4. Communication channels can be very
nasty!
Channel distortion, noise, interference……
How do we communicate reliably over such channels?
5. Communication has a
long history
• Smoke signals,
telegraph, telephone…
• 1895: invention of the
radio by Marconi
• 1901: trans-atlantic
communication
6. State of affairs:
Early 20th century
• Most communication systems are
analog.
• Engineering designs are ad-hoc, tailored
for each specific application.
7. Big Questions
• Is there a general methodology for
designing communication systems?
• Is there a limit to how fast one can
communicate?
8. Harry Nyquist (1928)
• Analog signals of bandwidth
W can be represented by 2W
samples/s
• Channels of bandwidth W
support transmission of 2W
symbols/s
9. From CT to DT
Nyquist converted the continuous-time
problem to a discrete-time problem.
But has he really solved the communication
problem?
No. You can communicate infinite number of
bits in one continuous-valued symbol!
11. Randomness
Shannon thought of both information
sources and channels as random and
used probability models for them.
encoder
source channel decoder
12. Everything is bits
Shannon showed the universality of a
digital interface between the source and
the channel.
source Source
Encoder
Channel
encoder
channel
Bit stream
13. Information is like fluid
• Every source has an entropy rate H bits
per second.
• Every channel has a capacity C bits per
second
• Reliable communication is possible if
and only if H < C.
15. Initial Reactions
Engineers didn’t understand what he was
talking about.
• People were still stuck in the analog world.
• Complexity way too high for implementation
technology of the day.
• He didn’t really tell people exactly how to
design optimal communication systems.
16. 50 years later….
• Our communication infrastructure is
going fully digital.
• Most modern communication systems
are designed according to the principles
laid down by Shannon.
Internet
S
D
19. Mobility in Embedded Networks
People and
their stuff
Transportation systems
(e.g., cars)
Environmental and wildlife monitoring
(e.g., Princeton ZebraNet Wildlife Tracker)
Inventory and supply chain
management (e.g., RFID tags)
20.
21. Ubiquitous Mobility
• All nodes potentially move
– Network topology changes with time
• Efficient routes require knowledge of topology
– Control traffic: distance vector or link state
updates, flooded discovery packets, …
22. Shanno
• Scaling to large networks?
– How costly is the dissemination of enough
information to allow for “reasonably good routes”?
– Does control traffic grow more quickly than
capacity of the network?
23. Position-based Routing
• Position-based routing:
– Geographic coordinates rather than graph to
make routing decisions
• Local routing decisions based on positions
of destination and neighbors
• Separation into
– Location service: where is the destination?
– Local routing protocol: select next hop
towards destination
25. Location Services
• Challenge: construct a distributed database out
of mobile nodes
• Approaches:
– Virtual Home Region: hash destination id to
geographic region -> rendez-vous point for source
and dest (Giordano & Hamdi, EPFL tech. report,
1999)
– Grid Location Service: quad-tree hierarchy, proximity
in hashed id space (Li et al., Mobicom 2000)
– DREAM: Distance Routing Effect Algorithm (Basagni
& Chlamtac & Syrotiuk, Mobicom 1998)
26. Last Encounter History
• Question:
– Do we really need a location service?
• Answer:
– No (well, at least not always)
• Observation:
– Only information on network topology available for free at a
node is local connectivity to neighboring nodes
– But there is more: history of this local connectivity!
• Claim:
– Collection of last encounter histories at network nodes
contain enough information about current topology to
efficiently route packets
27. Last Encounter Routing
• Can we efficiently route a packet from a source to a
destination based only on LE information, in a large
network with n nodes?
• Assumptions:
– Dense encounters: O(n^2) pairs of nodes have encountered each
other at least once
– Time-scale separation: packet transmission (ms) << topology
change (minutes, hours, days)
– Memory is cheap (O(n) per node)
• Basic idea:
– Packet carries with it: location and age of best (most recent)
encounter it has seen so far
– Routing: packet consults entries for its destination along the way,
“zeroes in” on destination
28. Definition: Last Encounter Table
A
B
encounter at X
between A and B
at t=10
B: loc=X, time=10
C: ...
A: loc=X, time=10
C: ...
D: ...
X
33. EASE: Searching for Messenger Node
time
-T
0
-T/2
Search: who has seen
dest at most T/2 secs ago?
34. EASE: Forwarding the Packet
time
-T
0
-T/2
Forwarding towards new position
with T:=new encounter age
35. EASE: Sample Route
Def:
anchor point of
age T = pos. of
dest. T sec ago
EASE:
- ring search nodes
until new anchor
point of age less
than T/2 is found
- go there and
repeat with T=new
age
src
dst
36. Performance of EASE
• Length of routes clearly depends on
mobility process
– Cannot work without locality
– Counterexample: i.i.d. node positions every
time step
• Model:
– 2-D lattice, N points, fixed density of nodes
– Each node knows its own position
– Independent random walks of nodes on lattice
• Cost = forwarding cost + search cost
37. Cost of EASE Routes
• Claim:
– The asymptotic expected cost
for large N of EASE routes is
on the order of shortest
route, i.e., total forwarding
cost is O(shortest path):
• Forwarding cost:
– Geometric series of ages ->
geometric series of EASE
segments
– Total length = O(shortest
path)
38. Search Cost
• Single step search cost is small
compared to forwarding cost:
– Show that density of messenger nodes
around current anchor point is high
– Depends on:
• Number of unique messenger nodes
encountered by destination = O(log T)
• Distance traveled by messenger nodes
= same order as destination
39. Interpretation: Distance Effect
and Mobility Diffusion
• Observation: required precision of destination’s location can
decrease with distance
– DREAM algorithm: exploit distance effect to decrease state
maintenance overhead
– When a node moves by d meters, inform other nodes in disk of radius
c*d meters
– Relax separation of location service and routing service
• Basic idea behind last encounter routing:
– Exploit mobility of other nodes to diffuse estimate of destination’s
location “for free”
– Concurrently for all nodes
destination
46. Simulation: Pareto Random Walk
•N nodes
•Positions i.i.d.
•Increments i.i.d.,
heavy-tailed distance
distribution
47. Simulation: Random Waypoint
•N nodes
•Positions i.i.d.
•Every node has a
waypoint
•Moves straight towards
waypoint at constant
speed
•When reached, new
waypoint selected
uniformly over area
49. Related Idea:
Last Encounter Flooding
• With coordinate system
– Last-encounter information: time + place
– EASE/GREASE algorithms
• Blind, no coordinate system
– Last-encounter information: time only
– FRESH algorithm: flood to next anchor point
– Henri Dubois-Ferrière & MG & Martin Vetterli,
MOBIHOC 03
51. Summary: Last Encounter Routing
• Last Encounter Routing uses position information that is
diffused for free by node mobility
– Last encounter history: noisy view of network topology
– Packet successively refines position estimate as it moves towards
destination
– Mobility creates uncertainty, but also provides the means to diffuse new
information
• No explicit location service, no transmission overhead to update
state!
– Only control traffic is local “hello” messages
– At least for some classes of node mobility, routes are efficient!
– Key ingredients: locality, homogeneity, mixing of trajectories
• Rich area for more research:
– Prediction
– Integration with explicit location services & routing protocols
– More realistic mobility models
• Ref: MG & Martin Vetterli, IEEE INFOCOM 03
