Chapter 4 Routing Protocols (Part II)hscc.cs.nthu.edu.tw/~sheujp/lecture_note/10wsn/wsn042.pdf · 2015. 8. 14. · 4.1 Routing Challenges and Design Issues in WSNs 4.2 Flat Routing

Chapter 4

Routing Protocols

(Part II)(Part II)

1

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� 4.6 Data Aggregation and Convergecast

� 4.7 Data Centric Networking

� 4.8 ZigBee

� 4.9 Conclusions

2

Chapter 4.6

Data Aggregation and ConvergecastData Aggregation and Convergecast

3

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

4

Outline






5

4.6.1 The Impact of Data Aggregation

� Impact of Data Aggregation in Wireless Sensor Networks

(Krishnamachari, Estrin, & Wicker, 2002)

� Aggregation in Sensor Networks

� Theoretical Results on Aggregation

� Aggregation Techniques

� Performance study� Performance study

6

Aggregation in Sensor Networks

� Traditional Address-Centric routing

� IP address routing

� Not suitable in large scale sensor networks

� Data-Centric Routing

� Content-based routing

� Enhance the data aggregation opportunity� Enhance the data aggregation opportunity

7

Source 1 Source 2

A B

Sink

Source 1 Source 2

A B

Sink

a) Address-Centric (AC) Routing b) Data-Centric (DC) Routing

1

1

2

2

21

1+2

DataAggregation

Theoretical Results on Aggregation

� Let there be k sources located within a diameter X, each a distance di from

the sink. Let NA and ND be the number of transmissions required with AC

and optimal DC protocols, respectively.

1. The following are bounds on ND:

( 1) min( )

min( ) ( 1)

D i

D i

N k X d

N d k

≤ − +

≥ + −

2. Asymptotically, for fixed k, X, as d = min(di) is increased,

3. Although the problem is NP-hard in general, the optimal data aggregation

tree can be formed in polynomial time when the sources induce a

connected subgraph on the communication graph.

min( ) ( 1)D i

N d k≥ + −

1lim D

d

A

N

N k→∞

=

8

Aggregation Techniques

� In general the formation of the optimal aggregation tree is NP-

hard. Some suboptimal DC routing heuristics as follows:

� Center at Nearest Source (CNSDC)

� All sources send the information first to the source nearest to the sink, which

acts as the aggregator.

� Shortest Path Tree (SPTDC)

Opportunistically merge the shortest paths from each source wherever they � Opportunistically merge the shortest paths from each source wherever they

overlap.

� Greedy Incremental Tree (GITDC)

� Start with path from sink to nearest source. Successively add next nearest

source to the existing tree.

� Address Centric (AC)

� No aggregation, distinct shortest paths from each source to sink.

9

Performance Study

Event-Radius model Random Sources model

10

Performance Study (cont.)

Energy Costs

Event-Radius model Random Sources model

11

Conclusions

� Data aggregation can result in significant energy savings for a

wide range of operational scenarios.

� The gains from aggregation are paid for with potentially higher

delay. It should be possible to design routing algorithms for

sensor networks in which this tradeoff is made explicitly.

12

Reference

� B. Krishnamachari, D. Estrin, and S. B. Wicker, "The impact of

data aggregation in wireless sensor networks," In Proceedings

of the 22nd International Conference on Distributed

Computing Systems Workshops (ICDCSW'02), pp. 575-578,

Vienna, Austria, July 02-05 2002.

13

Outline






14

4.6.2 Data Mules

� Data MULEs: Modeling a Three-tier Architecture for Sparse

Sensor Networks (Shah, Roy, Jain, & Brunette, 2003)

� Three-tier architecture

� Data MULEs approaches

15

Three-tier Architecture

Access points – ample resources

Mobile nodes – renewable resources for

intermittent connectivity

Source nodes – limited resources

16

� A top tier of WAN connected devices

� A middle tier of mobile transport agents

� A bottom tier made of fixed wireless sensor nodes

Data MULEs approach

� Data Mules

� Exploit mobile nodes (called MULEs)

� MULEs collect data when near sensor

� Transfer data to an access point when close

17

access pointsensor

Discussions

� Benefits

� Energy efficient

� Short distance communication

between sensor and MULE

� Scalable

� Addition of sensors or MULEs

requires no configuration

� Limitations

� No guarantees on data delivery

� MULEs may lose data

� MULEs may not arrive at a

sensor

� MULEs may not arrive at an

access pointrequires no configuration

� Simple

� Least functionality in sensors

� No forwarding, no global

discovery

access point

18

Reference

� R. C. Shah, S. Roy, S. Jain, and W. Brunette, "Data MULEs:

modeling and analysis of a three-tier architecture for sparse

sensor networks," Ad Hoc Networks, Volume 1, Issues 2-3, pp.

215-233, September 2003.

19

Outline






20

Convergecasting in WSN

� WSN are mainly used for monitoring

� Monitoring involves data collection and request dissemination

� Convergecasting� Process of data collection from all or a set of sensors in the network

towards the base station (Many to one communication)

� Energy and latency minimization is required for WSNs

21

Convergecasting

� Route construction plays a major role during convergecasting

� Criterion for route construction

� Energy consumption

� Latency incurred

� Choice of MAC layer – since traffic is many to one

2222

Collisions

� Results in packet loss

� Need reliability, use retransmissions

� Retransmission increases energy consumption and latency

� Avoided by using a contention based or contention free MAC

protocolBS

23

1

BS

2

3 4 5

6

CollisionCollision CollisionCollision Coverage Area

or

Sensing Range

Data

Data

Energy & LatencyEnergy

� Energy consumed at a node is used for� Running the transceiver circuitry for transmitting a bit (Etrx)

� Amplifying a bit of data to be transmitted (Eamp)

It depends on the transmission distance

BS BS

E = 4nj Eamp= 4nj

24

3 hops

2 hopsP1

P2

P1

P2

n n

Eamp= 4nj

Eamp= 4nj

Eamp= 5nj

Eamp= 4nj

Eamp= 5nj

Energy consumed for running transceiver

To transmit k data bits from n to BS

P1: 3 * Etrx * k

P2: 2 * Etrx * k

Amplification energy consumed for

transmitting k data bits from n to BS

P1: (4nj + 4nj + 5nj) * k = 13 * k nj

P2: (4nj + 5nj ) * k = 9 * k nj

P1 and P2: paths

BS: Base Station

Energy & Latency (cont.) Energy

� Transceiver startup time

� Frequently switching the transceiver on leads to higher energy wastage

� Aggregation reduces packet header overhead

Time-slot =1 Time-slot = 2

Time-slot = 3

BSBS – Base Station

Aggregation reduces

transmitter startup energy

wastage

Slots allocated to children should

reduce cumulative startup time

of parent’s receiver 1

4 5

2

3

25

Energy & Latency (cont.) Latency

� Time taken to gather data at the base station

� Latency = No. of time-slots × Length of one-slot

� Balanced tree helps in reducing total number of time-slots and length of time-slots

Unbalanced Tree Balanced Tree

BS : Base StationBS

26

Number of slots = 4

Length of each slot = 4 packets

Latency = 16 units

Number of slots = 3

Length of each slot = 3 packets

Latency = 9 units

BS : Base Station

t =1 t = 2 t = 3

t = 4t = 1

BS

1

4 5

2

3

t =1 t = 2 t =1

t = 2t = 3

BS

1

4 5

2

3

Energy & Latency (cont.) Summary

� Energy and latency minimized by avoiding collisions

� Energy consumption can also be minimized by

� Reducing the number of hops

� Choosing path that minimizes amplification energy

� Reducing energy wastage due to transceiver startup time by performing

data aggregationdata aggregation

� Latency minimization by building a balanced routing tree

27

Main Procedures

� Assumptions

� Etrx < Eamp

� One transceiver per node

� Nodes have maximum transmission range (MEamp)

� Clock synchronization mechanism exists

� Builds the tree and allocates channel for the nodes� Builds the tree and allocates channel for the nodes

� Allocates channel for two different convergecast patterns

� Synchronous: Used for realtime data. Enables aggregation. Therefore

parent transmits after it receives from children (parent time-slot > child

time-slots)

� Asynchronous: Used for non-realtime data. Enables aggregation only if

data does not depend in time.

28

Synchronous Convergecast

� Data collection starts from leaf nodes

� Each parent waits for data from its children before sending its data

� Reordering based on timestamp is not necessary at base station

2 3

BS BS

<4, 1> <3, 1>

Note: Weights indicate the

amplification energy expended

Network

13

3

3

1.4

2.2

2

1 2

3

4

5

Convergecast Tree

1 2

3

4

5

<4, 1> <3, 1>

<3, 1>

<2, 1>

<1, 1>


to transmit a data bit over that

link

29 <t, c>: a tuple of time-slot t and CDMA code c

Asynchronous Convergecast

� Data collection takes place at independent and not interfering parts of the

network

� Reordering necessary at base station

� Latency will be low

Network 2 3

BS

Convergecast Tree

BS

Note: Weights indicate the Network

1

2 3

3

3

3

1.4

2.2

2

1 2

3

4

5

1 2

3

4

5

<1, 1>

<2, 1>

<3, 1>

<1, 1><2, 1>

Note: Weights indicate the


to transmit a data bit over that

link

30

Channel Allocation Criterion 1

� Each node has one transceiver

� Therefore a parent with two children cannot receive from both

of them at the same time using two different codes

� Therefore children transmit at different time instants

ParentX = ParentYParentX = ParentY

X Y

ParentX = ParentY

X Y

ParentX = ParentY

<t1,c1> <t2,c1><t1,c1> <t1,c2>

31


� Avoid exposed terminal problem

ParentX ParentYTransmission

range

If X and Y useCollisionCollision CollisionCollision

X Y

If X and Y use

the same channel

If X and Y transmit

at different time-slots

If X and Y transmit

using different CDMA

codes

32


� Parent cannot receive the same time it is transmitting

� Therefore, we have parent time-slot ≠ child time-slot

ParentParentxParentParentx

X

ParentX

<t1,c1>

<t1,c2>

X

ParentX

<t1,c1>

<t2,c1>

33

Algorithm

� Build the tree and allocate the channel (is a tuple of time-slot tand CDMA code c, <t, c>)

� Tree constructed in a top down manner

� Use channel allocation criteria defined earlier

� Additional criterion for synchronous convergecasting� child time-slot < parent time-slot

� Since tree construction is top down, it is not possible to allocate � Since tree construction is top down, it is not possible to allocate a valid time-slot for children

� Channel allocation in two phases� Phase I

� Construct tree and allocate channel in increasing order of time-slots

� Phase II� Reverse mapping of time slots to enable synchronous convergecast

34

CTCCAA: Phase I (Tree Construction)

� Construct the tree by reducing number of hops and then choose the path that consumes minimum amplification energy� Reason: Etrx < Eamp since transmission range of sensors are small

� Start constructing the tree with Base station (BS) as the root node

� Maintain a possible parent and a possible child list� Possible Parent List (PPL) = {All nodes recently added to the tree} � Possible Parent List (PPL) = {All nodes recently added to the tree}

� Possible Children List (PCL) = {x | there exists y ∈ PPL such that Eamp(x,y) < MEamp}

� Parent selection� For all x ∈ PCL parentx = arg Minforall y ∈ PPL Eamp(x,y)

� If for all x ∈ PCL parentx ≠ null, then copy PCL to PPL

35

Example: Phase I

Initially current level is 0

PPL = {BS}

PCL = {1, 2}

Since BS is the only possible

parent both 1 and 2 choose 1

2 3

BS

1 2

Weights on links indicate the


to transmit a data bit

parent both 1 and 2 choose

BS as their parent.

13

3

3

1.4

2.2

23

4

5

36

CTCCAA: Phase I (Channel Allocation)

� Use a combination of CDMA codes and time-slots

� Allocate children a time-slot that is greater than parent (will do

reverse mapping in phase II)

37

Example: Phase I

Weights on links indicate the


to transmit a data bitThis example assumes channel to be

divided over time.


PPL = {BS}

BS

1<1, 1>PPL = {BS}

PCL = {1, 2}1

2

3

4

5

<1, 1><2, 1>

13

3

3

1.4

2.2

2

38

Example: Phase I

Weights indicate the

amplification energy

BS

1


PPL = {1, 2}

PCL = {3, 4, 5}

<1, 1> 12

3

4

5

<1, 1>

<3, 1>

<4, 1>

<2, 1>

<2, 1>

13

3

3

1.4

2.2

2

39

CTCCAA: Phase II

� Only executed for synchronous convergecast

� Use maximum time-slot (Maxts) allocated in the network

� Actual time-slot = Maxts – allocated time-slot

BSMaxts = 4

40

<1, 1> 2

3

4

5

<2, 1>

<4, 1>

<3, 1>

<2, 1> <1, 1>

<4, 1>

<3, 1>

<2, 1>

<3, 1>1

Example

� This shows the advantage of divided channel over time and

CDMA Codes. CDMA codes help in reducing latency by

increasing time-slot reuse

BS

1 2

3

4

5

<3, 2><2, 2>

<1, 1>

<2, 1>

<1, 2>

41

Conclusions

� Convergecast will be preceded by broadcast in monitoring applications

� Measured energy and latency incurred during convergecastover a broadcast tree and a tree constructed by CTCCAA

� Similarly we measured energy and latency for broadcasting over both the trees

42

Reference

� V. Annamalai, S.K.S. Gupta, and L. Schwiebert, “On tree-

based convergecasting in wireless sensor networks,” IEEE

Wireless Communications and Networking, vol. 3, pp.1942-

1947 , March 2003.

43

Outline






44

Outline

� 4.6.4. Distributed time-optimal scheduling for convergecast

� 4.6.4.1. System model and Assumptions

� 4.6.4.2. Convergecast in tree networks

� 4.6.4.2.1. Linear Networks

� 4.6.4.2.2. Multi-line Networks

� 4.6.4.2.3. Tree Networks

� 4.6.4.2.4. Sleep schedule for energy conservation

� 4.6.4.3. Convergecast in general networks

� 4.6.4.4. Convergecast in other scenarios

45

4.6.4. Distributed Time-optimal Scheduling for

Convergecast

� Convergecast is a typical many-to-one communication pattern

in sensor network applications.

� In convergecast many, or all nodes in the network send data to

a base station during a relatively short time period.

� Using CSMA MAC layer, the convergecast latency incurred by

radial coordination is far from the optimal.radial coordination is far from the optimal.

� TDMA schedule such that the entire convergecast can be

completed in minimal number of timeslots.

46

4.6.4.1. System Model and Assumptions

� Assumptions

� the nodes and the associated base station are static

� the nodes (including the base station) cannot transmit and receive at the

same time

� the bandwidth of every wireless link in the network is assumed to be the

same

the network connectivity is fixed over time� the network connectivity is fixed over time

� the maximum length of a packet is fixed

� the drift in the clock of a node is bounded all the time.

47

4.6.4.2. Convergecast in Tree Networks

� There are three considers of sensor nodes and propose an

optimal convergecast scheduling algorithm.

� linear networks

� multi-line networks

� tree networks

48

4.6.4.2.1. Linear Networks

� We define the following states that a node can be in each

timeslot during the convergecast

� R: The node may receive from a neighboring node.

� T: The node can transmit.

� I : The node neither transmits nor receives.

49

Linear Networks (cont.)

A Linear Network

Convergecast Schedule

50

4.6.4.2.2. Multi-line Networks

� Convergecast scheduling algorithm for multi-line networks is

to schedule transmissions parallelly along multiple branches

Next timeslot

R T

51

State transition for convergecast scheduling

Next timeslotNext timeslot

R T

I

� The network consists of branches A, B, C and D (A < B < C <

D) with 3, 2, 2, and1 nodes

Multi-line Networks (cont.)

A multi-line network

52

Multi-line Networks (cont.)

Convergecast schedule for multi-line networks

53

The Pkts Left field is used to track the number of packets remaining in each branch.

The Last Slot field shows the last timeslot in which a branch has forwarded a

packet to the base station (two less than the last active timeslots).

4.6.4.2.3. Tree Networks

� Convergecast scheduling algorithm for tree networks is based

on the observation that a tree network can be reduced to a

multi-line network with each line represented as a combination

of linear branches of nodes.

54

Tree Networks (cont.)

Reduction of a tree network into linear branches.

(a) (b)

55

4.6.4.2.4. Sleep Schedule for Energy

Conservation

� Energy spent in sleep state is negligible

� At most 3N timeslots are required to finish the convergecast

� The total energy consumption in the network is

Joules2

)1(3 eNN +

� Hence, we conclude that the sleep schedule results in about

50% energy conservation in linear networks.

56

4.6.4.3. Convergecast in General Networks

57

4.6.4.4. Convergecast in Other Scenarios

� We show that our convergecast scheduling algorithm is

applicable even when these assumptions do not hold.

� Base station initiated convergecast

� Nodes with multiple packets

� Non-ideal radio characteristics

58

Convergecast in Other Scenarios (cont.)

Base station initiated convergecast in linear networks.

59

Convergecast in Other Scenarios (cont.)

(a)

(b)

Multi-packet network.

Non-ideal radio propagation characteristics.

60

Conclusions

� A minimal time distributed convergecast scheduling algorithm

for sensor networks.

� The optimal convergecast schedule consists of

3N -3 timeslots, where N is the number of nodes in the network.

� More than 50% of the energy can be saved by using the

proposed sleep schedule.proposed sleep schedule.

61

Reference

� S. Gandham, Y. Zhang, and Q. Huang, “Distributed time-

optimal scheduling for convergecast in wireless sensor

networks,” Computer Networks, vol. 52, pp. 610-629, 2008.

62

Chapter 4.7

Data centric networkingData centric networking

63

Outline

� 4.7.1 Data centric routing

� 4.7.2 Data-centric storage

� 4.7.2.1 One-dimensional data storage

� 4.7.2.2 Multi-dimensional data storage

� 4.7.2.3 Hierarchical data storage

64

4.7.1 Data Centric Routing

� A central querier/data sink (or collection of queriers/sinks)

issues queries that sources in the network respond to. Due to

energy constraints it is desirable for much of the data

processing to be done in-network, and this has led to the

concept of data centric information routing, in which the

queries and responses are for named data.queries and responses are for named data.

� A sensor node is not an identity (address)

� Content based and data centric

� Where are nodes whose temperatures will exceed more than 10 degrees for

next 10 minutes?

� Tell me in what direction that vehicle in region Y is moving?

� Give me periodic reports about animal location in region A every 30 seconds.

65

Data Centric Routing (cont.)

� Depending on the applications, there are likely to be different

kinds of queries in these sensor networks.

� The types of queries can be categorized in many ways, for

example:

� Continuous queries, which result in extended data flows (e.g. “Report

the measured temperature for the next 7 days with a frequency of 1 the measured temperature for the next 7 days with a frequency of 1

measurement per hour”) versus One-shot queries, which have a simple

response (e.g. “Is the current temperature higher than 70 degrees?”)

� Aggregate queries, which require the aggregation of information from

several sources (e.g. “Report the calculated average temperature of all

nodes in region X”) versus Non-aggregate Queries which can be

responded to by a single node (e.g. “What is the temperature measured by

node x?”)

66


� Complex queries, which consist of several nested or batched sub-queries

(e.g. “What are the values of the following variables: X, Y, Z?”) versus

simple queries, which have no sub-queries (e.g. “What is the value of the

variable X?”)

� Queries for replicated data, In which the response to a given query can

be provided by many nodes (e.g. “Is there at least one target in the

area?”) and queries for unique data, in which the response to a given area?”) and queries for unique data, in which the response to a given

query can be provided only by one node.

67


� SPIN

� One-shot interactions

� 3-stage handshake protocol

� ADV – new data advertisement

� REQ – request for data

� DATA – data messageADV ADVREQDATA

68

ADVREQDATA

ADV

ADV

ADV

ADV

ADV

REQ

REQ

REQ

REQ

DATA

DATA

DATA

DATA

The SPIN protocol


� Directed Diffusion

� Repeated interactions

A simplified schematic for directed diffusion

69

4.7.2 Data-centric Storage

� Data centric storage

� Data is stored inside the network.

� All data with the same name (or data range) will be stored at the same

sensor network location

� E.g. an elephant sighting.

� Why data centric storage?� Why data centric storage?

� Energy efficiency

� Robustness against mobility and node failures

� Scalability

70

4.7.2

Data-centric storageData-centric storageOne-dimensional data storage

71

One-dimensional Data Storage

� Data-Centric Storage in Sensornets with GHT, a Geographic

Hash Table(GHT [Ratnasamy et al. 2003])

� Data Storage and Retrieval

� Perimeter Refresh Protocol

� Structured Replication

72

Data Storage and Retrieval

� GHT

� Put(k,v)-stores v (observed data)

according to the key k

� Get(k)-retrieve whatever value is

associated with key k

� Hash function

Hash the key in to the

(12,24)data

� Hash the key in to the

geographic coordinates

� Put() and Get() operations on the

same key “k” hash k to the same

location

user

queryresponse

Hash (‘elephant’)=(12,24)

Put (“elephant”, data) Get (“elephant”)

Hash (‘elephant’)=(12,24)

An example for GHT

73

Perimeter Refresh Protocol

� Assume key k hashes at

location L

� A is closest to L so it

becomes the home node

E

D

Replica

Replica

F

B

D

A

C

L

home

74

Structured Replication

� Augment event name with

hierarchy depth

� Given root r and given

hierarchy depth d

� Compute 4d – 1 mirror images

of r

(100, 100)(0, 100)

of r

(0, 0) (100, 0)

root point

level 1 mirror points

level 2 mirror points

Example of structured replication

with a 2-level decomposition

75

Conclusions

� Data centric storage entails naming of data and storing data at

nodes within the sensor network

� GHT uses Perimeter Refresh Protocol and structured

replication to enhance robustness and scalability

� DCS is useful in large sensor networks and there are many

detected events but not all event types are Queried detected events but not all event types are Queried

76

4.7.2

Data-centric storageData-centric storageMulti-dimensional data storage

77

Multi-dimensional Data Storage

� Multi-Dimensional Range Queries in Sensor Networks (DIM

[Li et al. 2003])

� Building Zones

� Data Insertion

� Query Propagation

78

Building Zones

� Divide network into zones.

� Each node mapped to one

zone.

� Encode zones based on

division.

� Each zone has a unique 6

5

1110

1111

43

21

1100111

0110

010

L∈∈∈∈[1/2, 1)L∈∈∈∈[0, 1/2)

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

code.

� Map m-d space to zones.

� Zones organized into a

virtual binary tree.

78

10

90001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[0, 1/4) L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

L: Light, T: Temperature

79

Data Insertion

� Encode events

� Compute geographic

destination

� Hand to GPSR

� Intermediate

nodes can refine

1110

1111

L∈∈∈∈[1/2, 1)

1

110

L∈∈∈∈[0, 1/2)

0111

0110

010

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

2

34

5

6

E1= <0.8, 0.7>

nodes can refine

the destination

estimation

L∈∈∈∈[0, 1/4)

0001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

987

10

Store E1


80

Query Propagation

� Split a large query into smaller subqueries.

� Encode each subquery.

� Process subqueries separately, resolving locally or

1110

1111

L∈∈∈∈[1/2, 1)

1

110

L∈∈∈∈[0, 1/2)

0111

0110

010

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

2

34

5

6Q = <.75-1, .5-.75>

Q12= <.75-1, .75-1>Q11= <.5-.75, . 5-1>

locally or forwarding to other nodes based on their codes.

L∈∈∈∈[0, 1/4)

0001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

987

10

Q10= <.75-1, .5-.75>

Q1= <0.5-1, 0.5-1>


81

Conclusions

� DIM resolves multi-dimensional range queries efficiently.

� Work that still needs to be done

� Skewed data distribution

� These can cause storage and transmission hotspots.

� Existential queries

� Whether there exists an event matching a multi-dimensional range.

� Node heterogeneity� Node heterogeneity

� Nodes with larger storage space assert larger-sized zones for themselves.

82

4.7.2

Data-centric storageData-centric storageHierarchical data storage

83

Hierarchical Data Storage

� Load balance and Efficient Hierarchical Data-Centric Storage

in Sensor Networks (HVGR [Zhao et al. 2008])

� Constructing the hierarchical architecture

� Storage load balancing

� Data Storage and retrieval

84

Constructing Hierarchical Architecture

� Assumptions

� Large

� Static

� density

•Each node knows the shortest root

•Each node knows the shortest

path to their first level landmark

85

Constructing Hierarchical Architecture (cont.)

� Assumptions

� Large

� Static

� density


L2

L3

root

• Nodes know the shortest

path to their second level landmark

•Termination: the subregion only

contains the owner landmark and

the landmark’s one-hop neighbors.


path to their first level landmark

L1

L4

L5

L4.1

L4.2

L4.3

86

Storage Load Balancing

N2N3

L2:[0.1,0.3)L3:[0.3,0.6)

N: the total number node in WSN

Ni: the number node in Ni’s subregion

N=50

0.1

0.2 0.3

N1

N4

N5

L1: [0,0.1)

L4:[0.6,0.8)L5:[0.8,1)

L4.1:[0.6,0.67)

L4.2:[0.67,0.73) L4.3:[0.73,0.8)

Node A table:

L1st:{L1:(0,0.1],L2 :(0.1,0.3],L3:(0.3,0.6],

L4:(0.6,0.8],L2 :(0.8,1]}

L2nd:{L4.1:(0.6,0.67], L4.2:(0.67,0.73],

L4.3:(0.73,0.8]}

L3rd:{L4.2.1: (0.67,0.73],

L4.2.2: (0.70,0.73]}

A

0.1

0.2

0.2

87

Data Storage and Retrieval

L1st:{L4 :(0.6,0.8],…}

Source(0.68)

Query (0.68)

L2 L3

Query (0.68)

L1st:{L4:(0.6,0.8],…}

L2nd:{L4.2:(0.67,0.73],…}

L1

L4L5

L4.1

L4.2L4.3

A

DL4.2.1

L4.2.2

L1st:{L4 :(0.6,0.8],…}

L2nd:{L4.2:(0.67,0.73],…}

L3rd:{L4.2.1: (0.67,0.73],L4.2.2: (0.70,0.73]}

88

Conclusions

� HVGR is very scalable, as the initialization overhead and

routing table size of each node is O(logN).

� HVGR design a simple hash mechanism so that HVGR can

provide a well load balanced data-centric storage system.

89

References

� S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin, and F. Yu, “Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table,” in Journal of Mobile Network Applications, vol. 8, no. 4, pp. 427-442, 2003.

� X. Li, Y. J. Kim, R. Govidan, and W. Hong, "Multi-Dimensional Range Queries in Sensor Networks," in Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03), Los Embedded Networked Sensor Systems (SenSys'03), Los Angeles, CA, USA, pp.63-75, Nov. 2003.

� Y. Zhao, Y. Chen, and S. Ratnasamy, "Load Balanced and Efficient Hierarchical Data-Centric Storage in Sensor Networks," in Proceedings of the 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, San Francisco, California, USA, pp.560-568, June 2008.

90

Chapter 4.8

ZigBeeZigBee

91

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.3 The Application Layer

92

Outline




� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

93

The ZigBee Standard

� ZigBee is a low cost, low power, low complexity, and low data rate wireless

communication technology at short range. Based on IEEE 802.15.4, it is

mainly used as a low data rate monitoring and controlling sensor network

Applications

802.15.4

Zigbee

Specification

Application Framework

Network & Security

Medium Access Control (MAC) Layer

Physical (PHY) Layer

Application

Zigbee stack

Hardware

94

Zigbee Frame Format

� General frame format

Octets: 2 2 2 1 1 Variable

Frame Control Destination Source Radius Sequence Frame Frame Control Destination Source

Address

Radius Sequence

Number

Frame

Payload

Routing Fields

NWK Header NWK

Payload

95

Zigbee Frame Format (cont.)

� General frame format

Frame control field

Frame type setting

96


� Command frame format

Octets: 2 1 Variable

Frame control Routing fields NWK command

identifier

NWK command

payload

NWK header NWK payload

Command frame format

NWK header NWK payload

Frame type

97


� RREQ command

RREQ command

payload format

Command options field

98


� RREP command

RREP command

payload format

99

Outline








100

The Network Layer

� ZigBee identifies three device types

� The ZigBee coordinator (one in the network) is an FFD managing the

whole network

� A ZigBee router is an FFD with routing capabilities

� A ZigBee end-device corresponds to a RFD or FFD acting as a simple

device

� The ZigBee network layer supports three types of network

configurations:

� Star topology

� Tree topology

� Mesh topology

101

The Network Layer (cont.)

ZigBee coordinator ZigBee router ZigBee end device

(a) Star network (b) Tree network (c) Mesh network

102

Outline








103

Network Formation and Address Assignment

� Before forming a network, the coordinator determines

� Maximum number of children of a router (Cm)

� Maximum number of child routers of a router (Rm)

� Depth of the network (Lm)

� Note that a child of a router can be a router or an end device, so � Note that a child of a router can be a router or an end device, so

Cm ≥ Rm

� The coordinator and routers can each have at most Rm child

routers and at least Cm − Rm child end devices

104


(cont.)

� For the coordinator, the whole address space is logically

partitioned into Rm + 1 blocks

� The first Rm blocks are to be assigned to the coordinator’s

child routers and the last block is reserved for the coordinator’s

own child end devices

� From Cm, Rm, and Lm, each router computes a parameter � From Cm, Rm, and Lm, each router computes a parameter

called Cskip to derive the starting addresses of its children’s

address pools

( )( )

1

1 1 ,if 1

1Otherwise,

1

Lm d

Cm Lm dRm

Cskip d Cm Rm Cm Rm

Rm

− −

+ × − −=

= + − − ×

−

105


(cont.)

� The coordinator is said to be at depth d = 0, and d is increased

by one after each level

� Address assignment begins from the ZigBee coordinator by

assigning address 0 to itself

� If a parent node at depth d has an address Aparent , the n-th child

router is assigned to address router is assigned to address

� Aparent + (n − 1) × Cskip(d) + 1

� n-th child end device is assigned to address

� Aparent + Rm × Cskip(d) + n

106


(cont.)

Addr = 12

Addr = 8

Addr = 9

Addr = 10

Cm = 5

Rm = 4

Lm = 2

A2

Addr = 7

Cskip = 1


B1

Addr = 25

Addr = 0

Cskip = 6

A4

Addr = 19

Cskip = 1

A3

Addr = 13

Cskip = 1

Addr = 24

A1

Addr = 1

Cskip = 1

Addr = 6

Addr = 3

Addr = 2

107

Outline








108

ZigBee Routing protocol

� In a ZigBee network, the coordinator and routers can directly

transmit packets along the tree

� When a device receives a packet, it first checks if it is the

destination or one of its child end devices is the destination

� If so, this device will accept the packet or forward this packet

to the designated child. Otherwise, it forwards the packet to its

parent

109

ZigBee Routing protocol (cont.)

� Assume that the depth of this device is d and its address is A.

This packet is for one of its descendants if the destination

address Adest satisfies A < Adest < A+ Cskip(d − 1), and this

packet will be relayed to the child router with address

( )( )

11

destA AA A Cskip d

− += + + ×

� If the destination is not a descendant of this device, this packet

will be forwarded to its parent

( )

( )( )

11

dest

r

A AA A Cskip d

Cskip d

− += + + ×

110

ZigBee Routing protocol (cont.)

Cm = 6

Rm = 4

Lm = 3

Addr = 125

Addr = 30

Addr = 126

Addr = 92

Addr = 63

Cskip = 7

Addr = 64

Cskip = 1

Addr = 1

( )

( )( )

11

dest

r

A AA A Cskip d

Cskip d

− += + + ×


Addr = 31

Addr = 38

Addr = 1

Cskip = 7

Addr = 32

Cskip = 7Addr = 33

Cskip = 1

Addr = 40

Cskip = 1

Z

? ?

( )( )1A A Cskip d

Cskip d= + + ×

BC

A < Adest < A+ Cskip(d − 1)

A

111

Outline








112

Route Discovery

Field Name Description

Destination Address 16-bit network address of the destination

Next-hop Address 16-bit network address of next hop towards destination

Entry Status One of Active, Discovery or InactiveEntry Status One of Active, Discovery or Inactive

Routing Table in ZigBee

113

Route Discovery (cont.)

Field Name Description

RREQ ID

(route request)

Unique ID (sequence number) given to every RREQ

message being broadcasted

Source Address Network address of the initiator of the route request

Sender Address Network address of the device that sent the most recent

lowest cost RREQ

Forward Cost The accumulated path cost from the RREQ originator to

the current device

Residual Cost The accumulated path cost from the current device to the

RREQ destination

Route Discovery Table

114


RREQ message

Create RDT entry and

record fwd path cost

RDT entry

exists for this

RREQ ?

NoYes

Is

RREQ for local

node or one of

end-device

children ?

Create RT entry

(Discovery_Underway)

And rebroadcast RREQ

Update RDT entry with

better fwd path cost

Does

RREQ report

A better fwd

path cost ?

Send RREPDrop RREQ

Yes

No

Yes

No

The RREQ processing

115


A

C

BDiscard route

request

route reply

S

D

T

Unicast

Broadcast

Without routing capacity

116

Outline








117

The Application Layer

� A ZigBee application consists of a set of Application Objects (APOs) spread over

several nodes in the network

� The ZigBee Device Object (ZDO) is a special object which offers services to the

APOs

� The Application Sub layer (APS) provides data transfer services for the APOs and

the ZDO

118

References

� P. Baronti, P. Pillai, V. Chook, S. Chessa, and F. Gotta, A. andFun Hu. Wireless

sensor networks: a survey on the state of the art and the 802.15.4 and zigbee

standards. Communication Research Centre, UK, May 2006.

� J. Bruck, J. Gao and A. A. Jiang, “MAP: Medial Axis Based Geometric Routing in

Sensor Network,” in Proceedings of ACM MobiCom, 2005.

� Q. Fang, J. Gao, L. Guibas, V. de Silva, and L. Zhang. GLIDER: Gradient landmark-

based distributed routing for sensor networks. In Proc. of the 24th Conference of the

IEEE Communication Society (INFOCOM’05), March 2005.IEEE Communication Society (INFOCOM’05), March 2005.

� B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient

coordination algorithm for topology maintenance in ad hoc wireless networks. In

International Conference on Mobile Computing and Networking (MobiCom 2001),

pages 85–96, Rome, Italy, July 2001.

� Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for ad

hoc routing. In Proceedings of the ACM/IEEE International Conference on Mobile

Computing and Networking, pages 70–84, Rome, Italy, July 2001.

119

Conclusions

� Routing in sensor networks is a new area of research, with a

limited but rapidly growing set of research results

� We highlight the design trade-offs between energy and

communication overhead savings in some of the routing

paradigm, as well as the advantages and disadvantages of each

routing technique

120

routing technique

� Overall, the routing techniques are classified based on the

network structure into four categories: flat, hierarchical, and

location-based routing, and QoS based routing protocols.

� Although many of these routing techniques look promising,

there are still many challenges that need to be solved in sensor

networks

Documents

Chapter 4 Routing Protocols (Part II)hscc.cs.nthu.edu.tw/~sheujp/lecture_note/10wsn/wsn042.pdf · 2015. 8. 14. · 4.1 Routing Challenges and Design Issues in WSNs 4.2 Flat Routing