Chapter 4

Routing Protocols

1

Overview

Routing in WSNs is challenging due to the inherent

characteristics that distinguish these networks from other

wireless networks like mobile ad hoc networks or

cellular networks.

First, due to the relatively large number of sensor nodes, it is

not possible to build a global addressing scheme for the

deployment of 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

Overview (cont.)

Third, sensor nodes are tightly constrained in terms of

energy, processing, and storage capacities. Thus, they require

careful 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

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

tactics, e.g., data aggregation and in-network

processing, clustering, different node role assignment,

and data-centric methods were employed.

4

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

Chapter 4.1

Routing Challenges and Design

Issues in WSNs

6

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

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. If the

resultant distribution of nodes is not uniform, optimal

clustering becomes necessary to allow connectivity and enable

energy efficient network operation.

8

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.

Sensor node lifetime shows a strong dependence on the battery

lifetime. In a multihop 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

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 in terms of energy consumption and route stability.

10

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

Fault Tolerance

Some sensor nodes may fail or be blocked due to lack

of power, physical damage, or environmental

interference.

It may require actively adjusting transmit 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

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

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).

On the other hand, sensing fixed events allows the

network to work in a reactive mode while dynamic

events in most applications require periodic reporting

to the BS.

14

Transmission Media

The traditional problems associated with a wireless

channel may also affect the operation of the sensor

network.

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

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

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

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

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

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

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

Chapter 4.2

Flat Routing

22

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

4.2.1

SPIN Sensor Protocols for Information via Negotiation

24

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

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

SPIN (cont.) Implosion

Node

The direction

of data sending

The connect

between nodes

27

A

C B

D

x

x x

x

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

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

SPIN (cont.) Sensor Protocols for Information via Negotiation

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

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

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

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

SPIN (3-Step Protocol)

B

A

35

SPIN (3-Step Protocol)

B

A

Notice the color of the data packets sent by node B

36

SPIN (3-Step Protocol)

B

A

SPIN effective when DATA sizes are large :

REQ, ADV overhead gets amortized

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

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

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

4.2.2

Directed Diffusion A Scalable and Robust Communication Paradigm

for Sensor Networks

40

Overview

Wireless sensor networks

Sensing devices with communication

capability

Event monitoring

Enemy detection, aircraft interiors, large

industrial plants

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

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

Challenges

Scalability

Energy efficiency

Robustness/Fault tolerance in outdoor areas

Efficient routing (multiple source destination pairs)

Directed Diffusion is an important milestone in the data centric

routing research of sensor networks

42

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

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

Data Naming

Expressing an Interest

Using attribute-value pairs

E.g.,

Other interest-expressing schemes possible

E.g., hierarchical (different problem)

45

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

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

Exploratory Gradient

48

Low

Data-rate

Interest

Event

Low

Data-rate

Interest

Low

Data-rate

Interest

Exploratory Request

Gradient

Bidirectional gradients established on all links through flooding

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

Event-data Propagation

Event e1 occurs, matches i1 in sensor cache

e1 identified based on waveform pattern matching

Interest reply diffused down gradient (unicast)

Diffusion initially exploratory (low packet-rate)

Cache filters suppress previously seen data

Problem of bidirectional gradient avoided

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

51

Source

Event

Reinforcement (2/4)

Path establishment for multiple sources and sinks

Node reinforce all neighbors from which new events were

recently received

Ex: Multiple sources A and B

52

Sink

D

Multiple sources

C

Event

A

B

Reinforcement (3/4)

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

53

Event

Reinforcement (4/4)

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 eliminated

Conservative negative reinforces useful for

fault resilience

54

C

E D

A B

A removable loop

Sink

Source

B

Multiple paths

A

Event

Design Considerations

Design Space for Diffusion

55

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

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 56

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

57

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.

58

Chapter 4.3

Hierarchical Routing

59

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.

60

4.3.1

LEACH Low-Energy Adaptive Clustering Hierarchy

61

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 TDMA/CDMA MAC to reduce inter-cluster

and intra-cluster collisions.

62

LEACH (cont.)

Sensors elect themselves to be local cluster-heads at any given

time with a certain probability. These cluster-head nodes

broadcast their status to the other sensors in the network.

Each sensor node determines to which cluster it wants to

belong by choosing the cluster-head that requires the minimum

communication energy.

Once all the nodes are organized into clusters, each cluster-head creates a schedule for the nodes in its cluster.

A cluster-head drains the battery of that node. In order to

spread this energy usage over multiple nodes, the cluster-head

nodes are not fixed; rather, this position is self-elected at

different time intervals.

63

LEACH: Adaptive Clustering

Periodic independent self-election

Probabilistic

CSMA MAC used to advertise

Nodes select advertisement with strongest signal strength

Dynamic TDMA cycles

64

All nodes marked with a given symbol belong to the same cluster, and

the cluster head nodes are marked with a ●.

Algorithm

Periodic process

Three phases per round:

Advertisement

Execute election algorithm

Setup

Schedule creation

the clusters are organized and cluster heads are selected

Steady-State

Data transmission

the data transfers to the BS (Base Station)

65

Homework Assignment - LEACH

66

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

66

Algorithm (cont.)

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 ,

Pif n G

P r PT n

otherwise

67

Algorithm Details (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

68

Algorithm (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 them back to the BS using a single direct

transmission

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

70

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

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

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.

72

Chapter 4.4

Location Based Routing

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)

74

4.4.1

GEAR Geographical and Energy Aware Routing

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

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 the learned cost of a neighbor to a region is not available, 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 distance of neighbor to region

e(Ni) = normalized consumed energy at node i

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(Ni, R) + c(N, Ni)

c(N, Ni) = combination of remaining energy of N and Ni and the

distance between them

78

Forwarding Around Holes

5A 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

79

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)= . 5 5

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

80

Ni

Recursive Geographic Forwarding (cont.) Pathologies

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 times for

sending and 4 times for receiving

= consuming

5 units of energy

81

Recursive Geographic Forwarding (cont.) Pathologies

Non-Termination

When network density is low compared to (sub) target region size

C

B

F

L

A

E

K

H

82

Recursive Geographic Forwarding (cont.) Proposed solution for pathologies

Node degree is used as a criteria to differentiate low density

networks from high density ones

Choice of restricted flooding over recursive geographic

forwarding is made accordingly

83

Conclusion

GEAR strategy attempts to balance energy consumption and

thereby increase network lifetime

GEAR performs better in terms of connectivity after initial

partition

84

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

4.4.2

GPSR Greedy Perimeter Stateless Routing

86

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 route.

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

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

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 regime follows successively closer geographic hops, until the destination is reached

89

Greedy Forwarding (cont.)

D

x

y

90

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

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

The Right-Hand Rule: Perimeters

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

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

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

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)

Planarized Graphs (cont.) Gabriel Graph (GG)

u v

w

97

Planarized Graphs (cont.) Relative Neighborhood Graph (RNG)

u v w

98

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

Because RNG removes more edges

Hereby, the RNG is used

If the original graph is connected, RNG is also

connected

99

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

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.

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

GPSR

Greedy Forwarding Perimeter Forwarding

greedy fails

have left local maxima greedy works greedy fails

Combining Greedy and Planar Perimeters (cont.)

GPSR packet header fields used in perimeter mode forwarding

104

Field Function

D

Lp

Lf

e0

M

Destination Location

Location Packet Entered Perimeter Mode

Point on xV Packet Entered Current Face

First Edge Traversed on Current Face

Packet Mode: Greedy or Perimeter

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

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.

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.

107

Chapter 4.5

QoS Based Routing

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.

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

110

Challenges in QoS Routing

Dynamically varying network topology

Imprecise state information

Lack of central coordination

Hidden node problem

Limited resource

Insecure medium

111

4.5.1

TBP (Ticket-Based Probing)

QoS of Bandwidth

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 113

Ticket-Based Probing (cont.)

S

D

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

115

Ticket-Based Probing (cont.)

S

D

i

j

k

116

Ticket-Based Probing (cont.)

S D

A

B

C

E

3 3

3

3 2

2

2

6

5

x

Demand = 3

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)

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

119

Ticket-Based Probing (cont.)

120

S D

T2

T1

S D

T2

T1

S D

T2

T1

x

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)

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.

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.

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.

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.

125