Scalable localized routing in wireless sensor networks

1
Scalable localized routing in wireless sensor
networks
Tutorial

• Ivan Stojmenovic
• Ivan_at_site.uottawa.ca
• www.site.uottawa.ca/ivan

2
Sensors route reports to a fixed sink
Internet
humidity
Sink
End user
3
Multi-hop networks Routing
Unit graphs radius
Sensor networks Position information

• Routing source ? destination

4
Routing with/out position information ?

• Sensors can function efficiently only with
  position information GPS and location estimation
  advanced rapidly (cubic cm sensor with 7mm x
  7mm x 2mm GPS)
• Sink can flood network with/out its own position
• Routes can be learn while flooding, or
• Only position of sink is learned and used

5
Proactive routing ad hoc networks

• Routing table contains the first hop/neighbor
  toward each destination
• Bellman-Ford Each node exchanges its routing
  tables with all its neighbors, and
• Best neighbors N for route from S to D is one
  that minimizes cost of link S to N cost N to D
  (from routing table in N)
• OLSR (Optimized Link State Routing) link changes
  are flooded Dijkstras shortest path
• MPR (MultiPoint Relay) to reduce flooding

6
Reactive routing ad hoc networks

• Source floods route discovery (short) message
• Destination node replies back to source upon
  receiving discovery message(s) using memorized
  hops (AODV) or paths (DSR)
• Source sends full message using recorded path
• Multi-paths for QoS
• Route discovery message may contain accumulated
  delay, congestion, power, cost etc. along paths
  best path selected at destination
• Local route maintenance expanding ring search

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Route discovery by flooding
Each sensor retransmits once Problem sink stable
but sensors may sleep
DSR, AODV in ad hoc networks, position info not
needed directed diffusion for sensors
Intanagonwiawat, Govindan, Estrin 2000
8
Directed diffusion

• Monitoring center broadcasts packet to all
  sensors in a region
• Sensors create links for reporting along reverse
  broadcast tree
• Link is toward sensor from which the first copy
  of packet is received

9
Greedy position based localized routing
Localized protocol S knows only position of
itself, its neighbors and destination D S
forwards to neighbor B closest to D Finn 1987
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Greedy SABCD vs shortest path SECD
S
A
D
B
C
E
Localized vs. globalized protocol SP Overhead
messages to maintain global information at each
node following mobility and/or sleep/active
periods changes
11
Greedy is loop-free
An
A1
A2
An-1
D
A3
Assume A1 closest to D A2 sends to A3
contradiction, A1 is closer
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Progress based routing 84-86.
A
B
C
D
A
S
E
F
MFR Choose closest projection on SD minimize
SA.SD
13
MFR is loop-free
An
A1
A2
An-1
D
A3
A1 ? A2
Proof by Stojmenovic, Lin 1998
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Greedy vs. MFR
B
A
D
S
A
B
may choose different node ADltBD choice is same
most of time! Similar performance
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DIRectional routing methods
Basagni, Chlamtac, Syrotiuk, Woodward MOBICOM98
(DREAM) Ko, Vaidya MOBICOM 98 (LAR) Kranakis,
Singh, Urrutia CCCG99 (compass routing)
A
D
S
Closest direction
Send to all neighbors within angular range from
direction BCSW,KV location update schemes
BCSW, KV
Flooding rate ( of messages vs SP) ??
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DIR is not loop-free !
E
H
D
F
G
Transmission radius
Stojmenovic 1998
Greedy and MFR are loop free
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Performance evaluation

• Random unit graphs Choose n nodes at random
  in 0,mx0,m
• select average node degree d 2,3,4,5,
• sort all (n-1)n/2 edges in increasing order
• Radius R nd/2-th edge in sorted order!
• Reject graph if disconnected

Success rate high for high degree, low for low
degree hop count successful Greedy/MFR close to
SP, DIR gt flooding rate (messages vs SP) close
to SP Independent variable is d, not R !!!
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Is hop count the best metric ?

• Power consumption
• Reluctance (avoiding nodes with low energy)
• Power_reluctance
• Delay
• Expected hop count (realistic physical layer)
• COST - selected metric

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Cost to progress ratio framework

• Progress measures advance toward destination
• Progress SD-ADd-a
• Select neighbor A that minimizes
  cost(SA)/progress(A)
• Hop count cost1
• ?Maximize advance

A
r
a
Stojmenovic IEEE Network 2006
D
S
d
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Parameterless behavior

• Cost-to-progress ratio framework has no added
  parameters such as thresholds
• Threshold based approach eliminate bad links,
  drop packet if there is no good neighbor
• What if a solid path has just one weak bridge?
• Experiments so far indicate that threshold based
  approaches are inferior for all threshold values
  - either high failure rate or suboptimal since
  there is no notion of best neighbor

21
Power saving localized routing
Constant power ? minimize hop count power u(d)
d? c ? minimize total power ????? Many
articles assume c0 in practice cgt0 since power
is needed to run hardware at each node, and
correct reception requires minimal transmission
power (no energy free transmission at zero
distance) reluctance f(A) to forward packets
1/g(A) g(A) in 0,1 lifetime ? minimize
total cost Power_reluctance f(A)u(d)
model by Rodoplu, Meng 1999
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Ideal and localized power aware routing

• of hops n ? d(a(? -1)/c)1/?
• minimal power v(d) dc(a(?-1)/c)1/?
  da(a(? -1)/c)(1-?)/? O(d)
• A minimizes u(r) v(s) among neighbors of S

Stojmenovic, Lin 1998
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Localized power aware routing

• Kuruvila, Nayak, Stojmenovic 2004
• Power progress minimize (r?c)/(d-a)
• Iterative power progress select B if
  power(SB)power(BA) lt power(SA)
• (Iterative) Projection power progress

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Reluctance routing algorithm
Stojmenovic, Lin 1998 Rediscovered by Yu,
Govindan, Estrin GEAR, TR-01-0023, Aug. 2001.
A
f(A) reluctance 1/g(A) g(A) in 0,1
lifetime A neighbors of S that minimizes f(A)
f(S)s/R ( cost of A average cost around S
ideal number of hops from A to D) If D is
neighbor of S then deliver to D else forward to
A Reluctance/progress minimize
f(A)/(SD-SA) Kuruvila, Nayak, Stojmenovic
2004 (no added parameters)
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Power_reluctance routing
Stojmenovic, Lin 1998
A neighbors of S that minimizes u(r) v(s) If
D is neighbor of S and u(d) lt min u(r) v(s)
then deliver to D else A neighbor of S that
minimizes f(A)u(r) v(s)f(S) forward to A
Powerreluctance/progress minimize
f(A)power(SA)/(SD-SA) Kuruvila, Nayak,
Stojmenovic 2004 (no added parameters)
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Physical layer impact

• Expected hop count (counting all transmissions
  and possibly acknowledgements)
• F(SA) expected hop count from S to A
• Minimize F(SA)/(d-a)
• Kuruvila, Nayak, Stojmenovic 2004
• Delay
• QoS routing
• Bitrate

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Physical layer impact
Lognormal shadowing model

• 4
• p(R)0.5

Packet reception probability
Unit graph model Prp(x)1, x?R Prp(x)0, xgtR
R
Distance between nodes What is the transmission
radius ? Who are neighbors?
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Simulation dilemma

• Home-made simulator or one used by others (NS-2,
  Qualnet, J-sim,)?
• Greedy routing uses hop count as measure
• NS-2 applies realistic physical layer, which
  mostly penalizes long hops ?
• Why to use simulator that defeats the model,
  hides physical models and parameters which impact
  the data, impact comparison, and provide no
  explanation?
• Solution build protocols and simulators in
  parallel, so that results can be explained and
  protocols improved ?
• Network layer protocol need to be designed with
  more realistic physical layer, not with unit disk
  graph model

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How to simulate ?

• Study one variable at a time, explain it fully
• Ideal MAC, no congestion, for initial studies
• If one routing A is on average better than one
  routing B, it should cause less congestion, thus
  show even more advantage at the transport layer
• Simulation to match ideal assumptions
• Stable graphs first localized design takes care
  of dynamics
• Independent variable is one that matters e.g.
  density (average number of neighbors per node),
  not transmission radius
• Compare against the best (e.g. shortest path),
  not against worst (e.g. flooding)

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Approximate packet reception probability
p(x) ? 1-(x/R)q?/2 for x lt R ?
(2-x/R)q?/2 for 2R ? x ? R q depends on L, packet
length, 2 ? ? ? 6

• Signal strength is a random variable, and
  deviation cannot be predicted in advance (but
  some articles use it to select best neighbors)
• Transmission power is assumed fixed and same
• q1 for L1 q?2 for L120.
• Exact formula complex, time consuming and
  unreliable
• each bit is received or not independently (no
  coding) packet received correctly iff all bits
  received

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Reactive routing with physical layer

• In route discovery phase, forward the sum of
  Expected Hop Counts along partial route, or
• Wait retransmission proportional to EHC on link
• Problems
• A single retransmission by a given node may not
  reach the best forwarding neighbor tradeoff of
  retransmissions and gains made
• Real traffic may not use routes created by
  control traffic different packet lengths, or
  low packet reception probability

32
Hello messages with physical layer

• fixed hello protocol
• Send hello messages fixed number of times, to
  increase the probability of reception by
  neighbors
• variable hello protocol
• Send hello packets until sufficient number of
  such packets from neighbors received (learn
  enough neighbors for desired density)
• Goel, Kalaichelvan, Nayak, Stojmenovic,
  Villanueva-Pena 2006

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Greedy routing is not hop count optimal

• Ideal routing
• Place additional nodes between Source and
  Destination as required.
• Ideal Hop count computed for different u and ?
  values
• Each received packet is acknowledged u times
• Low values for 0.6R?x ? 0.9R u1
• 50 higher at xR, very high xgtR or xlt0.1R

• Kuruvila, Nayak, Stojmenovic 2004

34
IHC for Different u Values (?2)
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Expected progress routing
a
A
x
c
D
Hop by hop ack
C
Progress c-a Expected hop count for u1 f(x,1)
1/p2(x)1/p(x) Best value of u
u?1/p(x) Forward to neighbor (closer to
destination) that maximizes (c-a)/f(x,1) (EPR-1)
or (c-a)/f(x,u) (EPR-u)
36
tR Greedy Algorithm

• The redefined notion of greedy routing.
• Current node S selects neighbor closest to D
  among all neighbors that are closer to D than
  itself, and which are at distance at most tR from
  S, for forwarding the message.
• Experiments for t 1, 1.25 and 1.4377
• Threshold based greedy routing

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Performance summary

• Good performance for localized parameterless
  algorithms
• low hop counts for dense networks and ?100
  success rates
• tR-greedy are significantly inferior a
  choice of long edge is quite likely on a route
  which then contributes to very high expected hop
  count measure, or
• optimistic parameter choice fails traffic
  unnecessarily

38
Loop-free with guaranteed delivery

• Stop if message is to be returned to neighbor it
  came from concave node
• MFR, DIR, Greedy
• Flooding Greedy, Flooding MFR
• Concave nodes flood message to all neighbors and
  then reject further copies of the same message
• Loop-free methods that guarantee delivery,
  reasonable flooding rate
• But nodes memorize past traffic

Stojmenovic, Lin 1999
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Routing around void areas ?
A ?
S
Recovery, perimeter, face mode
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1. Constructing planar graph faces
Bose, Morin, Stojmenovic, Urrutia, 1999
D
A ?
S
Some planar graphs (Gabriel graph) can be
constructed without message exchange!
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2. Traverse proper face until recovery
Bose, Morin, Stojmenovic, Urrutia, 1999
D
S ?
B

• Select face containing SD
• Follow that face by left hand or right hand rule
• until recovery ( closer node reached)

C
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GFG Greedy-FACE-Greedy

• run Greedy until delivery or a failure node A,
  ADd,
• run FACE until delivery or B reached, BDltd,
• run Greedy
• paths close to SP for higher degrees,
• lt3.5 times longer than SP for low degrees
• No traffic memorization, localized, close to SP
  ? scalable !!
• Karp and Kung MOBICOM 2000 duplicated (with
  citation) GPSR GFG (added MAC, mobile nodes)

Bose, Morin, Stojmenovic, Urrutia, 1999
43
Gabriel graph
Gabriel, Sokal 1984
Gabriel graph GG(S) contains an edge (U,V) iff
the disk with diameter (U,V) contains no other
point from S distance from other points to
center of UV is gt UV/2 Acute angles for all
joint neighbors ? in GG GG(S) is planar and
connected (contains MST)
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Gabriel graph is planar
Planar graph no two edges intersect
Proof by contradiction Assume UV, PQ ? GG(S),
UV ? PQ
P
U
V
Q

• ?? PUQ lt ? /2, ? PVQ lt ? /2,
• UPV lt ? /2, ? UQV lt ? /2,
• ? Sum of angles in UPVQ lt 2?

45
Gabriel graph contains MST
P
Q
W
By contradiction Assume PQ ?MST, PQ ? GG ?
?W, PWltPQ, QWltPQ, PW? MST Replace PQ by PW in
MST ? new MST has smaller sum of edge lengths.
contradiction ? Gabriel graph connected
46
Unit (connected) graph contains MST
Kruskals algorithm to construct MST Sort all
edges by their length, from shortest to
longest. Consider each edge in that order for
inclusion in MST Include it in MST if its
addition does not create a cycle. Unit graph
edges considered before any other edge. After
their consideration, MST is already connected,
and no more edges can be added. ? GG(S) ? U(S)
planar and connected!
47
Traversal of selected face leads to recovery
D
E
X
S ?
F
B
C

• Line SD intersects the face in X on an edge EF
• E or F is closer to D than A (if nothing else
  found before)

48
Getting closer on the face is guaranteed for GG
E
?S lt ?/2, ?Dlt ?/2 since EF is in GG ? ?E gt ?/2 or
?F gt ?/2 ?F gt ?/2 ? SD gt FD ? F is closer to
D than S
Frey, Stojmenovic MOBICOM 2006
49
Conclusions

• Imprecise location information is challenge for
  georouting with guaranteed delivery
• Georouting in 3D has no guaranteed delivery
• Unit disk graph is required
• For planar graphs GFG still always works, but
  GPSR by Karp and Kung does not
• For other metrics, there is still no alternative
  to GG based face routing for recovery mode, which
  prefers close neighbors (except shortcuts,
  dominating sets..)

Frey, Stojmenovic 2006
50
Greedy, GFG (greedy-face-greedy)
J
G
U
K
L
D
A
V
F
W
B
I
E
C
H
51
Robustness of GFG

• GFG requires unit graph equal transmission
  radius, no obstacles, nodes in plane
• Extension for fuzzy unit graphs connected if
  distance lt r, nor connected if distance gtR, may
  or may not be connected otherwise, R/r lt
  1.41 Barriere, Fraigniaud, Narajanan, and
  Opatrny 2001
• Loop-free for static nodes loops can be created
  by mobile nodes but exit can be found by adding
  timestamp of the last intersection with imaginary
  line SD and ignoring links created afterwards

52
Shortcut procedure in FACE mode
Datta, Stojmenovic, Wu 2001
C
E
G
B
F
A
ABCE replaced by AF 2-hop information needed
53
Restricting FACE to a dominating set

• Paths in FACE mode may be quite long
• Reduce paths by restricting routes to a connected
  dominating set (CDS)
• Each node is either in CDS or neighbor of a node
  from CDS
• Localized maintenance of CDS preferred
• Dominating set status to be communicated, or
  2-hop information needed

Datta, Stojmenovic, Wu 2001
54
Beaconless greedy routing

• Füßler, Widmer, Käsemann, Mauve, Hartenstein 2003
• Heissenbüttel Braun 2003
• No hello messages
• S transmits packet containing position of
  destination
• Each receiving node sets timeout based on its
  distance to destination
• If a packet from a neighbor received while
  waiting, cancel retransmission
• Otherwise retransmit at end of timeout
• Details for reducing the of paths searched,
  e.g.
• Sender asks for help, and sends full message only
  to neighbor that responded first

55
Beaconless routing with guaranteed delivery

• In face mode, nodes respond to S based on
  distance to S, not distance to D
• Closer neighbors respond sooner
• In basic variant, all neighbors respond
  (optimizations possible)
• Witness node B for a non-GG edge SA responds
  before A since SBltSA, and that message is
  received by A
• Neighbors that discover witness cancel their
  Gabriel edge
• After learning Gabriel edges, apply face mode
  routing
• Chawla, Goel, Kalaichelvan, Nayak, Stojmenovic
  2006

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QoS routing

• Find a route which satisfies delay, bandwidth
  etc. QoS criteria
• Huang, Dai, Wu 2004
• Localized routing, maximizes progress/cost
• Progress advance on the projection to
  destination
• Cost from QoS criterion used
• Backward checking iterative improvement

Instead of routing to B, route to C if
cost(AC)cost(CB) lt cost (AB)
A
B
C
57
QoS DFS routing

• Depth First Search with Greedy to sort neighbors,
  and O(1) memory in each node, guarantee delivery
• bandwidth criterion edge elimination
• delay criterion hop count more bandwidth
• new connection time criterion
• Jain, Puri and Sengupta Stojmenovic, Russell,
  Vukojevic 1999
• Power and cost addition Vukojevic, Stojmenovic
  2005

58
Power/cost aware localized routing with
guaranteed delivery
Stojmenovic, Datta 2000

• PFP power-face-power
• run Power-aware until delivery or a failure node
  A, ADd,
• run FACE until delivery or B reached, BDltd,
• run Power-aware
• CFC Cost-FACE-Cost
• PcFPc PowerCost-FACE-PowerCost
• Competitive with respect to globalized solutions

59
Component routing
E
Concave nodes send packet to one neighbor in each
connected component of subgraph of
neighbors Parallel path search reduced
flooding greedy
F
P
A
Q
D
U
B
V
J
W
I
H
G
Lin, Lakhsdisi, Stojmenovic MobiHoc 2001
Routing from A to D reject nodes B forwards to
E,W from two neighbors connected components E
fails, W delivers
60
Assisted routing
Blazevic, Giordano, Le Boudec 2000