Chapter 4Routing 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 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

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

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 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

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

NodeThe direction of data sendingThe connect between nodes

27

A

CB

D

x

x x

x

SPIN (cont.)- Overlap

q

r

s

(q, r) (s, r)

NodeThe direction of data sendingThe connect between nodesThe searching range of the node

A B

C28

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

ADVREQDATA

ADV

AD

VADV

ADV

AD

V ADV

REQ

RE

Q

REQ

RE

Q

REQ

DATADA

TA

DATA

DA

TA

DATA

35

SPIN (3-Step Protocol)

B

A

DATADA

TA

DATA

DA

TA

DATA

Notice the color of the data packets sent by node B

36

SPIN (3-Step Protocol)

B

A

DATADA

TA

DATA

DA

TA

DATA

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

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

EventEventEventEventSources

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 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

EventEvent

Low Data-rate Interest Low Data-rate Interest

Exploratory RequestGradient

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

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

SinkA sensor field

Reinforced gradientReinforced gradient

51

Source

EventEvent

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

EventEvent

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 AC

B

MD

Link A-M lossyA reinforces BB reinforces C C reinforces D or A negative reinforces MM negative reinforces D

53

EventEvent

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 →B eliminated Conservative negative reinforces useful for

fault resilience54

C

ED

A B

A removable loop

Sink

SourceB

Multiple paths

AEventEvent

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 slot1

Time slot2

Time slot3

Setup phase Steady-state phase

Self-election of cluster headsCluster 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

NodeCluster-Head NodeNode that has been cluster-head in the last 1/P roundsCluster Border

X

Bad case scenarioGood 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 = xxx

79

5

α is set to 1. Initially, at time 0, at node S, among all neighbors of S, B, C, Dare 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

FL

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

xx

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 vw

97

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

u vw

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

uv

w

Planarized Graphs (cont.)

Gabriel Graph (GG)Relative Neighborhood Graph (RNG)

Original

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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

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103

GPSR

Greedy Forwarding Perimeter Forwarding

greedy fails

have left local maximagreedy works greedy fails

Combining Greedy and Planar Perimeters (cont.)

GPSR packet header fields used in perimeter mode forwarding

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Field Function

DLp

Lf

e0

M

Destination LocationLocation Packet Entered Perimeter ModePoint on xV Packet Entered Current FaceFirst Edge Traversed on Current FacePacket Mode: Greedy or Perimeter

Combining Greedy and Planar Perimeters (cont.)

Lp

Lf

e0

D

xIf forwarding node to D < Lp to D, returns a packet to greedy mode

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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.

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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.

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Chapter 4.5QoS 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.

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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

P1(1)P1(1)

P4(1)

P3(1)

P2(2)

116

P3(

1)

Ticket-Based Probing (cont.)

S D

A

B

C

E

3 3

3

32

2

2

6

5

x

Demand = 3

117

Ticket-Based Probing (cont.)

S D

A

B

C

E

3 3

3

22

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

22

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 DT2

T1

S DT2

T1

x

Ticket-Based Probing (cont.)

S D

A

B

C

E

4 3

3

24

2

3

6

5

xDemand = 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.

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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.

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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.

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