WSn Rouing Algorithmsn

1. Chapter 4
Routing Protocols
1

2. Overview
 Routing in WSNs is challenging due to distinguish from
other wireless networks like mobile ad hoc networks or
cellular networks.
 First, it is not possible to build a global addressing scheme for a
large number of sensor nodes. Thus, traditional IP-based
protocols may not be applied to WSNs. In WSNs, sometimes
getting the data is more important than knowing the IDs of
which nodes sent the data.
 Second, in contrast to typical communication networks, almost
all applications of sensor networks require the flow of sensed
data from multiple sources to a particular BS.
2

3. Overview (cont.)
 Third, sensor nodes are tightly constrained in terms of
energy, processing, and storage capacities. Thus, they require
carefully resource management.
 Fourth, in most application scenarios, nodes in WSNs are
generally stationary after deployment except for, may be, a
few mobile nodes.
 Fifth, sensor networks are application specific, i.e., design
requirements of a sensor network change with application.
 Sixth, position awareness of sensor nodes is important since
data collection is normally based on the location.
 Finally, data collected by many sensors in WSNs is typically
based on common phenomena, hence there is a high
probability that this data has some redundancy.
3

4. Overview (cont.)
 The task of finding and maintaining routes in WSNs is
nontrivial since energy restrictions and sudden
changes in node status (e.g., failure) cause frequent
and unpredictable topological changes.
 To minimize energy consumption, routing techniques
proposed for WSNs employ some well-known routing
strategies, e.g., data aggregation and in-network
processing, clustering, different node role assignment,
and data-centric methods were employed.
4

5. Outline
 4.1 Routing Challenges and Design Issues in WSNs
 4.2 Flat Routing
 4.3 Hierarchical Routing
 4.4 Location Based Routing
 4.5 QoS Based Routing
 4.6 Data Aggregation and Convergecast
 4.7 Data Centric Networking
 4.8 ZigBee
 4.9 Conclusions
5

6. Chapter 4.1
Routing Challenges and Design Issues in
WSNs
6

7. Overview
 The design of routing protocols in WSNs is influenced by
many challenging factors. These factors must be overcome
before efficient communication can be achieved in WSNs.
 Node deployment
 Energy considerations
 Data delivery model
 Node/link heterogeneity
 Fault tolerance
 Scalability
 Network dynamics
 Transmission media
 Connectivity
 Coverage
 Data aggregation/convergecast
 Quality of service
7

8. Node Deployment
 Node deployment in WSNs is application dependent
and affects the performance of the routing protocol.
 The deployment can be either deterministic or
randomized.
 In deterministic deployment, the sensors are manually
placed and data is routed through pre-determined
paths.
 In random node deployment, the sensor nodes are
scattered randomly creating an infrastructure in an ad
hoc manner.
8

9. Energy Considerations
 Sensor nodes can use up their limited supply of
energy performing computations and transmitting
information in a wireless environment. Energy
conserving forms of communication and computation
are essential.
 In a multi-hop WSN, each node plays a dual role as
data sender and data router. The malfunctioning of
some sensor nodes due to power failure can cause
significant topological changes and might require
rerouting of packets and reorganization of the
network.
9

10. Data Delivery Model
 Time-driven (continuous)
 Suitable for applications that require periodic data
monitoring
 Event-driven
 React immediately to sudden and drastic changes
 Query-driven
 Respond to a query generated by the BS or another node in
the network
 Hybrid
 The routing protocol is highly influenced by the data
reporting method
10

11. Node/Link Heterogeneity
 Depending on the application, a sensor node can have
a different role or capability.
 The existence of a heterogeneous set of sensors raises
many technical issues related to data routing.
 Even data reading and reporting can be generated
from these sensors at different rates, subject to diverse
QoS constraints, and can follow multiple data
reporting models.
11

12. Fault Tolerance
 Some sensor nodes may fail or be blocked due to lack
of power, physical damage, or environmental
interferences
 It may require actively adjusting transmission powers
and signaling rates on the existing links to reduce
energy consumption, or rerouting packets through
regions of the network where more energy is available
12

13. Scalability
 The number of sensor nodes deployed in the sensing
area may be on the order of hundreds or thousands, or
more.
 Any routing scheme must be able to work with this
huge number of sensor nodes.
 In addition, sensor network routing protocols should
be scalable enough to respond to events in the
environment.
13

14. Network Dynamics
 Routing messages from or to moving nodes is more
challenging since route and topology stability become
important issues
 Moreover, the phenomenon can be mobile (e.g., a
target detection/ tracking application).
14

15. Transmission Media
 In general, the required bandwidth of sensor data will
be low, on the order of 1-100 kb/s. Related to the
transmission media is the design of MAC.
 TDMA (time-division multiple access)
 CSMA (carrier sense multiple access)
15

16. Connectivity
 High node density in sensor networks precludes them
from being completely isolated from each other.
 However, may not prevent the network topology from
being variable and the network size from shrinking
due to sensor node failures.
 In addition, connectivity depends on the possibly
random distribution of nodes.
16

17. Coverage
 In WSNs, each sensor node obtains a certain view of
the environment.
 A given sensor’s view of the environment is limited in
both range and accuracy.
 It can only cover a limited physical area of the
environment.
17

18. Data Aggregation/Convergecast
 Since sensor nodes may generate significant
redundant data, similar packets from multiple nodes
can be aggregated to reduce the number of
transmissions.
 Data aggregation is the combination of data from
different sources according to a certain aggregation
function.
 Convergecasting is collecting information “upwards”
from the spanning tree after a broadcast.
18

19. Quality of Service
 In many applications, conservation of energy, which is
directly related to network lifetime.
 As energy is depleted, the network may be required to
reduce the quality of results in order to reduce energy
dissipation in the nodes and hence lengthen the total
network lifetime.
19

20. Routing Protocols in WSNs: A taxonomy
20
Network Structure Protocol Operation
Flat routing
• SPIN
• Directed Diffusion (DD)
Hierarchical routing
• LEACH
• PEGASIS
• TTDD
Location based routing
• GEAR
• GPSR
Negotiation based routing
• SPIN
Multi-path network routing
• DD
Query based routing
• DD, Data centric routing
QoS based routing
• TBP, SPEED
Coherent based routing
• DD
Aggregation
• Data Mules, CTCCAP
Routing protocols in WSNs

21. Reference
 J. N. Al-Karaki and A. E. Kamal, “Routing techniques in
wireless sensor networks: a survey,” IEEE Wireless
Communications, vol. 11, no. 6, pp. 6-28, Dec. 2004.
21

22. Chapter 4.2
Flat Routing
22

23. Overview
 In flat network, each node typically plays the same role and
sensor nodes collaborate together to perform the sensing task.
 Due to the large number of such nodes, it is not feasible to
assign a global identifier to each node. This consideration has
led to data centric routing, where the BS sends queries to
certain regions and waits for data from the sensors located in
the selected regions. Since data is being requested through
queries, attribute-based naming is necessary to specify the
properties of data.
 Prior works on data centric routing, e.g., SPIN and Directed
Diffusion, were shown to save energy through data negotiation
and elimination of redundant.
23

24. 4.2.1
SPIN
Sensor Protocols for Information via Negotiation
24

25. SPIN -Motivation
 Sensor Protocols for Information via Negotiation,
SPIN
 A Negotiation-Based Protocols for Disseminating
Information in Wireless Sensor Networks.
 Dissemination is the process of distributing individual
sensor observations to the whole network, treating all
sensors as sink nodes
 Replicate complete view of the environment
 Enhance fault tolerance
 Broadcast critical piece of information
25

26. SPIN (cont.)- Motivation
 Flooding is the classic approach for dissemination
 Source node sends data to all neighbors
 Receiving node stores and sends data to all its
neighbors
 Disseminate data quickly
 Deficiencies
 Implosion
 Overlap
 Resource blindness
26

27. SPIN (cont.)-Implosion
Node
The direction
of data sending
The connect
between nodes
27
A
C
B
D
x
x x
x

28. SPIN (cont.)- Overlap
q
r
s
(q, r) (s, r)
Node
The direction
of data sending
The connect
between nodes
The searching
range of the
node
A B
C
28

29. SPIN (cont.)- Resource blindness
 In flooding, nodes do not modify their activities based
on the amount of energy available to them.
 A network of embedded sensors can be resource-
aware and adapt its communication and computation
to the state of its energy resource.
29

30. SPIN (cont.)
 Negotiation
 Before transmitting data, nodes negotiate with each other to
overcome implosion and overlap
 Only useful information will be transferred
 Observed data must be described by meta-data
 Resource adaptation
 Each sensor node has resource manager
 Applications probe manager before transmitting or
processing data
 Sensors may reduce certain activities when energy is low
30

31. SPIN (cont.)- Meta-Data
 Completely describe the data
 Must be smaller than the actual data for SPIN to be
beneficial
 If you need to distinguish pieces of data, their meta-data
should differ
 Meta-Data is application specific
 Sensors may use their geographic location or unique node ID
 Camera sensor may use coordinate and orientation
31

32. SPIN (cont.)- SPIN family
 Protocols of the SPIN family
 SPIN-PP
 It is designed for a point to point communication, i.e., hop-
by-hop routing
 SPIN-EC
 It works similar to SPIN-PP, but, with an energy heuristic
added to it
 SPIN-BC
 It is designed for broadcast channels
 SPIN-RL
 When a channel is lossy, a protocol called SPIN-RL is
used where adjustments are added to the SPIN-PP protocol
to account for the lossy channel.
32

33. SPIN (cont.)- Three-stage handshake protocol
 SPIN-PP: A three-stage handshake protocol for point-
to-point media
 ADV – data advertisement
 Node that has data to share can advertise this by
transmitting an ADV with meta-data attached
 REQ – request for data
 Node sends a request when it wishes to receive some
actual data
 DATA – data message
 Contain actual sensor data with a meta-data header
 Usually much bigger than ADV or REQ messages
33

34. 34
SPIN (3-Step Protocol)
B
A

35. 35
SPIN (3-Step Protocol)
B
A
Notice the color of the data packets sent by node B

36. 36
SPIN (3-Step Protocol)
B
A
SPIN effective when DATA sizes are large :
REQ, ADV overhead gets amortized

37. SPIN (cont.)- SPIN-EC (Energy-Conserve)
 Add simple energy-conservation heuristic to SPIN-PP
 SPIN-EC: SPIN-PP with a low-energy threshold
 Incorporate low-energy-threshold
 Works as SPIN-PP when energy level is high
 Reduce participation of nodes when approaching low-energy-
threshold
 When node receives data, it only initiates protocol if it can
participate in all three stages with all neighbor nodes
 When node receives advertisement, it does not request the
data
 Node still exhausts energy below threshold by receiving ADV
or REQ messages
37

38. SPIN (cont.)- Conclusion
 SPIN protocols hold the promise of achieving high
performance at a low cost in terms of complexity, energy,
computation, and communication
 Pros
 Each node only needs to know its one-hop neighbors
 Significantly reduce energy consumption compared to
flooding
 Cons
 Data advertisement cannot guarantee the delivery of data
 If the node interested in the data are far from the source,
data will not be delivered
 Not good for applications requiring reliable data delivery,
e.g., intrusion detection
38

39. SPIN (cont.)- Reference
 J. Kulik, W.R. Heinzelman, and H. Balakrishnan, “Negotiation-
based protocols for disseminating information in wireless
sensor networks,” Wireless Networks, Vol. 8, pp. 169-185, 2002.
39

40. 4.2.2
Directed Diffusion
A Scalable and Robust Communication
Paradigm for Sensor Networks
40

41. Overview
 Data-centric communication
 Data is named by attribute-value pairs
 Different form IP-style communication
 End-to-end delivery service
 e.g.
 How many pedestrians do you
observe in the geographical region X?
41
Event
Sources
Sink Node
Directed
Diffusion
A sensor field

42. Overview (cont.)
 Data-centric communication (cont.)
 Human operator’s query (task) is diffused
 Sensors begin collecting information about query
 Information returns along the reverse path
 Intermediate nodes aggregate the data
 Combing reports from sensors
 Directed Diffusion is an important milestone in the
data centric routing research of sensor networks
42

43. Directed Diffusion
 Typical IP based networks
 Requires unique host ID addressing
 Application is end-to-end
 Directed diffusion – use publish/subscribe
 Inquirer expresses an interest, I, using attribute values
 Sensor sources that can service I, reply with data
43

44. Directed Diffusion (cont.)
 Directed diffusion consists of
 Interest - Query which specifies what a user wants
 Data - Collected information
 Gradient
 Direction and data-rate
 Events start flowing towards the originators of interests
 Reinforcement
 After the sink starts receiving events, it reinforces at least
one neighbor to draw down higher quality events
44

45. Data Naming
 Expressing an Interest
 Using attribute-value pairs
 e.g.,
45

46. Interests and Gradients
 Interest propagation
 The sink broadcasts an interest
 Exploratory interest with low data-rate
 Neighbors update interest-cache and forwards it
 Flooding
 Geographic routing
 Use cached data to direct interests
 Gradient establishment
 Gradient set up to upstream neighbor
 Low data-rate gradient
 Few packets per unit time needed
46

47. Gradient Set Up
 Inquirer (sink) broadcasts exploratory interest, i1
 Intended to discover routes between source and sink
 Neighbors update interest-cache and forwards i1
 Gradient for i1 set up to upstream neighbor
 No source routes
 Gradient – a weighted reverse link
 Low gradient  Few packets per unit time needed
47

48. Exploratory Gradient
48
Low Data-rate Interest
Event
Low Data-rate Interest
Low Data-rate Interest
Exploratory Request
Gradient

49. Data Propagation
 A sensor node that detects a target
 Search its interest cache
 Compute the highest requested data-rate among all
its outgoing gradients
 Data message is unicast individually
 A node that receives a data message
 Find a matching interest entry in its cache
 Check the data cache for loop prevention
 Re-send the data to neighbors
49

50. Reinforcement (1/4)
 Positive reinforcement
 Sink selects the neighboring node
 Original interest message but with high data-rate
 Neighboring node must also reinforce at least one neighbor
 Low-delay path is selected
 Exploratory gradients still exist: useful for faults
Sink
A sensor field
Reinforced gradient
Reinforced gradient
50
Source
Event

51. Reinforcement (2/4)
 Path failure and recovery
 Link failure detected by reduced rate, data loss
 Choose next best link (i.e., compare links based on
infrequent exploratory downloads)
 Negatively reinforce lossy link
 Either send interest with base (exploratory) data rate or
allow neighbor’s cache to expire over time
Sink
Source A
C
B
M
D
Link A-M lossy
A reinforces B
B reinforces C
C reinforces D
or
A negative reinforces M
M negative reinforces D
51
Event

52. Reinforcement (3/4)
 Multipath routing
 Consider each gradient’s link quality
 Using negative reinforcement
 Path Truncation
 Loop removal
 For resource saving
 Ex:
 B gets same data from both A and D, but
D always delivers late due to looping
 B negative reinforces D, D negative
reinforces E, E negative reinforces B
 Loop B→E →D →B eliminated
 Conservative negative reinforces useful for
fault resilience
52
C
E
D
A B
A removable loop
Sink
Source
B
Multiple paths
A
Event

53. Design Considerations
 Design Space for Diffusion
53
Diffusion element Design Choices
Interest Propagation •Flooding
•Constrained or directional flooding based on location
•Directional propagation based on previously cached data
Data Propagation •Reinforcement to single path delivery
•Multipath delivery with selective quality along different
paths
• Multipath delivery with probabilistic forwarding
Data caching and
aggregation
•For robust data delivery in the face of node failure
•For coordinated sensing and data reduction
• For directing interests
Reinforcement •Rules for deciding when to reinforce
•Rules for how many neighbors to reinforce
•Negative reinforcement mechanisms and rules

54. Directed Diffusion: Pros & Cons
 Different from SPIN in terms of on-demand data querying
mechanism
 Sink floods interests only if necessary (lots of energy savings)
 In SPIN, sensors advertise the availability of data
 Pros
 Data centric: All communications are neighbor to neighbor
with no need for a node addressing mechanism
 Each node can do aggregation & caching
 Cons
 On-demand, query-driven: Inappropriate for applications
requiring continuous data delivery, e.g., environmental
monitoring
 Attribute-based naming scheme is application dependent
 For each application it should be defined a priori
 Extra processing overhead at sensor nodes
54

55. Conclusions
 Directed diffusion, a paradigm proposed for event monitoring
sensor networks
 Directed Diffusion has some novel features - data-centric
dissemination, reinforcement-based adaptation to the
empirically best path, and in-network data aggregation and
caching.
 Notion of gradient (exploratory and reinforced)
 Energy efficiency achievable
 Diffusion mechanism resilient to fault tolerance
 Conservative negative reinforcements proves useful
55

56. References
 C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed
Diffusion: A Scalable and Robust Communication Paradigm
for Sensor Networks,” in the Proceedings of the Sixth Annual
International Conference on Mobile Computing and Networks
(MobiCom’00), August 2000.
 C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and
F. Silva, “Directed Diffusion for Wireless Sensor Networking,”
IEEE/ACM Transactions on Networking, Vol. 11, No. 1, Feb.
2003.
56

57. Chapter 4.3
Hierarchical Routing
57

58. Overview
 In a hierarchical architecture, higher energy nodes can be used
to process and send the information while low energy nodes
can be used to perform the sensing of the target.
 Hierarchical routing is mainly two-layer routing where one
layer is used to select cluster heads and the other layer is used
for routing.
 Hierarchical routing (or cluster-based routing), e.g., LEACH,
PEGASIS, TTDD, is an efficient way to lower energy
consumption within a cluster and by performing data
aggregation and fusion in order to decrease the number of
transmitted messages to the base stations.
58

59. 4.3.1
LEACH
Low-Energy Adaptive Clustering Hierarchy
59

60. LEACH
 LEACH (Low-Energy Adaptive Clustering Hierarchy), a
clustering-based protocol that minimizes energy dissipation in
sensor networks.
 LEACH outperforms classical clustering algorithms by using
adaptive clusters and rotating cluster-heads, allowing the
energy requirements of the system to be distributed among all
the sensors.
 LEACH is able to perform local computation in each cluster to
reduce the amount of data that must be transmitted to the base
station.
 LEACH uses a CDMA/TDMA MAC to reduce inter-cluster
and intra-cluster collisions.
60

61. LEACH (cont.)
 Sensors elect themselves to be local cluster-heads at
any given time with a certain probability.
 Each sensor node joins a cluster-head that requires the
minimum communication energy.
 Once all the nodes are organized into clusters, each
cluster-head creates a transmission schedule for the
nodes in its cluster.
 In order to balance the energy consumption, the
cluster-head nodes are not fixed; rather, this position
is self-elected at different time intervals.
61

62. LEACH
100 m
叢集區
觀測區域
Base Station
Sensor (Non Cluster Head)
Sensor (Cluster Head)
Initial Data
Aggregated Data
~100m
62

63. LEACH: Adaptive Clustering
 Periodic independent self-election
 Probabilistic
 CSMA MAC used to advertise
 Nodes select advertisement with strongest signal strength
 Dynamic TDMA cycles
63
All nodes marked with a given symbol belong to the same cluster, and
the cluster head nodes are marked with a ●.

64. Algorithm
 Periodic process
 Two phases per round:
 Setup phase
 Advertisement: Execute election algorithm
 Members join to cluster
 Cluster-head broadcasts schedule
 Steady-State phase
 Data transmission to cluster-head using TDMA
 Cluster-head transfers data to BS (Base Station)
64

65. Algorithm (cont.)
65
Advertisement phase Cluster setup phase Broadcast schedule
Time slot
1
Time slot
2
Time slot
3
Setup phase Steady-state phase
Self-election of cluster
heads
Cluster heads compete
with CSMA
Members
compete with
CSMA
Cluster head Broadcast
CDMA code to members
Fixed-length cycle
65

66. Algorithm Summary
 Set-up phase
 Node n choosing a random number m between 0 and 1
 If m < T(n) for node n, the node becomes a cluster-head where
 where P = the desired percentage of cluster heads (e.g., P= 0.05), r=the
current round, and G is the set of nodes that have not been cluster-heads
in the last 1/P rounds. Using this threshold, each node will be a cluster-
head at some point within 1/P rounds. During round 0 (r=0), each node
has a probability P of becoming a cluster-head.
1 [ * mod(1/ )]
( )
0 ,
P
if n G
P r P
T n
otherwise




 


66

67. Algorithm Summary (cont.)
 Set-up phase
 Cluster heads assign a TDMA schedule for their members
where each node is assigned a time slot when it can transmit.
 Each cluster communications using different CDMA codes to
reduce interference from nodes belonging to other clusters.
 TDMA intra-cluster
 CDMA inter-cluster
 Spreading codes determined randomly
 Broadcast during advertisement phase
67

68. Algorithm Summary (cont.)
 Steady-state phase
 All source nodes send their data to their cluster heads
 Cluster heads perform data aggregation/fusion through local
transmission
 Cluster heads send aggregated data back to the BS using a
single direct transmission
68

69. An Example of a LEACH Network
 While neither of these diagrams is the optimum scenario, the
second is better because the cluster-heads are spaced out and
the network is more properly sectioned
69
Node
Cluster-Head Node
Node that has been cluster-head in the last 1/P rounds
Cluster Border
X
Bad case scenario
Good case scenario

70. Conclusions
 Advantages
 Increases the lifetime of the network
 Even drain of energy
 Distributed, no global knowledge required
 Energy saving due to aggregation by CHs
 Disadvantages
 LEACH assumes all nodes can transmit with enough power
to reach BS if necessary (e.g., elected as CHs)
 Each node should support both TDMA & CDMA
 Need to do time synchronization
 Nodes use single-hop communication
70

71. Reference
 W. Heinzelman, A. Chandrakasan, and H. Balakrishnan,
“Energy-efficient communication protocol for wireless sensor
networks,” Proceedings of the 33rd Hawaii International
Conference on System Sciences, January 2000.
71

72. Chapter 4.4
Location Based Routing
72

73. Overview
 Sensor nodes are addressed by means of their locations.
 The distance between neighboring nodes can be estimated on the basis of
incoming signal strengths.
 Relative coordinates of neighboring nodes can be obtained by
exchanging such information between neighbors.
 To save energy, some location based schemes demand that
nodes should go to sleep if there is no activity.
 More energy savings can be obtained by having as many
sleeping nodes in the network as possible.
 Hereby, two important location based routing protocols, GEAR
and GPSR, are introduced.
 Geographical and Energy Aware Routing (GEAR)
 Greedy Perimeter Stateless Routing (GPSR)
73

74. 4.4.1
GEAR
Geographical and Energy Aware Routing
74

75. Geographical and Energy Aware Routing
(GEAR)
 The protocol, called Geographic and Energy Aware Routing
(GEAR), uses energy aware and geographically-informed
neighbor selection heuristics to route a packet towards the
destination region.
 The key idea is to restrict the number of interests in directed
diffusion by only considering a certain region rather than
sending the interests to the whole network. By doing this,
GEAR can conserve more energy than directed diffusion.
 The basic concept comprises of two main parts
 Route packets towards a target region through geographical
and energy aware neighbor selection
 Disseminate the packet within the region
75

76. Energy Aware Neighbor Computation
 Each node N maintains state h(N, R) which is called learned
cost to region R, where R is the target region
 Each node infrequently updates neighbor of its cost
 When a node wants to send a packet, it checks the learned cost
to that region of all its neighbors
 If a node does not have the learned cost of a neighbor to a
region, the estimated cost is computed as follows:
c(Ni, R) = αd(Ni, R) + (1-α)e(Ni)
where
α = tunable weight, from 0 to 1.
d(Ni, R) = normalized the largest distance among neighbors of N
e(Ni) = normalized the largest consumed energy among neighbors
of N
76

77. Energy Aware Neighbor Computation
(cont.)
 When a node wants to forward a packet to a
destination, it checks to see if it has any neighbor
closer to destination than itself
 In case of multiple choices, it aims to minimize the
learned cost h(Nmin, R)
 It then sets its own cost to:
h(N, R) = h(Nmin, R) + c(N, Nmin)
c(N, Nmin) = the transmission cost from N and Nmin
77

78. Forwarding Around Holes
5
A B C D E
F G H I J
K L T
S
C – T = 2
h(C,T) = h(B,T)+c(C,B)
B – T =
x
78
5
α is set to 1. Initially, at time 0, at node S, among all neighbors of S, B, C, D
are closer to T than S. h(B,T)=c(B,T)= , h(C,T)=c(C,T)=2, h(D,T)=c(D,T)= .
After that , h(C, T) = h(B, T) + 1
5 5

79. Recursive Geographic Forwarding
 Once the target region is reached, the packets are disseminated
within the region by recursive geographic forwarding
 Forwarding stops when a node is the only one in a sub-region
79
Ni

80. Recursive Geographic Forwarding (cont.)
 When network density is low recursive geographic
forwarding is subject to two pathologies: inefficient
transmissions and non-termination
80

81. Recursive Geographic Forwarding (cont.)
 Inefficient Transmission
 Recursive geographic forwarding vs. Restricted flooding
F
A
E B
C
D
Recursive Geographic
Forwarding 3 times for sending
and 3 times for receiving =
consuming 6 units of energy
Restricted flooding 1 time for
sending and 4 times for receiving
= consuming
5 units of energy
81

82. Recursive Geographic Forwarding (cont.)
Pathologies
 Non-Termination
 In the recursive geographic forwarding protocol, packet
forwarding terminates when the target subregion is empty.
C
B
F
L
A
E
K
H
82

83. Recursive Geographic Forwarding (cont.)
Proposed solution for pathologies
 Solution:
 Node degree is used as a criteria to differentiate low
density networks from high density ones
 Choice of restricted flooding over recursive
geographic forwarding if the receiver’s node degree is
below a threshold value
83

84. Conclusion
 GEAR strategy attempts to balance energy
consumption and thereby increase network lifetime
 GEAR performs better in terms of connectivity after
initial partition
84

85. References
 Y. Yu, D. Estrin, and R. Govindan, “Geographical and Energy-Aware
Routing: A Recursive Data Dissemination Protocol for Wireless
Sensor Networks”, UCLA Computer Science Department Technical
Report, UCLA-CSD TR-01-0023, May 2001.
 Nirupama Bulusu, John Heidemann, and Deborah Estrin. “Gps-less
low cost outdoor localization for very small devices”. IEEE Personal
Communications Magazine, 7(5):28-34, October 2000.
 L. Girod and D. Estrin. “Robust range estimation using acoustic and
multimodal sensing”. In IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2001), Maui, Hawaii, October
2001.
 Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan.
“The cricket location-support system”. In Proc. ACM Mobicom,
Boston, MA, 2000.
 Andreas Savvides, Chih-Chieh Han, and Mani B. Strivastava.
“Dynamic fine-grained localization in adhoc networks of sensors”. In
Proc. ACM Mobicom, 2001.
85

86. 4.4.2
GPSR
Greedy Perimeter Stateless Routing
86

87. Greedy Perimeter Stateless Routing (GPSR)
 Greedy Perimeter Stateless Routing (GPSR) proposes the
aggressive use of geography to achieve scalability
 GEAR was compared to a similar non-energy-aware routing
protocol GPSR, which is one of the earlier works in geographic
routing that uses planar graphs to solve the problem of holes
 In case of GPSR, the packets follow the perimeter of the planar
graph to find their routes
 Although the GPSR approach reduces the number of states a
node should keep, it has been designed for general mobile ad
hoc networks and requires a location service to map locations
and node identifiers.
87

88. Algorithm & Example
 The algorithm consists of two methods:
greedy forwarding + perimeter forwarding
 Greedy forwarding, which is used wherever possible,
and perimeter forwarding, which is used in the regions
greedy forwarding cannot be done.
88

89. Greedy Forwarding (cont.)
 Under GPSR, packets are marked by their originator
with their destinations’ locations
 As a result, a forwarding node can make a locally
optimal, greedy choice in choosing a packet’s next
hop
 Specifically, if a node knows its radio neighbors’
positions, the locally optimal choice of next hop is the
neighbor geographically closest to the packet’s
destination
 Forwarding in this scheme follows successively closer
geographic hops, until the destination is reached
89

90. Greedy Forwarding (cont.)
D
x
y
90

91. Greedy Forwarding (cont.)
 A simple beaconing algorithm provides all nodes with
their neighbors’ positions: periodically, each node
transmits a beacon to broadcast MAC address,
containing its own identifier (e.g., IP address) and
position
 Position is encoded as two four-byte floating point
quantities, for x and y coordinate values
 Upon not receiving a beacon from a neighbor for
longer than timeout interval T, a GPSR router assumes
that the neighbor has failed or gone out-of-range, and
deletes the neighbor from its neighbor table
91

92. Greedy Forwarding (cont.)
The Problem of Greedy Forwarding
x
w y
D
v z
|xD|<|wD|and|yD|
x will not choose to
forward to w or y
using greedy
forwarding
void
x
x
92

93. The Right-Hand Rule: Perimeters
 In previous works, use the right-hand rule to map perimeters by
sending packets on tours of them. The state accumulated in
these packets is cached by nodes, which recover from local
maxima in greedy forwarding by routing to a node on a cached
perimeter closer to the destination.
 This approach requires a heuristic, the no-crossing heuristic, to
force the right-hand rule to find perimeters that enclose voids
in regions where edges of the graph cross
93
x
y
z

94. 94
Right-Hand Rule Does Not Work with
Cross Edges
u
z
w
D
x
 x originates a packet to u
 Right-hand rule results in the
tour x-u-z-w-u-x

95. 95
Remove Crossing Edge
u
z
w
v
x
Make the graph planar
Remove (w,z) from the
graph
 Right-hand rule results in
the tour x-u-z-v-x

96. 96
Make a Graph Planar
 A graph in which no two edges cross is known as
planar. A set of nodes with radios, where all radios
have identical, circular radio range r, can be seen as a
graph: each node is a vertex, and edge (n, m) exists
between nodes n and m if the distance between n and
m, d(n, m)≦r.
 Convert a connectivity graph to planar non-crossing
graph by removing “bad” edges
 Ensure the original graph will not be disconnected
 Two types of planar graphs:
 Relative Neighborhood Graph (RNG)
 Gabriel Graph (GG)

97. Planarized Graphs (cont.)
Relative Neighborhood Graph (RNG)
u v
w
97

98. Planarized Graphs (cont.)
Gabriel Graph (GG)
u v
w
98

99. Planarized Graphs (cont.)
 An algorithm for removing edges from the graph that
are not part of the RNG or GG would yield a network
with no crossing links
 The RNG is a subset of the GG
 It is because RNG removes more edges
 Hereby, the RNG is used
 If the original graph is connected, RNG is also
connected
99

100. 100
Connectedness of RNG Graph
 Key observation
 Any edge on the minimum spanning
tree of the original graph is not
removed
 Proof by contradiction: Assume
(u,v) is such an edge but removed in
RNG
u
v
w

101. Planarized Graphs (cont.)
Gabriel Graph (GG)
Relative
Neighborhood Graph
(RNG)
Original
101
The GG subset of
the full graph
The full graph of a radio
network, 200 nodes, uniformly
randomly placed on a 2000 x
2000 meter region, with a radio
range of 250 m.
The RNG subset of the
full and GG graphs.

102. Combining Greedy and Planar Perimeters
 All data packets are marked initially at their originators as
greedy mode
 GPSR packet headers include a flag field indicating whether
the packet is in greedy mode or perimeter mode
 Packet sources also include the geographic location of the
destination in packets
 Only a packet’s source sets the location destination field, it is
left unchanged as the packet is forwarded through the network
 Upon receiving a greedy-mode packet for forwarding, a node
searches its neighbor table for the neighbor geographically
closest to the packet’s destination
 When no neighbor is closer, the node marks the packet into
perimeter mode
102

103. 103
GPSR
Greedy Forwarding Perimeter Forwarding
greedy fails
have left local maxima
greedy works greedy fails

104. Combining Greedy and Planar Perimeters
(cont.)
Lp
Lf
e0
D
x
If forwarding node to D < Lp to D,
returns a packet to greedy mode
104

105. Conclusion
 GPSR’s benefits all stem from geographic routing’s
use of only immediate-neighbor information in
forwarding decisions.
 GPSR keeps state proportional to the number of its
neighbors, while both traffic sources and intermediate
DSR routers cache state proportional to the product of
the number of routes learned and route length in hops.
105

106. References
 B. Karp and H. T. Kung, “Greedy Perimeter Stateless Routing
for Wireless Networks”, Proc. 6th Annual ACM/IEEE Int'l.
Conf. Mobile Comp. Net., Boston, MA, pp. 243-54, August
2000.
 G. G. Finn, “Routing and addressing problems in large
metropolitan-scale internetworks”, Tech. Rep. ISI/RR-87-180,
Information Sciences Institute, March 1987.
 S. Floyd and V. Jacoboson, “The synchronization of periodic
routing messages”, IEEE/ACM Transactions on Networking,
Vol. 2, pp. 122-136, April 1994.
 B. Karp “Greedy perimeter state routing”, Invited Seminar at
the USC/Information Sciences Institute, July 1998.
 J. Saltzer, D. P. Reed, and D. Clark, “End-to-end arguments in
system design”, ACM Transactions on Computer Systems, Vol.
2, No. 4, Pages: 277-288, November 1984.
106

107. Chapter 4.5
QoS Based Routing
107

108. Overview
 QoS is the performance level of service offered by a
network to the user.
 The goal of QoS is to achieve a more deterministic
network behavior so that the information carried by
the network can be better delivered and the resources
can be better utilized.
 In QoS-based routing protocols, the network has to
balance between energy consumption and data quality.
 In particular, the network has to satisfy certain QoS
metrics, e.g., delay, energy, bandwidth, etc. when
delivering data to the BS.
108

109. Parameters of QoS Networks
 Different services require different QoS parameters
 Multimedia
 Bandwidth, delay jitter & delay
 Emergency services
 Network availability
 Group communications
 Battery life
 Generally the parameters that are important are:
 bandwidth
 delay jitter
 battery charge
 processing power
 buffer space
109

110. Challenges in QoS Routing
 Dynamically varying network topology
 Imprecise state information
 Lack of central coordination
 Hidden node problem
 Limited resource
 Insecure medium
110

111. 4.5.1
TBP (Ticket-Based Probing)
QoS of Bandwidth
111

112. Ticket-Based Probing
 Distributed multi path QoS routing scheme
 Bandwidth-constrained routing and delay-constrained routing
 There are numerous paths from source to destination,
we shall not randomly pick several paths to search
 We shall not use any flooding path-discovery
approaches, which may send routing messages to the
entire network
 Multipath search is tolerant to imprecise information
 We want to make an intelligent hop-by-hop path
selection to guide the search along the best candidate
paths
112

113. Ticket-Based Probing (cont.)
S
D
113

114. Ticket-Based Probing (cont.)
 A ticket is the permission to search one path. The
source node issues a number of tickets based on the
available state information
 Utilizes tickets to limit the number of paths searched
during route discovery
 A ticket is the permission to search a single path
 More tickets, more QoS constraints are required
 Probes (routing messages) are sent from the source
toward the destination to search for a low-cost path
that satisfies the QoS requirement
 Each probe is required to carry at least one ticket
114

115. Ticket-Based Probing (cont.)
S
D
i
j
k
115

116. Ticket-Based Probing (cont.)
S
D
A
B
C
E
3 3
3
3
2
2
2
6
5
x
Demand = 3
116

117. Ticket-Based Probing (cont.)
S
D
A
B
C
E
3 3
3
2
2
2
2
6
5
Demand = 4
(1.1,3)
(1.2,1)
(1.2,1)
(1.1,3)
(1.2,1)
117

118. Ticket-Based Probing (cont.)
S
D
A
B
C
E
3 3
3
2
2
2
2
6
5
(1.1,3)
(1.2,1)
(1.1.1,2)
(1.1.2,1)
(1.1.2,1)
(1.2,1)
(1.2,1)
Demand = 4
118

119. Ticket-Based Probing (cont.)
119
S
D
T2
T1
S
D
T2
T1
S
D
T2
T1
x

120. Ticket-Based Probing (cont.)
S
D
A
B
C
E
4 3
3
2
4
2
3
6
5
x
Demand = 4
x
(1,4)
(2.1,3)
(2.2,1)
(2.1,3)
(2.1,3)
(2.1,3)
(2.2,1)
(2.2,1)
120

121. Conclusion
 The routing overhead is controlled by the number of tickets,
which allows the dynamic tradeoff between the overhead and
the routing performance. Issuing more tickets means searching
more paths, which results in a better chance of finding a
feasible path at the cost of higher overhead.
 A distributed routing process is used to avoid any centralized
path computation that could be very expensive for QoS routing
in large networks.
 This approach not only increases the chance of success but also
improves the ability to tolerate the information imprecision
because the intermediate nodes may gradually correct a wrong
decision made by the source.
121

122. Conclusion (cont.)
 Ticket-based probing scheme achieves a balance between the
single-path routing algorithms and the flooding algorithms. It
does multipath routing without flooding.
 The basic idea is to achieve a near-optimal performance with
modest overhead by using a limited number of tickets and
making intelligent hop-by- hop path selection.
122

123. References
 S. Chen and K. Nahrstedt, “On finding multi-constrained paths,” in Proc.
IEEE ICC’98, pp. 874-879.
 R. Guerin and A. Orda, “QoS-based routing in networks with inaccurate
information: Theory and algorithms,” in Proc. IEEE INFOCOM’97,
Japan, pp. 75-83.
 Q. Ma and P. Steenkiste, “Quality-of-service routing with performance
guarantees,” in Proc. 4th Int. IFIP Workshop Quality of Service, May
1997, pp. 115-126.
 Z. Wang and J. Crowcroft, “QoS routing for supporting resource
reservation,” IEEE J. Select. Areas Commun., Sept. 1996.
 S. Chen and K Nahrstedt, “Distributed Quality-of-Service Routing in Ad
Hoc Networks,” IEEE J. Select. Areas Commun, vol.17, no. 8, pp.
1488-1505, Aug. 1999.
123

124. References
 T. Hea, J. A Stankovic, C. Lu, and T. Abdelzaher, “SPEED: a
stateless protocol for real-time communication in sensor
networks,” in Proc. IEEE International Conference on
Distributed Computing Systems, pp. 46-55, May 2003.
 G. S. Ahn, A. T. Campbell, A. Veres, and L.H. Sun. “SWAN:
Service Differentiation in Stateless Wireless Ad Hoc Networks,”
In Proc. IEEE INFOCOM'2002, June 2002.
124