MED)
   Oracles used: Contact Summary
   Edge cost = average waiting time
     average contact wait + transmission + propagation

   Regular routing => minimize average path
   Downsides:
     No reaction to congestion
     Ignores a good link even if it is available
Dijkstra with time-varying costs

   Dijkstra with time-varying costs (2)

Message size = m
Edge Capacity = c(e,t)
Edge Propagation Delay = d(e,t)
Queue backlog = Q(e,t,s)

     w(e,t) = w’(e,t,m,s) = t’(e,t,m,s) – t + d(e,t’)
   t' (e, t, m, s)  min{t" |  c(e, x)dx  (m  Q(e, t, s)}
   Dijkstra’s with Time-varying Costs
Step 1                                                  Time = 0
                   wAB(0) = 5

      L(A)=0                                           L(D)=
                      cBC=(7,10),(14,15),(26,30)… D
                   wAC(0) = 9

 cAC(9,10),(14,17),(25,26),…             cCD=(6,7),(13,15),(23,25)…
 Dijkstra’s with Time-varying Costs (2)
Step 1                                                  Time = 5
  cAB=(5,7),(13,16),(20,22)…            cBD=(3,4),(11,15),(26,28)…

                                          wAC(5) = 6
                           wBC(5) = 2
      L(A)=0                                           L(D)=
                     cBC=(7,10),(14,15),(26,30)… D

 cAC=(9,10),(14,17),(25,26),…           cCD=(6,7),(13,15),(23,25)…
              Earliest Delivery (ED)

   Oracles used: Contacts
   Q(e,t,s) = 0
       Ignores queuing info
       Ignores buffer occupancy
       Source routing
   ED is optimal if:
    1. Low traffic rates (e.g. 1 msg)
    2. Or infinite bandwidth and buffer
   Problems
       If an edge is missed due to lack of bandwidth => may
        result in disastrous behavior
         Earliest Delivery with Local
               Queuing (EDLQ)
   Oracles used: Contacts
   PLUS: look at local queues for choosing paths:

e = (s,*)  Q(e,t,s) = data queued for e at time t
otherwise  Q(e,t,s) = 0

   Problems:
     Buffer overflow
     Potential loops (not consistent topology view
      between nodes when running Dijkstra)
         Earliest Delivery with Global
               Queuing (EDAQ)
   Oracles used: Contacts, Queuing
   Q(e,t,s) = data queued for e at time t at node s
   Source routing
   Requires bandwidth reservation (ensure that no
    later arrivals change the experienced queue size)
     How is this to be implemented?
     Current queuing knowledge depends on reservations
      up to now
     Still no bandwidth
            Variations and Practical
   Re-computing routes for ED (earliest delivery)
     Message might miss contact due to queuing
     If missed => re-compute remaining shortest path (at
      intermediate node)
   Implementing queuing oracle with local info
     Local queuing keeps track of messages it forwards
      and their path
     Extrapolate (expected) queue sizes at other nodes
      (based on capacity and traffic assumptions)
   Message/Path splitting
     Message fragmentation
     Multi-path routing (e.g. for MED algorithm)
      Routing with Complete Knowledge

   What are we missing??
     Buffer constraints
     Future traffic demand
   How do we solve this?
    Multi-commodity flow problem: balance flows
    over links
    Dynamic version: balance flows over contacts

   We can formulate a Linear Program for the
    problem in hand
     note: variable space might grow exponentially
    Routing with Complete Knowledge (2)

   Many ideas from graph theory and network
    flow problems
     Optimize some metric (e.g. average path cost)
     While abiding to constraints (e.g. link/buffer

   Transport Networks with time-varying graphs
     Quickest transshipment of cargo with time-varying
      links (e.g. a periodic cargo flight)
   Dynamic Network Flows
     Rather difficult problems in general
           Performance Comparison

   A network of (20) city buses with radios
   Varying traffic load

   Conclusion 1: ED(-,LQ,AQ) algorithms better
   Conclusion 2: ED algorithm optimal for small loads
        Performance Comparison (2)

   Large bandwidth => ED is optimal
   Small bandwidth => ED closer to MED
        Performance Comparison (3)

   Higher transmission range => more contacts => easier
    to route
   Smaller buffer space => ED* schemes perform better
Performance Comparison (4)
         Practical Routing for DTNs

   How to implement Oracles
   The contact oracle:
   No need to assume full knowledge
   MED: expected contact delay (average over all
    future contacts)
   MEED: estimate future contact delay, based
    on past contacts (sliding window)
              MEED Algorithm
     (Minimum Estimated Expected Delay)
   Keep history of past contacts
   Maintain running average
     Sliding window
     Large window => slow reaction to changes
     Small window => too many updates, oscillations
   Link-state epidemic dissemination
     Whenever a contact changes significantly (x% form
      previous estimate) => flood topology update packet
     Link-state Topology => Epidemic
   Message vector i
     Table with topology updates from nodes NSi

   Two nodes meet: exchange message vectors
    NSA and NSB
   Exchange topology updates not in common
    until NSA=NSB
   Flood new topology updates further
        Calculating the Routing Path

   Eventually topology updates from all nodes
    (global topology) – not all equally “fresh”
   Source Routing? Intermediate hops might have
    more recent info than source
   Hop-by-Hop Routing? What if an infrequent
    contact (large expected wait) arrives first?
   Per-contact routing = assign current contact
    cost 0
          Example of MEED routing

   Link AB (path ABD) are better on average
    than link AC (path ACD)
   But if at time t link AC is up, then ACD is
    better! (per contact routing)
           Link-state DTN Routing:
   Link-state overhead: O(N2)
     If node mobility not restricted everyone sees
      everyone else, eventually

   Can be an interesting approach IFF:
-   Nodes are static: e.g. sensor with wake-up
-   Topology changes infrequently/network is dense
   BUT: If mobility pattern does not have enough
    structure (e.g. IID) then it degenerates to
    random forwarding

    How to extend to keep track of

1)   average queuing
2)   average traffic requirements

    Approximate other algorithms
        EDLQ
        EDAQ
        LP?
                Message Ferrying

   A sparse network of “production” nodes
   Nodes may be static (e.g. sensors) => how to
    bridge partitions?
   Nodes may be mobile, but slow => long delays
     waiting for a contact to occur may take time

   Solution: Use specialized nodes (DataMules or
    Message Ferries) to carry traffic between
    production nodes
     Ferries are always mobile
     No energy considerations
                  Message Ferrying
             1. Enforce Ferry Trajectory
Robots, unmanned aerial vehicles (UAVs) Li al ‘03,
  Zhao et al ’04



                   DataMule         DataMule

 The problem: design optimal trajectories
             Message Ferrying
        2. Use Existing Trajectories
 Scheduled mobility: Uncontrolled but predictable
 mobile nodes (e.g. city buses) Jain et al. ’04



    Predict ferry mobility
    Optimal use of available ferry bandwidth
    Production node trajectory
         Message Ferrying: The Problem
   Ferry mobility
    1. Designed for non messaging reasons (buses)
    2. Optimized for message transfer (robots)
   Production node mobility
       Static vs. Mobile
   Number of ferries
       Single vs. Multiple ferries
   Ferry relaying:
       Yes/No
   Node Relaying
       Node-to-ferry vs. node clustering
           Ferries for non-messaging
   No explicit trajectory design + known schedules
    => could apply principles from earlier presented
    algorithms (e.g. ED, MED, etc.)

   No trajectory design + no/limited knowledge of
    => use opportunistic routing, e.g. epidemic (later)

   Focus on trajectory design cases
            Static Nodes + Single Ferry

   bij = traffic (rate) requirement from node i to j
   Ferry route L of length |L|
   Ferry speed f: ferry cycle T = |L|/f
   d ij = average delay for traffic from i to j
       Wait for ferry: T/2f
       Upload data (queuing at node): f(ferry in range, upload rate)
       Wait for destination (on ferry): T/2f
       Download data to recipient: f(ferry in range, download rate)

                  L
             bij d ij
    dL 
           i, j
                         average delay for all traffic
              i, j
     Static Nodes + Single Ferry (2)

Problem: find trajectory L, such that:

      -   min d   ij
                       (Delay Problem)
                                         (Bandwidth Problem)
      - while satisfying traffic matrix B = {bij}
                     Delay Problem

    Assume infinite/enough bandwidth for bij
      All data uploaded when encountered

     min d   ij   ,such that L passes by all nodes

    If bij = bji => dL= |L|/f

Delay Problem = Traveling Salesman Problem (NP-complete)

 Step 1: TSP approximation algorithms
 Step 2: Local optimization
        Traveling Salesman Problem

   Given a (connected) weighted graph
   Find a path that:
     Visits all nodes exactly once
     And has a minimum cost
                  Bandwidth Problem

   Increase route (local detour) to satisfy bandwidth
    requirement                  Tx rate
        Path extension for i                       Traffic demand
                               (x i  2r)W         of i (per cycle)
                                             si
                               | L |  x j

   Minimize amount of increase (Linear Program)

        minimize        x

        subject to Wx i  s j  x j  si | L | 2rW,      xi  0
         Optimal Trajectory Design:
            The online problem
   Previous case: traffic requirements known in
    advance => offline, optimal solution

   What if traffic requests arrive on-demand

   Problem: design trajectory to optimally serve
    existing requests
     Minimize message drop rate
     Minimize expected delivery delay
            Mobile Nodes + Single Ferry

     Ferry has a predefined route, which is known
     Nodes decide when to move close to the ferry
      to upload data (Node-Initiated Message
      Ferrying, NIMF)

Task (e.g. sensing)                    Receiver
      Mobile Nodes + Single Ferry (2)

   Goal 1: minimize time not performing task
     E.g. time moving = time not sensing

   Goal 2: minimize message drop ratio
     While “working”, outgoing messages accumulate in
      buffer => buffer overflow
     While not going to ferry, incoming messages
      accumulate in ferry => buffer overflow
     Messages have TTL => if not delivered in time they
      are dropped
      When to Move Towards Ferry?

Keep msg drop rate low:
 Di(t) = msg drop rate at i (outgoing)

 Df->i(t) = msg drop rate for i at ferry (incoming)

 Gi = msg arrival rate at i

 Gf->i = msg arrival rate at ferry for I

   (Di(t) + Df->i(t))/(Gi+ Gf->i) >    (condition 1)

Keep fraction of time not performing task low:
    (task time)/(total time) > w (condition 2)
             Shortcomings of NIMF

   What if node task is correlated with message
     e.g. task = sensing data that needs to be periodically
      transmitted to a sink
   Conditions 1 and 2 may not be able to be
    satisfied at the same time! WHY?

   How are the nodes mobile? Robots? A person
    decides to move close to the bus?
     Static Nodes + Multiple Ferries

Case 1: No ferry interaction

Case 2: Ferry relaying
   Ferries can exchange data with each other
   Synchronization between ferries

Case 3: Node relaying
   Node overhead for storing inter-relay traffic
           Ferry Trajectory Design

   Phase 1: Assign nodes to ferries
   Phase 2: Choose path for each ferry

   Phase 3: Fine tune route to meet traffic demand
       Single-Route Algorithm (SIRA)

   All nodes follow the same route
     Constant speed and distance
     No interaction

   Phase 1: all nodes to all ferries

   Phase 2,3: similar to single ferry
     step 1: Traveling Salesman approximation
     step 2: Local delay optimizations (waitm = wait1/m)
     step 3: minimum route extension to satisfy traffic
       Multi-Route Algorithm (MURA)

   Different Routes + no Relaying

 Algorithm:
Step 1: assume n ferries – assign one to each
Step 2: estimate ED (expected delay) and
  reassign until m ferries and ED minimum
Step 3: refine assignment for end-to-end
Step 4: calculate optimal route for each ferry
Estimating ED (expected weighted delay)

   Calculate weighted delay per route
     Say route with k relays
   Route delay is a tuple (E*,E’)
       E* = excess capacity
       E’ = expected delay if capacity is met
       a = total data rate
        = service rate of route = 0.5 k W

            L(1  a  μ), if a  μ
        E 

           0,             if a  μ
             0,                   if a  μ
        E'        1       a
               L(1  )(1      ), if a  μ
                   k      μ-a
     (Re)assigning Nodes to Routes

Re-assign based on 4 operations – goal is to
 get m ferries and minimum ED
Op.1) overlap (i,j): extend one route to include
 node of other
Op.2) merge (i,j): combine routes i,j into one;
 ferries = ki+kj
Op.3) merge-(i,j): combine routes i,j into one;
 ferries = ki+kj-1
Op.4) reduce(i): ki = ki - 1
     (Re)assigning Nodes to Routes

                 The algorithm

 Problem 1: sender-destination not in same route
 Problem 2: route traffic demand > route capacity

Continue overlap/merge until assignment is feasible…OR
      Node Relaying Algorithm (NRA)

 Multi-hop routing:
node S  ferry fi  node r  ferry fj  node D

   Bound number of hops to maintain throughput
    (Gupta et. al)
   Overhead on relaying nodes
      Node Relaying Algorithm (NRA) (2)

   For each S-D pair nij: geographic routing => path of cells (e.g.
   Overlap operation between Cx,Cy => shared node is relay
   Assign ferries: 1 to each cell -> add extra ferry to highest EWD
       Ferry Relaying Algorithm (FRA)

   Data is relayed between ferries => no node
   Similar to NRA algorithm…until last step
   After routes are calculated per cell, need to
    synchronize between cells (not easy)
            Performance Analysis
             with Multiple Ferries
   Some simulation results show that MURA (non-
    relaying) has the best performance

   Is it because of the extra resources required by
    message relaying?
   Is it because of the specific algorithms chosen
    for relaying (i.e. could find better ones)
   Does it depend on traffic pattern? if uniform
    traffic, and no traffic weights, wouldn’t MURA
    routes need to cover ALL nodes??
     Multiple Ferries with Independent
            but Known Routes
   Ferry mobility is not related to data delivery (e.g.
    bus of networks)
     Hence, it cannot be changed
   Calculate inter-ferry contacts based on their
    mobility schedules
   Apply algorithms like MED, ED, etc.
   Maybe even MEED, or some opportunistic
    routing if schedules are not fully deterministic
    (e.g. traffic jam, etc.)
        Summarizing: DTN Routing

Scheduled/Known Contacts:
  Modified Dijkstra Algorithm (time-dependent weights)
  Dynamic Flow Problems

Enforced Contacts with Specialized Nodes (Ferries):
  Design of Optimal Mobility Paths (TSP)
  Optimal Assignment of Ferries

Opportunistic Contacts?
    Contacts not known in advance
    No extra nodes; only the mobility of the nodes themselves
     is available
Routing: Opportunistic
     Routing with Scheduled Contacts

   Graph is disconnected and/or time-varying
   Set of contacts C: known
   Set of nodes V: known

                      (B,D) = {10,12},{19,21}

                      B                              D

     A                    D

                      C (C,D) = {8,10},{15,17}
               Tx Range
                                     Tx Range
              Routing with Unknown Contacts
                  Opportunistic Routing
     Graph is disconnected and/or time-varying
     Set of contacts C: unknown!
     Set of nodes V: often unknown too!

                            (B,D) = ??

                            B              WHERE IS D?


                                                WHERE IS D?
WHERE IS D?                 C (C,D) = ??
                     Tx Range                        D
                  Epidemic Routing

   Give a message copy to every node encountered
     essentially: flooding in a disconnected context



                             B                          D

                      D                                 D

            Epidemic Routing (2)
             Message Vectors
   Node A encounters node B

Message Vector of A                  Message Vector of B
Dest ID    Seq. Num.                 Dest ID    Seq. Num.

     D         0                        D           0
     G         1                        E           0

     F         0                        F           0
                                        F           1
            Epidemic Routing (2)
             Message Vectors
   After message exchange

Message Vector of A          Message Vector of B
Dest ID    Seq. Num.         Dest ID    Seq. Num.

     D         0                D           0

     E         0                E           0

     F         0                F           0

     F         1                F           1

     G         1                G           1

    Two nodes “encounter” each other when they are inside
     Transmission Range
    How do they know?
    Beacons: periodically transmit a “HELLO” message to
     discover neighbors
        e.g. Bluetooth association
    Implications:
1.   Some encounters might be missed
2.   Encounter not immediately when in range

     Encounter => MSG vector exchange (+other info)
                 Delay of Epidemic Routing
                (a coloring problem analog)

                                                     1 M K
                                               ED        1Ti
                                                    M  1 K  i 1

T1 = 1 red → 1 blue
                  2       T2 = any of 2 red → any blue

                                                            M nodes
                                                            I.I.D. mobility
                                   Epidemic Routing Performance

               Redundant copies reduce delay
               But: too much redundancy is wasteful and often
                disastrous (due to contention)

                               Transmissions for Epidemic Routing                                           Delay for Epidemic Routing
                      160000                                                                      7000
                                     epidemic                                                               epidemic

                                                                    delivery delay (time units)
                      140000                                                                      6000
                                     optimal                                                                optimal
total transmissions


                       20000                                                                      1000

                           0                                                                         0

                                        increasing traffic                                                      increasing traffic

                                Too many transmissions                                                   Plagued by contention
         Randomized Flooding (Gossiping)

       “Spread” the message with a probability p ≤ 1
         p = 1) epidemic
         p = 0) direct transmission


                                   Outcome < p) Give a copy
                                   Outcome > p) Don’t give copy
                K-neighbor Epidemic

        Each node receiving a copy, can copy it again
         up to K times


 E                                   Already given 2 copies!
                                     Node E cannot fwd more


           Flooding-based Schemes

   Can reduce the transmissions of epidemic
     With some penalty on delay!

   Given long enough time, all nodes receive a
     Still flooding-based!

   Let’s re-think the problem. Must we flood
    everyone (or almost everyone)?
          Single-copy vs. Multi-copy
              routing strategies
     “Single-copy”: only a single copy of each message exists in the
      network at any time

     “Multi-copy”: multiple copies of a message may exist concurrently
      in the network

          Single-copy                           Multi-copy

+ lower number of transmission              + lower delivery delay
+ lower contention for shared resources     + higher robustness
             Choosing A Next Hop
    A local and intuitive criterion: A forwarding step is efficient
     if it reduces the expected distance from destination
    usually: reduction of expected distance => reduction of
     expected hitting time



Efficient Routing : Ensure that each forwarding step on the
average reduces distance or hitting time with destination
                 Direct transmission
   Forward message only to its destination
     simplest strategy
     minimizes transmissions



     The Delay of Direct Transmission



   EM: expected meeting time
     2 nodes starting from stationary distribution
     EM > ED: EM is a lower bound on delay!
   ET: expected hitting
     1 node is static (with position from uniform distribution
              Randomized routing
   A node forwards message to a new node with
    probability p; NO Duplication! It’s Hand-over!



                       B                  D

                   D                     D

    Why Transmitting is Faster Than Not!

                      D                D



   Transmission Speed is Faster than Node’s Speed!
Why Transmitting is Faster Than Not!
                         EATD = ET(d)

                ET(d  1)  ET(d  1)
      EB TD 

                                                  B   A   B             d

                                        PBA = ½               PAB = ½
                 Utility-based Routing

Utility UX(Y) = f(tX(Y))                 D
             t(D) = 26
                                                       Last encounter timers
Policy: forward to B iff
                                        t(D) = 0   tX(Y): time since X last saw Y
UB(D) > UA(D) + Uth
                                                   Indirect location information
             D         D                                  diffused with node mobility

         A         B
                       tB(D) = 100                 smaller timer  closer distance
     tA(D) = 138                                          For most mobility models
                                     t(D) = 68
          t(D) = 218
             Utility-based Routing (cont’d)
                              EATD = ET(d)

                     ET(d  1)  ET(d  1)
           EB TD 

   EBTD  PBA  ET(d  1)  (1  PBA )  ET(d  1)


                                                        B   A   B             d

                                              PBA = ½                   <
                                                                    PAB = ½

   Result 1: Utility-based routing has a larger expected delay
   reduction than the simple randomized policy
              Problems with Utility Routing


                                   tA(D) = 20
                                                                     tA(D) = 20

                                tA(D) = 20

          tA(D) = 200

   Timer values are good indicators of proximity only if their value is
       Timers/utility updated only when destination is found
       If source’s (relay’s) neighbors happen to have larger timers, message gets
        stuck for a long time
                      Transitivity Idea
   If A sees B, and B has recently seen D, then A is
    probably close to D too.
     update tA(D) when A encounters B
        • cache of most fresh entries for scalability
     (dAB): expected time to cover distance dAB
     tA(D) = tB(D) + (dAB)
        • (dAB) = (dAB)2 (random walk)
        • (dAB) = dAB (random waypoint)

         No transitivity                                      Transitivity

            PDF of timer value of A for D, when A is far from D
                    Seek and Focus
                 A hybrid routing strategy
   Set of node utility values: A time-varying, probabilistic
    utility-field with the global maximum at destination
   Utility-based routing is a greedy search of the field
       Issue: message often gets stuck at local maxima

                              Seek and Focus
        Seek phase: If current utility is below Uf perform randomized
        forwarding (quickly look for a “good lead”)
        Focus phase: If current utility is above Uf perform utility-based
        routing for at most Tf time units (follow the lead)
        Re-seek phase: If no better relay is found for Tf, perform
        randomized routing for at most Tseek or until a better relay is found
        (if stuck at local maximum, do “perimeter search”)
           Oracle-based optimal
   Assume all future movements are known
   Then, the algorithm picks the sequence of
    forwarding decisions that minimizes delay

   Note that flooding (multi-copy strategy) has the
    same delay as this algorithm when there is no
                                            Effect of Connectivity
                                         Random Walk (“local” model)
                                      Transmissions (Random Walk)                                                           Delivery Delay (Random Walk)
transmissions (per msg)


                                                                                        time units (LOG SCALE)
                                                                utility (no trans)
                          600                                                                                    10000
                                                                utility (trans)
                          500                                   seek&focus (trans)
                          400                                   optimal                                           1000
                          200                                                                                      100
                                40 (8.6%) 50 (14.8%) 60 (27.7%) 70 (52.9%) 80 (79.2%)                                    40 (8.6%) 50 (14.8%) 60 (27.7%) 70 (52.9%) 80 (79.2%)

seek&focus                                Tx Range (connectivity %)         X-axis: Tx Range                                        Tx Range (connectivity %)

                                      Increasing connectivity               (Connectivity)                                                         seek&focus
                                                                                                                                    Increasing connectivity
                                 Randomized has smallest delay
        Y-axis:     But, with order(s) of magnitude more transmissions
                                                       Y-axis: Delivery delay
        Transmissions per msg
                                                       (LOG SCALE)
                 Utility-based with transitivity performs very few transmissions
                                      But, with up to 10x worse delay than randomized
                                      Without transitivity things are even worse
                                 Seek & Focus achieves both low delays (close to randomized)
                                  and low transmissions (slightly higher than utility-based)
                                                 Effect of Connectivity
                                              Random Waypoint (non-local)
                                      Transmissions (Random Waypoint)                                                          Delivery Delay (Random Waypoint)
                          140                                                                                        10000

                                                                                            time units (LOG SCALE)
transmissions (per msg)

                                  randomized                           random
                                                                       utility (no trans)
                           60                                          utility (trans)
                           40                                          optimal
                           20                                                                                                       utility
                              0                                                                                         10
                                  30 (5.7%)   40 (8.6%) 50 (14.8%) 60 (27.7%) 70 (52.9%)                                     30 (5.7%) 40 (8.6%) 50 (14.8%) 60 (27.7%) 70 (52.9%)

utility                                          Tx Range (connectivity %)                                                                Tx Range (connectivity %)

                                  Randomized not fast for non-local mobility models
                                       A bad forwarding decision is costly
                                       Still high transmissions
                                  Utility-based has good delays and low transmissions
                                       Choice of the right transitivity function is important!
                                       No transitivity, or wrong transitivity (e.g. random walk) is really bad.
                                  Seek & Focus achieves even better delays
                                       Yet, with slightly more transmissions
             Single-copy Strategies:
                Lessons Learned
   Utility-based forwarding can be a good routing
     ONLY IF utility function is correctly designed!
      (transitivity + mobility model stats)
   Seek and Focus (hybrid) is the best candidate if
    a single-copy routing scheme has to be used
     can fix some of the utility-based routing shortcomings

   BUT, best single-copy strategy still an order of
    magnitude slower than optimal!
                   2-hop Scheme

   Source gives a copy to any relay encountered
   Relays can only give copy to destination

                   Relay C cannot FWD to B

                             B                      D

                       D                           Dst
                                         Relay C can FWD to Dst
          2-hop Scheme Performance

   How many transmissions?
 Delay?
T1 = time until source meets any node (M-1)
T2 = time until source meets any node (M-2)
       epidemic: time until 2 red meet any of M-2 (smaller)
                                M  n 1
           ED(n)  ETn 1               ED(n  1)
                                 M n
         Rem. Delay              Prob{next node
         after n copies          not DST)

BUT: a relay node may meet destination in the
    Controlled Replication (“Spraying”)

   2-hop scheme uses (M-1)/2 copies
     Still a lot! Only half of epidemic
   Limit number of copies to L (small, fixed)
     Transmissions = L!
   L = 2) Achieves O(1) per node capacity and
    deals with Kumar’s and Gupta’s conjecture
    (capacity →0) (Grossglauser et al. ‘01)
   L > 2 and L = O(1): (constant L)
     Still capacity gain
     Transmissions << M
     Multi-path diversity to reduce delay (Spray & Wait)
                Source Spraying

   Only source can give a copy (like 2-hop)
   Start with L copies; give one to L-1 first relays
   Delay (Src Spray) > Delay (2-hop)
     Assuming no contention!
                 Tree-based Spraying

   Use forwarding tokens; SRC starts with L tokens
   When L = 1, can only forward to DST
           L=1                   L=1
                           L=1    D

    L=4                     B               D
                      D                    Dst

            Tree-based Spraying (2)


                   n1            L-n1


                                nj           j-nj

   I.I.D. movement => Binary is optimal (nj = j/2)
   Heterogenous => high complexity
       Binary Spraying = Time-limited
   Do epidemic spreading until time T
   After T, switch to direct transmission
   If T = ETL then the same as token-based (on
     Remember: ETL = time until epidemic “covers” L
      Replication Method Matters

                                    Delay of Spray and Wait
                                                        source spray and wait
                                                        binary spray and wait
                      3000                                              (analysis)
         time units

                                5           10           15              20
                                            L (# of copies)

                             100x100 network with 100 nodes

1. Efficient spraying becomes more important for large L
2. Few copies suffice to achieve a delay only 2x the optimal!
                                            Effect of Traffic Load
                              (Rand. Way. - 500x500 grid, 100 nodes, Tx Range = 10)
                      50000                                                                         4500
                                 random-flood                                                                                         increasing traffic
                      45000                                                                         4000

                                                                      Delivery Delay (time units)
                      40000                                                                         3500
Total Transmissions

                      35000      spray&wait(L=16)                                                   3000
                                 spray&wait(L=10)                                                   2500
                      25000                                                                         2000
                                                                                                              ic          od             od            0)            6)       us
                                                                                                           em        f lo           f lo            =1            =1      f oc
                         0                                                                              id         m             ty           it( L         it( L       k&
                                      increasing traffic                                             ep         do         u tili          wa            wa          see
                                                                                                            ran                         y&           y&
                                                                                                                                  spra        s pra

                                                             Transmissions                                                                  Delay
                                 Low traffic                  >10x epidemic                                                  same as epidemic
                                                           3-4x other multi-copy                                          1.4-2.2x other schemes
                                 High traffic                    1.8-3.3x                                                          same as above
    Spray and Wait: A good scenario
        1                                                              by Relay 2
                         12               13                  D


2                                                                 16
                                          11             15

            5                                  10

                6       Covered by Relay 1

            Relays are highly mobile
            Relays routes are uncorrelated
     Spray and Wait: A bad scenario

                         12               13                      D


2                                                                     16
                                          11                 15
                                                    Node D’s community
Node S’ community                     8
             5                                 10


    Relays move slowly
    Relays move locally and are correlated
         Spray & Wait Performance

   Spray and Wait has desirable performance
     IF nodes move frequently around the network
      (e.g. VANETs, a mesh network over city buses,

   But, Spray and Wait may get in trouble if
     nodes’ mobility is restricted inside a local area
     nodes’ mobility is extremely slow (e.g. human
                  Spray & Focus

   1st Phase: Binary Spraying
     like Spray & Wait

   2nd Phase: Utility-based routing with transitivity
     for each copy

   Advantages:
     still: few transmission + redundant copies
     plus: take advantage of good transmission
     copies don’t get stuck in local neighborhood
                                         Effect of Connectivity: Random Walk
                                                                                (500x500 square, 100 nodes)
                                                      Transmissions                                                                                        Delivery Delay
                                70                                                                                                       3000
Transmissions (thousands)

                                                                                                           Delivery Delay (time units)
                                                                                          K = 15 (7.8%)
                                60                                                                                                       2500                                            slow!
                                                                                          K = 20 (14.9%)
                                                                                          K = 25 (35.9%)                                 2000
                                40                                                        K = 30 (68%)
                                30                                                        K = 35 (92.5%)
                                10                                                                                                        500

                                 0                                                                                                          0
                                               od                    od             it                us                                         mi
                                                                                                                                                    c                  od               od             it           us
                                        -flo                     -flo             wa              f oc                                                          -flo                -flo             wa           oc
                                                                                &                                                             ide                                                 y&           &f
                                   l ity                    om               ray             y&                                            ep              l ity               om               ra          ray
                                uti                     d                 sp              pra                                                           uti                d                 sp
                                                    ran                                  s                                                                             ran                               sp

                                    Transmissions: still ~10x improvement for both protocols
                                    Spray & Wait is slow: suffers from locality of movement
                                    Spray & Focus is the fastest:
                                       Takes advantage of locality
                                       Close-to-optimal (unless very low transmission range)
              Heterogeneous Scenarios

 Base Stations (pstatic)

                            Roam around network
                                                                         stay inside
                                                          1-pL(i)        community


                                                    Community (local) Nodes
Fast/Mobile Nodes (pfast)
             Effect of Connectivity:
         Community-based Mobility (cont’d)

Scenario 1: Homogenous                                          Delay Improvement by Spray and Focus
   Community nodes (100%)                                 25
                                                                     Scenario 1

                                  Delay(SW) / Delay (SF)
Scenario 2: Two types of nodes                             20        Scenario 2
   Community nodes (90%)                                            Scenario 3
   Roaming nodes (10%)
Scenario 3: Four types of nodes
   Community nodes (40%)
   Local nodes (40%)                                      0
                                                                 40 (8.6%)   50 (14.8%)   60 ( 27.7%)   70 (79.2%)
   Roaming nodes (10%)
                                                                     Transmission Range (Connectivity %)
   Static nodes (10%)
        Spray Routing: Summarizing

 “Non-local” mobility models: Spray and Wait
     10x fewer transmissions AND smaller delay!
     Spray and Focus has similar performance; but we
      don’t really need it

 “Local” mobility models: Spray and Focus
     Spray and Wait is slow
     Spray and focus has close-to-optimal performance

   Why does spraying work?
     Law of diminishing returns for number of copies used

   Smart Replication
     Who should get the copies?

   Other Utility Functions
       Energy
       Mobility
       Trustworthiness
       GPS location
       Queue Size
       Hybrid
            An Analytical Framework
              Why do we need it?
   Confirm our previous observations
   Predict performance under a larger range of

   Use this theory for system-design
     e.g. choose the right number of copies for Spraying
           An analytical framework for
           “mobility-assisted routing”
   Component 1) Hitting and Meeting Times:
       the basic building block;
       depends on mobility model;
       calculated for: random walk, random direction, random
        waypoint, and a new model

   Component 2) Multiple copies

   Component 3) Forwarding a message

  “Plug n’ calculate”: calculate the delay of any
scheme by combining the right components
              Performance Analysis
             An Analytical Framework
   Assumptions
     Network area:
        • Random walk: grid (torus) – discrete movement
        • Waypoint-based models: square (torus) – continuous movement
     Infinite bandwidth, infinite buffers
     calculate delivery delay

   Notation:
     M: number of nodes
     N: network area
     K: transmission range (small enough to have partial
      connectivity )
     EATB: expected hitting time from A to B
     ET: expected hitting time starting from stationary distribution
     EM: expected meeting time between two nodes starting from
      stationary distribution
                   Random Walk
           Hitting Time (Tx Range K ≥ 0)
   Hitting time ET = EXTA (EM still equal to ET/2)

                                     1) EXTA = EXTY - EATY

                                     2) EXTY = cNLogN

    p = 0.25
                                                  2K 1  K  2 
                                                  2K  1   N
                                     3) E A TY                 
       X                                                        

                              2K 1  K  2 
               ET  N cLogN 
                                            
                                  2 1 
       Random Direction (Random Waypoint)
                  Hitting Time
    Movement is a set of “epochs”

1.   Probability that any given epoch                                D

     hits the destination
                                                                         epoch    N
             Phit                                          K
2.   Expected number of such                epoch
     epochs (geometric)                     start S              L

                  1    N
          Ne        
                 Phit 2KL
3.   Multiply by the expected     4. EM: divide by (normalized) relative
                                                                             
     duration of each epoch Te          speed between S and D, vr  E[| v S  v D |]
                                                                      E[| v S |]
                 N                                         ET
           ET      Te                                EM 
                2KL                                        vr
    Modeling Epidemic Spreading
Case Study: Epidemic Routing/Optimal
                                               M nodes
                                               Tx Range = K

                                  EDopt    EM
                                               (M - 1)
EM                                        where HM-1 is the
       1             EM
M -1                                      harmonic sum
                   2(M - 2)                         M1
                                          HM1  
           2                                         1
                                                 i1 i
          Modeling Epidemic Spreading
        Markov Chains (Probabilistic Model)

               Prob(ii+1,t) = (N-i)*i*t
N+1: nodes
1/: meeting time
state i: i copies
state A: DST found           Epidemic Routing

                               2-hop Routing
           Modeling Epidemic Spreading:
            Fluid Models (Deterministic)
    Assume N (num. of nodes)  
    I(t) = average number of “infected” nodes at time t
                         I (t)  λ (N  I) I
    P(t) = P(Td <t) CDF of delivery delay
P(t+dt)-P(t) = Prob{t ≤ Td ≤ t + dt}
= Prob{DST meets one of nI(t) infected nodes in [t,t+dt]} * Prob(Td>t)
= E[Prob(DST meets nI(t) | nI(t)] * (1-P(t))
= E[nI(t)dt]*(1-P(t))
=  I(t) * (1-P(t)) dt

=>                       P (t)  λ I (1  P)
                             '                        (2)
     Modeling Epidemic Spreading (2):
       Fluid Models (Deterministic)
   Ordinary Differential Equations (ODEs)
     Or systems of ODEs
     Sometimes PDEs, too.
   Solve (1) for I(t) – it’s a separable ODE
                  I(t) 
                         1  (N  1)e  λNt
   Replace I(t) in (2) and solve for P(t)

                  P(t) 
                         (N  1)  e  λNt
    Expected Delay ETd  (1  P(t))dt 
                                                λ(N  1)
        Modeling Message Forwarding
      Case Study 2: Randomized algorithm
  q: probability of Tx jump


   q = p • P(at least one node within range)       Average jump length:

   f(K): average transmission distance             D = 1 – q + q f(K)

1-q: probability of random walk jump
       Message Forwarding (cont’d)
    Case Study 2: Randomized algorithm
   Approximate actual message movement with a random
    walk performing D independent 1-step jumps at each
    time slot
   Note: This walk is slower than the actual walk
     would reach destination later, on the average
   Define an appropriate martingale to show that:
                                  2 EDdt
                        EDrnd   
                                   D 1
            Message                             Destination
            movement                            movement

Note: D + 1 ≥ 2  randomized is faster than direct transmission

Random Direction/Waypoint: Similar procedure gives
exact result
                     Utility-based algorithms
                           (no transitivity)
                                                       p   t x

    Prob{node with higher utility within range AND node is closer to D}

D                                  p         p         p             p          p         p
0        1       2           r-K       r-2       r-1             r        r+1       r+2       r+K   N
                                   p         p         p             p          p         p

       p: probability of no forwarding =>                                p
       random walk step

             Prob{node with higher utility within range AND node is farther than D}

     EDutil is simply the expected hitting time from stationarity
     to a state ≤ K
     *Similar procedure for seek and focus without transitivity
                    Source Spray and Wait

       Let ED(i) denote the expected remaining delay after i copies are
       Clearly EDsw(src) = ED(1)
       ED(1) can be calculated through a system of recursive equations
                                                                If new node found by
                                          If not destination,
                                                                source, forward another
                                                                     ED(i  1)
                                          add extra term           i

                                                   M  i 1                   i 1         

Expected remaining
                                                                                         
                                    i(M - i)
delay after i copies    ED(i)                                                      ED(i)   
are spread              Time until a new            Mi                         i          
                        node is found

                                                                           If found by relay,
                                                   P(not destination)      do nothing
                        If destination, stop
       A similar recursion procedure gives the delay of Optimal Spray
        and Wait
        Case Study: Choose the Number of
            Copies for Spray and Wait
   Exact delay not in closed form
   Derive a bound in closed form
   This is an upper bound for any Spray and Wait algorithm
                                 Probability a wait
                                 phase is needed
            Spray Phase                               Wait Phase

                                     ML 1
                 EDsw  ES                 EW

                 L 1
                  EM                                      EM
           ES                                        EW 
                i1 M  i                                    L
                            Bound is tight for L<<M
         Choosing L to achieve specified
   Suppose we want to achieve EDsw=  EDopt for
    some >1
   We choose the minimum L that satisfies
         L 1
             EM M  L  1 EM         HM-1
          M  i  M  1 L =   EM (M - 1)

            Upper bound on EDsw       EDopt

   Some values (for M=100):
               1.5   2      3    4    6      8   10
     Lmin       21    13     8    6    4      3   2
  What If Network Parameters Are Unknown?

  To compute Lmin we need to know M
  Use meeting times statistics and do online estimation
     EM                                          Estimation of M (200x200 grid)
T1                                    400
     M 1                              350                                   Actual M = 200

         1     1                     300                                   Estimated M

T2  EM                   M value

         M 1 M  2                  200

Estimator:   ˆ  2T2  3T1
                 T2  2T1                    0    1000     2000      3000      4000
                                                         number of samples

Applies to any mobility model with exponential meeting times
Routing: Other Issues
    Epidemic Routing: Wasted Resources

 Epidemic routing hands over a copy to every
  node encountered…
Even after the message has been delivered!
   After the destination is encountered by at least
    one relay, no need to keep other copies around
     Unnecessary transmissions (energy, throughput)
     Valuable buffer space
           Reducing Resource Waste
            “Dis-infection Schemes”
    After one copy has been delivered:

1.   Inform other nodes to stop spreading more
        No need to give extra copies to “non-infected”
2.   Remove copy from buffers
        Clear up buffer space of infected nodes
                     Full Erase

   When encountering the destination => delete message
    from buffer


            F                                 E
            D                       D

                                B                            D
        A                   D
                        C           D                       dst
                                        Delete local copy

   Node may get a copy again!
   Delete packet AND maintain an antipacket
     msg id: e.g. (src,dst,seq)
     Implies that node is recovered
               D                                               D

           F                                               E
           D                                     D

                                             B                           D
       A                                 D
                                     C                         B         D

                   No new copy to                    Recovered copy
                                                     Delete local Node
                   recovered nodes                   msg: (S,D,0)

   Propagate anti-packet to already infected nodes

                 D                                               Avoided this Tx

             F                                       E

         A                                 D
                                                         X   D

                                       C                 B             dst

                     C recovered! to
                     No new copy               Recovered copy
                                               Delete local Node
                     recovered nodes
                     msg: (S,D,0)              msg: (S,D,0)

   Propagate anti-packet to ANY node encounter
      Vaccinate susceptible nodes
Avoid this Tx, too
                  D                                 Vaccinate E

              F                                 E

          A                             D

                                        C                   dst

                      No new copy
                      C recovered! to
                      recovered nodes
                      msg: (S,D,0)          B
                 SIR Model
   I: infected nodes
     Nodes with a copy, and no anti-packet
   R: recovered nodes
     Nodes with an anti-packet
   S: susceptible nodes (S = N – I – R)
     Haven’t ever received a copy or anti-packet
              SIR Model: ODEs

   Immune:     I (t)  λ (N - I - R) I - λ I

                R' (t)  λI

                I ' (t)  λ (N - I - R) I - λ I(R  1)
   Immune-TX
                R' (t)  λI(R  1)
           Total number of transmissions

E[Tx] = limt{I(t) + R(t)} – I(0)

    Immune
    I ' (t)  λ (N - I - R) I - λ I   
                                            N - I - R - 1, I(0)  1
    R' (t)  λI                         dR

     I(R)  ( N  1)e R  R  N

     lim I(t)  0  lim R(t)  lim [( N  1)e R  N]
        t               t         t 

     lim R(t)  N            E[Tx]  N - 1
    N 10 t 
      Total Number of Transmissions


    I ' (t)  λ (N - I - R) I - λ I(R  1)
    R' (t)  λI(R  1)

              R 2  (N - 1)R  1
     I(R) 
                    R 1

              N - 3  N 2  2N  5
     E[Tx] 
            Performance of Buffer

The more aggressive the recovery scheme
1) the less the total transmissions (ignoring overhead of
2) the smaller the buffer occupancy
                Queuing Policies

   Limited buffer space
     Nodes with little memory (e.g. sensors)
     Nodes might offer only a small chunk of memory for
      3rd party traffic

   What if a message has to be dropped?
                Queuing Policies (2)

    When new packet arrives on buffer and buffer
    is full:
   Droptail
     drop it if buffer is full
   Drophead
     drop the oldest packet in buffer (most hops or least
      time to TTL expiration)
     rational(?): large time in the network => little chance
      to be delivered before TTL expires
   Drophead-sp (source-prioritized)
     Don’t drop a source packet for an arriving relay
       Queuing Policies: Performance

                                 buffer   droptail   drophead

                                   5       0.97       0.22       0.05

                                  10       0.95       0.03        0.0

                                  20       0.90       0.002       0.0

   Drophead: fast infenction, high packet loss for small
   Drophead-sp: slower infenction, higher delivery ratio
                  QoS Provision

   Multi-type traffic: what about traffic of different
    priorities (e.g. emergency messages vs.
   Multiple queues? Different forwarding policies
     E.g. never drop type A for type B

   Different routing policies?
    Reducing the overhead of epidemic:
             Network Coding
   So far we were not changing packets’ content
     Replication
     Forwarding
     Drops
   Coding may combine one or more packets

                         x1   x3               x3
                              x2               x2
        Incoming links        x1               x1

                              Outgoing links
    Reducing the overhead of epidemic:
             Network Coding
   So far we were not changing packets’ content
     Replication
     Forwarding
     Drops
   Coding may combine one or more packets

        Incoming links          x1              f(x1,x2,x3)

                               Outgoing links

                         Network Coding
      Coding Packets: A simple example

   XOR: The simplest combination:   f(x 1 , x 2 )  x1  x 2

msg x1:           1    0       1     1

msg x2:           0    1       1     0

f(x1,x2):         1    1       0     1
            De-coding Packets: A simple
   Assume node that send x1 receives the coded packet

msg x1:           1     0       1   1

f(x1,x2):         1     1       0   1

msg x2:           0     1       1   0
          Butterfly Network: Store-and-
      Two sources: S1, S2
      R1,R2: receive traffic from both S1 and S2
                    x1             x2
              S1                   S2
                         x1                       Time 1
                                                  Time 2
                              x2                  Time 3
                                                  Time 4
               x2                       x1
               x1                       x2
4 units:       R1                  R2        3 units:
received x1,x2                               received x1,x2
       Butterfly Network: Network Coding

      Two sources: S1, S2
      R1,R2: receive traffic from both S1 and S2

                     x1                x2
               S1                      S2
                            x1                         Time 1
                                                       Time 2
                            x1  x 2                   Time 3

                x1  x 2                    x1  x 2
                x1                          x2
3 units:       R1                      R2        3 units:
received x1,x2                                   received x1,x2
        Network Coding for Wireless

   Broadcast nature of medium: natural ground for
    network coding

            x2A         Ax2          Bx1
           Bx           Bx           Ax
             1            1            2

          A           C              B

              No coding: delay = 4
        Network Coding for Wireless

   Broadcast nature of medium: natural ground for
    network coding

               x1  x 2   A x2           x1  x 2
           B   x1         B x x
                            x11      A   x2

          A               C          B

              Coding: delay = 3
             Linear Network Coding

   m packets
   n linear combinations
              b1 = a11x1+ a12x2+…+ a1mxm
              b2 = a21x1+ a22x2+…+ a2mxm
              bn = an1x1+ an2x2+…+ anmxm

   independent linear combinations ≥ m
     Centralized choice of coefficients => Decode!
   Distributed) ai random and independent
                    => decode (prob 1)
     Network Coding for Challenged Nets
                The model
   Set of nodes V
   N(u): {iV: i neighbor of u}
   Set of sources S  V (m = |S|)
   Messages: xi, i=1,…,m
   xi = [xi1, xi2,…, xiM], M symbols F2k = (0,2k-1)
     K > 8 to ensure independence for random coding
   Encoding vectors: gi = [gi1, gi2,…, gim],
     m symbols F2k
 Encoding matrix G:                              m
                      m            m
row i = (gi1,…,,gim |  gij x j1 ,  gij x j2 ,…,  gij x jM )
                         j 1         j 1            j 1

           Encoding             gi*Gi (Gi= ith symbols of all xi}
             Encoding Matrix: Example

  Encoding matrix G at node 1
  m = 3 messages in total
  Each message contains M = 4 symbols in F8

g1=[1,0,0]    1        0        0   5         4        1       2
g2=[1,1,0]    1        1        1   6         3        2       2
               -       -        -   -         -        -       -

             Encoding vectors (2)       M = 4 (symbols per message)
              Encoding Matrix: Example

   Encoding matrix G at node 1
   m = 3 messages in total
   Each message contains M = 4 symbols in F8

 g1=[1,0,0]    1       0       0      5       4      1     2
 g2=[1,1,0]    1       1       1      6       3      2     2
 g2=[0,1,1]    0       1       1      3       7      3     4

                   New encoded message arrived: increase
                   rank of matrix G?

No! Linearly dependent with 1,2 (x3 = x1 XOR x2 (mod 8))
              Encoding Matrix: Example

   Encoding matrix G at node 1
   m = 3 messages in total
   Each message contains M = 4 symbols in F8

 g1=[1,0,0]    1       0       0      5       4      1     2
 g2=[1,1,0]    1       1       1      6       3      2     2
 g2=[1,0,1]    1       0       1      2       4      1     0

                   New encoded message arrived: increase
                   rank of matrix G?

Yes! 3 linearly dependent vectors (Gaussian elimination)
    Network Coding for Challenged Nets:
   At time t-dt node i receives an innovative
   With probability d: send (gi(t),yi(t)) = ri(t)*Gi(t)
     ri(t) = random vector (in F2k)
     Like gossiping: instead of forwarding new message,
      forward a linear combination of all messages
      currently in buffer!
   All nodes in N(i) receive (gi(t),yi(t))
     If not innovative discard
     If innovative, add to matrix G and do same process
   Need at most m innovative messages to decode
     Can probably decode some elements before that!
      Performance of Network Coding

   Increase Delivery Ratio: better utilize forwarding
   Increase average delay (have to wait for multiple
    messages to be received
        Generation Management:
    Which messages to code together?
   Assume infinitely large network with a
    percentage of nodes being sources

Do we code messages from all sources??
Coding matrix G will be huge!
Delay until all messages decoded  

   Code messages of subsets of sources together
     How do we choose subsets??
   Code multiple messages of same source
     How many generations??
              Network Coding Gains

   Generation management: Larger generations
     Better coding gains (throughput, energy, delivery)
     Larger potential end-to-end delay, complexity
   Related nodes in same generation?

   Types of traffic
       Multiple single-source single-destination messages
       One source-one destination, multiple messages
       Many sources-one destination
       Multiple one source-many destinations messages
        (multicast, broadcast)
  End-to-end vs. hop-by-hop decoding

1) Decoding of messages at end nodes
     This is what we were looking at so far
     Issues with generation management
     Potentially long/unbounded delays
2) Opportunistic Network (De-)Coding
     Keep track of neighbors messages
     Code only if next hop can decode
                           x3          x1
                           x2          x3
               x2 1,x2,x3) x1
               f(x                     x2
                Erasure Coding

   Provide better fault-tolerance by adding
    redundancy without the overhead of strict
    replication (e.g., Reed-Solomon, Gallager,
    Tornado, and IRA codes)

   Applications: P2P, overlay routing, WSN, data
    storage, etc.
                         Erasure Coding

    (r=2, n=4)

          A                     B                   C                D

A-1           A-3       B-1         B-3       C-1       C-3       D-1     D-3
  A-2           A-4       B-2         B-4       C-2       C-4       D-2     D-4

                                    Lossy Channel

    A-1         A-3       B-1         B-3       C-1                 D-1
      A-2         A-4       B-2                             C-4

          A                 B                       C               D
Layered Multiple Description Coding (LMDC)

   Layered coding

   Unequal erasure coding
LMDC Examples

   Video
   Web Document
       Transport Layer Issues in DTNs

TCP offers:
 Ports
     Still used by the overlay bundle layer
   Sequencing
     Still there, but for bundles
   Connection
     Impossible in most DTN cases
   Reliability
     Late ACKs. Large RTT.
   Congestion Control
     Very difficult to get up-to-date congestion info in partitioned
               Reliability in DTNs:
   Each message copy forwarded is
    acknowledged by the next hop
   This holds also if multiple message copies are
    propagated (e.g. epidemic)

   Hop-by-hop reliability has minimum delay
     No need to wait for end-to-end ack
   BUT: Hop-by-hop reliability does not
    guarantee end-to-end reliability
              Reliability in DTNs:
               “Active Receipt”
   Intermediate node may: lie, shut down, break
   Active receipt: generated by destination when it
    receives the message
   Active receipt = new message
     Other nodes route it as a normal message
   Epidemic spreading of receipt to guarantee
     ACK size < MSG size => less overhead
     Vaccinates/Cures other nodes encountered in the
      meantime (essentially VACCINE)
                Reliability in DTNs:
                “Passive Receipt”
   Active receipt: floods two messages
     Often, most overhead is MAC access
  “Passive Receipt”:
- generated by destination when it receives the
- can only be passed to infected nodes (essentially

   Plus: less overhead than active receipts
   Minus: larger delay than active receipts
              Reliability in DTNs:
           “Network-Bridged Receipt”
Assume complementary network:
DTN + (low bandwidth, connected network)
Cellular network

DTN network: send bulky data (with delay tolerance;
 e.g. ftp)
Cellular network: send immediate small ACK
     Could even be used for disinfection(?)
              Reliability in DTNs

   What else could we try?

   Where is each approach applicable?

   What is the penalty of late ACKs?
     What about ACKing multiple messages

   Can we take advantage of mobility/social
    structure to improve?
 Congestion Control in DTNs

             Connected Network

Cut back send rate!

        D             D          D

                                     Message Drop!
        S                      D

                          Buffer Full
                   Congestion Control in DTNs

                        Disconnected Network

               D              D
               D              D
               D              D
                                                          Message Drop!
Cut back send rate!
           D                  D

  Irrelevant Notification!
  Unnecessarily reduce
                                                 Buffer Full
                                                 No Congestion!
  May not see S
Mobility Models
                       Random Walk
    All nodes perform independent random walks
    Move to any neighboring location with probability ¼

                               p = 0.25

   Uniform stationary distribution
     torus: on boundary reflect on other side
   Brownian Motion as an extension
     Normal distribution increments
                Random Waypoint

   Choose a point in the network uniformly
     Choose speed randomly
   Pause for a random amount of time
   Choose another point uniformly and repeat

                   Random Direction

   Random Waypoint has some problems
       Non uniform stationary distribution: concentration in
       If not started from stationary distribution =>
        convergence issues: slowly drifting from uniform to

   Random Direction
    1. Choose direction uniformly in 360o
    2. Move for exponential amount of time
    3. Reflect or turn-around on boundary
    Uniform Stationary Distribution
                         Other Models

   Manhattan Model
     All nodes move within restricted street
     Grid structure (vertical and horizontal
      streets, like Manhattan)
     Stop lights?
   Freeway Model
     Nodes move on lanes of one line; lanes
      in both directions
     Potentially other crossing freeways
     Speed considerations between nodes in
      same lane
   Group Mobility
     Subset of nodes associated with a leader
     Followers make move based on leader’s
         Impact of Mobility Model on
   A study comparison between DSR, AODV,
    TORA, and DSDV under Random Waypoint
     All routing protocols (proactive and reactive)
Showed DSR was better overall

Comparison for different mobility models (Rand.
 Waypoint, Freeway, Manhattan, etc.)
Winner depends on mobility model; AODV
 actually better in more cases
       Some Common Assumption of
         Synthetic Mobility Models
   No location preference
     Uniform choice of destination
     Uniform stationary distribution

   IID node mobility
     Every node is doing the same
     Statistically equivalent
                           Real-life Mobility

 Base Stations (pstatic)

                               Roam around network
                                                                            stay inside
                                                             1-pL(i)        community


                                                       Community (local) Nodes
Fast/Mobile Nodes (pfast)
           Common Mobility Models:
              What is Wrong?
   Location preference?
     Nodes don’t visit all locations equally frequently
     Usually: spent most of the time in a small subset of locations (e.g.
      office, house, library, etc.)
   Identical node behavior?
     Different nodes; some more mobile than others
     Vehicles vs. pedestrians; first-year student vs. graduate student
   Does time play any role?
     Morning: commute to work
     Noon: lunch
     Weekend-vs-week
   What else?
     Social relationships
    Traces From Real Wireless Networks

   WLAN (WiFi) traces
     Collect logs from deployed WLANs in campuses
     Association(s) between user node and Access
      Point(s) (AP)

   Traces of contacts between different wireless
    nodes (ad hoc mode)
     PDAs carried around by users
     Logs of different encounters (e.g. PDA associations)
       DTN: We Care About Contacts

   Contact traces => we get this directly

   WLAN traces: translate Node-AP associations
    into Node-Node associations
     Same AP at the same time => contact
     Not always true
     What happens between APs?
     Public DTN Traces
   ZebraNet
   Bus trace (SF, Toronto, DieselNet)
   Campus trace (UCSD, Dartmouth, MIT)
   Conference trace (Infocom, SIGCOMM)
   Enterprise trace (Intel, IBM)
      Traces: What Have We Learned?

   Location/Node preference
     Tend to see specific locations/nodes, more often
      than other
   Node Heterogeneity
     Some nodes see all locations/nodes; others a small
   Behavior over time
     Different for different time of day, day of week, etc.
     Periodic behavior
            Community-based Mobility

   Capture Location Preference

                                                    Rest of the network
           Roam outside community
           (Rand. Direction or Waypoint)
                                                                    stay inside
          pR(i)                                                     community
    Continue roaming            1-pR(i)                local        pL(i)

                                              Community (e.g. house, campus)
        Community-based Mobility (2)

   Capture Node Heterogeneity
   Each node may have a different community

     pL(i)               pR(i)   pL(j)              pR(j)
               1-pL(i)                    1-pL(j)

      local              roam    local              roam

              1-pR(i)                    1-pR(j)

              Node i                     Node j
        Community-based Mobility (3)

   Multiple Communities (house, office, library, cafeteria)

                                                    Rest of the network
         p23(i)             Office

                  Library                            House          p11(i)
                    C3                                (C1)

                                              Community (e.g. house, campus)
        Community-based Mobility (4)

   Multiple Communities (house, office, library, cafeteria)

                       p11(i)                p22(i)

                          C1                  C2

                                p21(i)                     p24(i)

                          C3                          C4


   Inter-Community Mobility?
   Intra-Community Mobility?
       Community-based Mobility (5)

   Capture time-dependent behavior
   t = {morning, noon, weekend,…}
                p11(i)(t)                p22(i)(t)

                      C1                     C2

                            p21(i)(t)                  p24(i)(t)

                      C3                          C4

                  Mobility Profile

   Macroscopic View of Mobility
   Node i: {π(i)(C1), π(i)(C2),…, π(i)(Cn)}
   Approach 1: Route towards most popular
    communities (e.g. geographic routing)
   Approach 2: {π(i)(C1), π(i)(C2),…, π(i)(Cn)} =
    coordinates in an n-dimensional space
    Route to nodes whose distance is small in
    this n-dimensional space
            Multi-tiered Community

   Roaming outside local community is not uniform either!
   Move further away from local community with
    decreasing probability

                 Tier 4

                          Tier 3

                                       Tier 2

                          p13(i)(t)               Tier 1

              Inter-contact Times

   Time between subsequent encounters with the
    same node
   Consecutive transmission opportunities to a
    given node
   Contact-based trace measurements: what is
    the distribution of inter-contact times?
     WLAN traces (Dartmouth, UCSD)
     Inter-node (ad hoc mode) traces (Cambridge,
        CCDF for Inter-contact Times

   LOG-LOG plots
   Straight line in log-log plot => power law/heavy-tailed
    (slope = exponent)
      CCDF for Inter-contact Times (2)

   WLAN traces: similar behavior
            Power Law Distributions

   P[X > x] = x-a
   Infinite variance
   a < 2: infinite mean
   There is a high probability that some very large
    values will be drawn if X is sampled sequentially
   Contrast: exponential decay variables
     Very large values: almost improbable

       Most of the mobility models (synthetic)
        presented so far had exponential tails
          Power Law Distributions:
   Theory: most analysis (Markov, ODEs,
    combinatorics) assumes exponential tail
     Essentially for X1,X2,…,Xn IID and exponential
     E[min{X1,X2,…,Xn}] = EX / n

   Protocol Performance
     Opportunistic routing: give a copy randomly
     Depending on the exponent (a) any opportunistic
      protocol (e.g. direct transmission, 2-hop,
      spray&wait) may have infinite delay!
         But is it REALLY Heavy-tailed?

   Power-law only within a range of CCDF
   What about the rest of the tail (artifact of experiments, or not
    power-law really)?
Lognormal Seems Fit Better
Inevitable Censorship in Measurements

   UCSD trace           P(T>t)           Survival Curve

                                 5x10^3   6x10^4       6.6x10^6

         censored data
               Self-Similarity Test

   Hurst values are located between [0.5,1]
     Time-Variance Plot, R/S Plot, Periodogram Plot, Whittle
Social Networks
               Social Networks

   Social Network: who interacts with whom? Who
    is a “friend” of whom?
   Graph model: Vertices = humans, Weighted
    Edges = strength of interaction
     Social Network-based Mobility
1. Create (simulation) or Derive (from existing
   info – e.g. department affiliation) a social
   network among all nodes
2. Assign nodes to communities according to
   social network
3. Assign communities to locations
4. Induce mobility based on social network
        Communities in Social Networks

   Social networks have high clustering co-efficient
   Interaction Matrix = Connectivity Matrix
     For all weights > threshold => assume a connected link
              Community 1     Community 2   Community 3

   Identify Communities: Find nodes that connect
    communities (intuition: shortest paths go through these)
        Communities in Social Networks

   Social networks have high clustering co-efficient
   Interaction Matrix = Connectivity Matrix
     For all weights > threshold => assume a connected link
              Community 1     Community 2

B: connects 1,2

   Identify Communities: Find nodes that connect
    communities (intuition: shortest paths go through these)
            Mapping Communities to
   Assume a grid with different locations of interest
     Geographic consideration might gives us the candidate locations
        Mobility Between Communities
    pc(i) = attraction of node i to community/location c




pC 
        {j  C}
    Social Network-based Mobility Model

   Can reproduce similar behavior to (heavy-tailed)
     Inter-contact times
     Contact durations
   Some issues
     Nodes move only between specific (community)
     Different social graph weights depending on time of
     Evolve social graph weights
       Social Networks for Information
   Social networks are often better to find information that is
    location, community, or time-specific!
   Small World and Scale-Free properties
     Separation/diameter is smaller than random networks
   Query can often be answered quicker through peers
     Example: “where is a good Thai restaurant in Nice?”
   Approach 1: Find PC => Google => look websites that
    rate restaurant => hope the one suggested IS actually
   Approach 2: Ask friend who lives in Nice (he might now,
    or have heard, or ask another friend)
     What if we could do this wirelessly also?
            PeopleNet Architecture

   Cellular Networks (WiMaX) as main
   Bluetooth peer-to-peer networks (WiFi – ad hoc)
   Users transmit querys
     Request query: “who knows/has X?” (ticket to
      Monaco rally)
     Offer query: “I have/know Y”
   Queries are tagged according to some subject
    (e.g. sports, finance, news, etc.)
          PeopleNet Architecture (2)

   A query is sent to a subset of locations/base
    stations that have been assigned to the given
    query type
     Geography might play a role: e.g. “where is the
      closest local bookstore?”
   A few users receive the query through
    infrastructure, and propagate further using
    peer-to-peer messages
   If a “match” is found, requesting user is notified
    (SMS, email)
Further Issues
                    Research Issues

   Routing
   Buffer Management
   Power Management
   Auto-Configuration
   Network Reliability
     Free-riders
     Black holes
     Worm holes
   Information Security
     Data Encryption
   Real-world applications (and killer applications)
     Underwater Networks, Vehicular Networks, People Networks,
      Scientific Monitoring Networks, etc.

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