An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers.

1. Infrastructure-less
Wireless Networks
Gwendal Simon
Department of Computer Science
Institut Telecom
2009

2. Literature
Books include:
“Algorithms for sensor and ad hoc networks”,
D. Wagner and R. Wattenhofer
“Wireless sensor networks: an information
processing approach”, F. Zhao and L. Guibas
and journal/conferences include:
ACM SigMobile (MobiHoc, SenSys, etc.)
IEEE MASS and WCNC
Elsevier Ad-Hoc Network, Wireless Networks
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3. Motivations
Current wireless net. require an infrastructure:
cellular network: interconnected base stations
wifi Internet: an access point and Internet
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4. Motivations
Current wireless net. require an infrastructure:
cellular network: interconnected base stations
wifi Internet: an access point and Internet
Same flaws than centralized architectures:
cost
scalability
privacy
dependability
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5. Motivations
Sometimes, there is no infrastructure
transient meeting
disaster areas
military interventions
alter-communication
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6. Motivations
Sometimes, there is no infrastructure
transient meeting
disaster areas
military interventions
alter-communication
Sometimes not every station hear every other station
limited wireless transmission range
large-scale area
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7. Multi-hop Wireless Networks
Nodes: portable wireless devices
transmission ranges do not cover the area
density ensures network connectivity
Links: wireless characteristics
transmission model: local broadcasting
energy consumption: transmission is costly
Behavior: devices emit, receive and forward data
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8. A Taxonomy of
Applications
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9. Ad-Hoc vs. Sensor Networks
Ad-Hoc Networks Sensor Networks
nodes powerful wifi devices tiny zigbee nodes
algorithms all-to-all routing echo to sink
mobility human or car motions failures
performance criteria quality of service energy consumption
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10. Ad-Hoc Applications
Delay-Tolerant Network (social media application)
assumption: no connectivity, but high mobility
objective: ensuring eventual message delivery
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11. Ad-Hoc Applications
Delay-Tolerant Network (social media application)
assumption: no connectivity, but high mobility
objective: ensuring eventual message delivery
Mesh Networks (rural wireless coverage)
assumption: some nodes have Internet access
objective: maintaining path to these nodes
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12. Ad-Hoc Applications
Delay-Tolerant Network (social media application)
assumption: no connectivity, but high mobility
objective: ensuring eventual message delivery
Mesh Networks (rural wireless coverage)
assumption: some nodes have Internet access
objective: maintaining path to these nodes
Vehicular Ad-Hoc Networks
assumption: a particular mobility model
objective: mostly services related to car safety
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13. Sensor Network Applications
Sink-Based Networks (monitoring of natural areas)
assumption: one sink retrieves all sensed data
objective: increasing life-time
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14. Sensor Network Applications
Sink-Based Networks (monitoring of natural areas)
assumption: one sink retrieves all sensed data
objective: increasing life-time
Mobile Object Tracking (area surveillance)
assumption: sensors know their location
objective: determining hostile position
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15. Sensor Network Applications
Sink-Based Networks (monitoring of natural areas)
assumption: one sink retrieves all sensed data
objective: increasing life-time
Mobile Object Tracking (area surveillance)
assumption: sensors know their location
objective: determining hostile position
Multi-Sink Networks (intervention teams)
assumptions: mobile sinks and fixed sensor
objectives: increasing sink coverage
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16. Short Introduction
to Popular Models
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17. Network as a Graph
Unit-Disk Graph:
05 03
→ node position
07
09
→ circular transmission 12
11
06
10
01
→ boolean connections
02
00 08 04
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18. Network as a Graph
Unit-Disk Graph:
05 03
→ node position
07
09
→ circular transmission 12
11
06
10
01
→ boolean connections
02
00 08 04
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19. Network as a Graph
Unit-Disk Graph:
05 03
→ node position
07
09
→ circular transmission 12
11
06
10
01
→ boolean connections
02
00 08 04
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20. Interferences
Signal-to-noise-plus-interference (SINR) ratio
Pu
d(u,v )α
Pw ≥β
N+ w ∈V {u} d(w ,v )α
Pu : power level of sender u
d(u, v ): distance between u and v
α: path-loss exponent
N: noise
β: minimum ratio
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21. A Tour of the Most
Studied Issues
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22. Broadcasting I: Stormy Effect
Broadcast:
a simple basic problem :
a source emits a message
all nodes within the network eventually receive the
message
a simple and efficient solution:
upon first reception of message, forward it.
Limits of flooding in wireless networks:
redundant messages
interferences
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23. Broadcasting II: Proposals
Probabilistic flooding:
idea: forward the message with some probability p
drawbacks: no guarantee of delivering
refinements: adjust p to node density
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24. Broadcasting II: Proposals
Probabilistic flooding:
idea: forward the message with some probability p
drawbacks: no guarantee of delivering
refinements: adjust p to node density
Constrained flooding:
idea: only some nodes forward the message
implementation: build the Minimum
Connected Dominating Set
drawbacks: maintaining cost in dynamic systems
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25. Mobility Models I
Few theoretical proof, few real implementations
⇒ generate realistic node motions for simulations
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26. Mobility Models I
Few theoretical proof, few real implementations
⇒ generate realistic node motions for simulations
The simplest model: Random Waypoint
1. each node picks a random position uniformly
2. it travels toward this destination with a speed v
3. once it reaches it, it stops during few seconds
4. back to 1
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27. Mobility Models II: Improvements
Basic Structural Flaws:
non-uniform distribution of node location:
higher node distribution in the center
average speed decay:
low speed nodes spend more time to travel
Realistic Mobility Models:
group movement
area popularity
urban models
community-based
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28. Mobility Models II: Improvements
Basic Structural Flaws:
non-uniform distribution of node location:
higher node distribution in the center
average speed decay:
low speed nodes spend more time to travel
Realistic Mobility Models:
group movement
area popularity
urban models
community-based
the most realistic one : using real traces!
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29. Localized Data Gathering
Basic idea: query data from sensors within an area
two rounds:
query diffusion
retrieve data from sensors
main objectives:
minimize energy consumption
minimize the delay
A problem related with broadcasting except:
only sensors from the queried area are reached:
complex queries are possible (average, max, etc.)
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30. Time Synchronization I
Different time on nodes:
different oscillator frequency ⇒ frequency error
absolute difference between clocks ⇒ phase error
The need of a common clock
localization protocols
some MAC protocols
data fusion in sensor network
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31. Time Synchronization II
Broadcasting standard time via GPS system:
√
precision, simple implementation
× expensive devices
× limited usage (outdoor environment)
Achieve a common time distributively:
√
(almost) no special devices required
√
more tolerant to the environment
× special protocols
× message overhead, multi-hop delays
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32. A Focus on
Routing Protocols
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33. Routing Protocols
Objective:
select a path between a source and a destination
Main design challenges:
unstable network topology
low-cost devices (energy, computing. . . )
Main routing mechanisms:
neighbor discovering
route setup
route maintenance
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34. Proactive routing vs. On demand routing
Proactive Reactive
Setup all-to-all on demand
Maintenance regularly during utilization
Advantages no setup delay no unused routes
Disadvantages fixed overhead long setup delay
Main examples OLSR AODV
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35. AODV Route Discovery
G
F H
S B
E D
A
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36. AODV Route Discovery
Broadcasting
RREQ Mes- G
sage.
F H
S B
E D
A
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37. AODV Route Discovery
Setting up G
reverse path.
F H
S B
E D
A
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38. AODV Route Discovery
Setting up G
reverse path.
F H
S B
E D
A
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39. AODV Route Discovery
Setting up G
reverse path.
F H
S B
E D
A
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40. AODV Route Discovery
Setting up G
reverse path.
F H
S B
E D
A
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41. AODV Route Discovery
Setting up G
reverse path.
F H
S B
E D
A
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42. AODV Route Discovery
Replying
RREP to G
source. F H
S B
E D
A
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43. AODV Route Discovery
Forward path G
setup.
F H
S B
E D
A
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44. AODV Route Maintenance
Link breaks
between B G
and D. F H
S B
E D
A
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45. AODV Route Maintenance
Sending
RERR mes- G
sage.
F H
S B
E D
A
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46. AODV Route Maintenance
Restarting
route discov- G
ery.
F H
S B
E D
A
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47. AODV Route Maintenance
New route G
discovered. F H
S B
E D
A
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48. Some Tricks
Intelligent flooding (detect close destination)
idea: init TTL at 1, then 2, then 3. . .
idea: flood slowly and send message to stop it
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49. Some Tricks
Intelligent flooding (detect close destination)
idea: init TTL at 1, then 2, then 3. . .
idea: flood slowly and send message to stop it
Route caching (use past flooding)
idea: during flood, answer for a distant node
drawback: contradict reactive routing philosophy
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50. Some Tricks
Intelligent flooding (detect close destination)
idea: init TTL at 1, then 2, then 3. . .
idea: flood slowly and send message to stop it
Route caching (use past flooding)
idea: during flood, answer for a distant node
drawback: contradict reactive routing philosophy
Local maintenance (almost unchanged route)
idea: instead of NAK s, look for d by yourself
drawback: sometimes it does not work
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51. A Proactive Routing Protocol: OLSR
Objective: make use of Multi-Point Relay (MPR)
acting as super-peers
easing topology discovery
handling most of the traffic
OLSR message types:
HELLO: discover 1-hop and 2-hop neighbors
topology discovery through MPR
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52. Neighbor sensing
F G
H
S B
D
E A
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53. Neighbor sensing
F G
Nb:{S},
H
2hopNb:{}
Broadcasting
S B HELLO Message.
Nb:{S},
2hopNb:{}
D
E A
Nb:{S},
2hopNb:{}
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54. Neighbor sensing
F G
H
S B
Nb:{E}, Nb:{S,E},
2hopNb:{} 2hopNb:{}
D
E A
Nb:{E},
2hopNb:{S}
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55. Neighbor sensing
F G
Nb:{F},
H
2hop Nb:{S}
S B
Nb:{E,F}, Nb:{S,E,F},
2hop Nb:{} 2hop Nb:{}
D
E A
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56. Neighbor sensing
F G
Nb:{S,B,G}, Nb:{F,B,H},
H
2hopNb:{E,A,H} 2hopNb:{S,E,A,D} Nb:{G,D},
2hopNb:{B,F,A}
S B
Nb:{E,F,B}, Nb:{S,E,F,A,G},
2hopNb:{G,A} 2hopNb:{D,H}
D
E A Nb:{A,H},
Nb:{S,A,B}, Nb:{E,B,D}, 2hopNb:{B,E,G}
2hopNb:{F,G,D} 2hopNb:{S,F,G,H}
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57. MPR selection
F G
Nb:{S,B,G},
H
2hopNb:{E,A,H}
S B
Nb:{E,F,B}, Nb:{S,E,F,A,G},
2hopNb:{G,A} 2hopNb:{D,H}
D
E A
Nb:{S,A,B},
2hopNb:{F,G,D}
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58. MPR selection
F G
HELLO message
H
indicating B as
S B MPR of S and B
note S as its MPR
selector.
D
E A
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59. MPR selection
F G
MPR Selector: MPR Selector: H
{} {B,F,H} MPR Selector:
{G,D}
S B
MPR Selector: MPR Selector:
{} {S,G,E,F,A}
D
E A MPR Selector:
MPR Selector: MPR Selector: {A,H}
{} {B,E,D}
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60. Topology Table
Each node maintains a Topology Table
containing all possible destinations
notifying a MPR to reach them
Structure of Topology Table (on S for example):
Dest Addr Last Hop Seq Holding Time
G B 1 10
A B 4 20
D A 6 10
H G 5 15
... ... ... ...
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61. Building the Topology Table
F G
MPR Selector: H
{B,F,H}
S B
D
E A
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62. Building the Topology Table
F G
H
S B
Topology Table
Des Lhop Seq Htime
D
F
E G 2 30
A
H G 2 30
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63. Building the Topology Table
F G
H
MPR Selector:
{G,D}
S B
MPR Selector:
{S,G,E,F,A}
D
E A
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64. Building the Topology Table
F G
H
Topology Table
Des Lhop Seq Htime
S F G B2 30
H G 2 30
B G 2 30
D
E A
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65. Building the Topology Table
F G
H
Broadcasting contin-
S B ues. . .
D
E A MPR Selector:
MPR Selector: {A,H}
{B,E,D}
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66. Building the Topology Table
F G
MPR Selector: MPR Selector: H
{} {B,F,H} MPR Selector:
{G,D}
S B
MPR Selector: MPR Selector:
{} {S,G,E,F,A}
D
E A MPR Selector:
MPR Selector: MPR Selector: {A,H}
{} {B,E,D}
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67. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30
E A 6 30
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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H 30 Infrastructure-less Wireless Networks

68. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30
E A 6 30
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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69. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30
E A 6 30
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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70. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30 A B 2
E A 6 30 G B 2
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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71. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30 A B 2
E A 6 30 G B 2
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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72. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30 A B 2
E A 6 30 G B 2
D A 6 30
A D 7 30
H D 7 30
D H 8 30
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73. Building the Routing Table
Topology Table on S Neighbor Table on S
Des Lhop Seq Htime Nb:{E,F,B},
F G 2 30 2hopNb:{G,A}
H G 2 30
B G 2 30 Routing Table on S
F B 3 30 Des Nhop Hops
A B 3 30 E E 1
E B 3 30 F F 1
G B 3 30 B B 1
B A 6 30 A B 2
E A 6 30 G B 2
D A 6 30 H B 3
A D 7 30 D B 3
H D 7 30
D H 8 30
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74. Any Hybrid Approach ?
Merging advantages from both approaches:
build a routing table at 4 ∼ 5 hops
launch a reactive process if d is not
Applicative concerns:
OLSR is attractive because networks often small
AODV scales well but no all-to-all routing
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75. Research Activity:
Multi-Sinks Query
Range
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76. Multi-sink Multi-hop WSN
200
Target application: “fireman application”
Many sensors (small, blue) and some
firemen (large, green)
150
Firemen talk directly with the sensors
Gather only local information
On demand, fixed rate data gathering
100
Hop based query, constrained flooding
Simple to deploy and scalable
50
0 50 100 150 200 250 300
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77. Multi-sink Multi-hop WSN
200
Target application: “fireman application”
Many sensors (small, blue) and some
firemen (large, green)
150
Firemen talk directly with the sensors
Gather only local information
On demand, fixed rate data gathering
100
Hop based query, constrained flooding
Simple to deploy and scalable
50
0 50 100 150 200 250 300
35 / 0
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78. Multi-sink Multi-hop WSN
200
Target application: “fireman application”
Many sensors (small, blue) and some
firemen (large, green)
150
Firemen talk directly with the sensors
Gather only local information
On demand, fixed rate data gathering
100
Hop based query, constrained flooding
Simple to deploy and scalable
50
0 50 100 150 200 250 300
35 / 0
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79. Multi-sink Multi-hop WSN
200
Target application: “fireman application”
Many sensors (small, blue) and some
firemen (large, green)
150
Firemen talk directly with the sensors
Gather only local information
On demand, fixed rate data gathering
100
Hop based query, constrained flooding
Simple to deploy and scalable
50
0 50 100 150 200 250 300
35 / 0
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80. Multi-sink Multi-hop WSN
200
Target application: “fireman application”
Many sensors (small, blue) and some
firemen (large, green)
150
Firemen talk directly with the sensors
Gather only local information
On demand, fixed rate data gathering
100
Hop based query, constrained flooding
Simple to deploy and scalable
Networking assumptions:
50
IEEE 802.15.4 MAC layer, ZigBee tree routing
No in-network data aggregation, compression
0
Static sensors100
50
and sinks 150
(may extend to mobile 250
200
sinks) 300
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81. Network Sharing Without Congestions
200
Capacity of sensors c = 5
Each flow consumes r = 1
Nodes within u hops generate traffic
150
Configurations Feasible?
(5, 1) yes
S1
100
u1 = 5
S2
50 u2 = 1
0 50 100 150 200 250 300
0
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82. Network Sharing Without Congestions
200
Capacity of sensors c = 5
Each flow consumes r = 1
Nodes within u hops generate traffic
150
Configurations Feasible?
(5, 1) yes
S1
100 (4, 2) no
u1 = 4
S2
50 u2 = 2
0 50 100 150 200 250 300
0
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83. Network Sharing Without Congestions
200
Capacity of sensors c = 5
Each flow consumes r = 1
Nodes within u hops generate traffic
150
Configurations Feasible?
(5, 1) yes
S1
100 (4, 2) no
(3, 2) yes
u1 = 3
S2
50 u2 = 2
0 50 100 150 200 250 300
0
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84. Network Sharing Without Congestions
200
Capacity of sensors c = 5
Each flow consumes r = 1
Nodes within u hops generate traffic
150
Configurations Feasible?
(5, 1) yes
S1
100 (4, 2) no
(3, 2) yes
u1 = 2 (2, 3) yes
S2
50 u2 = 3
0 50 100 150 200 250 300
0
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85. Network Sharing Without Congestions
200
Capacity of sensors c = 5
Each flow consumes r = 1
Nodes within u hops generate traffic
150
Configurations Feasible?
(5, 1) yes
S1
100 (4, 2) no
(3, 2) yes
u1 = 2 (2, 3) yes
S2
50 u2 = 3
62 configurations
0 50 100 150 200 250 300
0
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86. Which Configuration is Better?
200
Basic considerations:
(4, 2): not feasible, (1, 1): inefficient
150
100
50
0 50 100 150 200 250 300
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87. Which Configuration is Better?
200
Basic considerations:
(4, 2): not feasible, (1, 1): inefficient
150
Optimality criteria: 1
Maximum Impact Range
100 (5, 1): Sum up to 6
Max-Min Fairness 4
(2, 3) = (3, 2) (5, 1) 3
2
50
(?, ?, ?, ?)
0 50 100 150 200 250 300
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88. Problem Formulation
Multi-Dimensional Multiple Choice Knapsack Problem
a NP-complete problem
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89. Problem Formulation
Multi-Dimensional Multiple Choice Knapsack Problem
a NP-complete problem
Toward a distributed heuristic algorithm
only local views of the network
only local optimal solutions
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90. Protocol
200
At each sink:
enlarge requirement periodically
receive notification from sensors
150
adjust requirement if it is smaller
At each sensor:
3
100
measure the traffic
detect congestion
A
50
4
2
1
solve the local problem
notify related sinks
0 50 100 150 200 250 300
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91. Conclusion
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92. Personal Thoughts
Great theoretical importance:
a lot of new and scientifically exciting problems
a multi-disciplinary field (network, algorithms,
computational geometry, probabilities)
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93. Personal Thoughts
Great theoretical importance:
a lot of new and scientifically exciting problems
a multi-disciplinary field (network, algorithms,
computational geometry, probabilities)
Unsure applicative importance:
no killer application yet
cellular networks just do what we want
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