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Survey on Routing in Sensor Networks. Lichun Bao Computer Science Department University of California, Irvine. Sensor Node Components. Sensing Processing Transmission Mobilizer position finding system Power units. Purposes of Sensor Networks.

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Survey on routing in sensor networks

Survey on Routing in Sensor Networks

Lichun Bao

Computer Science Department

University of California, Irvine


Sensor node components
Sensor Node Components

  • Sensing

  • Processing

  • Transmission

  • Mobilizer

  • position finding system

  • Power units


Purposes of sensor networks
Purposes of Sensor Networks

  • Information processing: distributed computing, target field imaging, weather monitoring

  • Detection:

    • intrusion detection, security and tactical surveillance

    • detecting ambient conditions such as temperature, movement, sound, light, or the presence of certain objects, inventory control

  • Disaster management.


Components of routing
Components of Routing

  • Control function: energy-efficient route discovery

  • Data-forwarding functions: relaying of data from the sensor nodes to the BS

  • Addressing scheme

    • Content-based

    • Topology-based

      • Subnetting

    • Location-based


Address centric routing ac

source 2

source 1

source 2

source 1

source 2

Address Centric Routing (AC)

Temperature Reading

(source 2)

Temperature Reading

(source 1)

Z

B

Give Me The Average Temperature?

( sink )


Data centric routing dc

source 2

source 1

source 2

source 1 & 2

Data Centric Routing (DC)

Temperature Reading

(source 2)

Temperature Reading

(source 1)

Z

B

Give Me The Average Temperature?

( sink )


Requirements on routing
Requirements on Routing

  • Self-organizing

  • Tailor toward specific traffic pattern (BS-centric, application specific, position-aware)

  • Constrained in energy, processing, storage

  • Stationary deployment

  • Redundancy – in-network processing


Classification of routing protocols
Classification of Routing Protocols

  • Networking structure

    • Flat

    • Hierarchical

    • Location-based

  • Network operation

    • Data-reporting model: time-driven (continuous), event-driven, query-driven, and hybrid

      • a diverse mixture of sensors for monitoring temperature, pressure and humidity of the surrounding environment, detecting motion via acoustic signatures, and capturing the image or video tracking of moving objects.

    • multipath-based

    • negotiation-based

    • QoS-based

    • Coherent-based



Design considerations
Design Considerations

  • Node deployment

  • Energy consumption

  • Data Reporting Model

  • Node/Link Heterogeneity

  • Fault Tolerance

  • Scalability

  • Network dynamics

  • Coverage

  • QoS


Overview
Overview

  • What is Data Routing?

    • Directed Diffusion [Estrin, 2000]

  • What is Data Aggregation?

    • Optimal Aggregation

  • Sensor Query Engines

    • IrisNet [Nath, 2003]

  • Using XML in Sensor Networks


Basic mechanisms of spin flat routing
Basic Mechanisms of SPIN : Flat Routing

  • Sensor Protocol for Information via Negotiation

    • Negotiation before exchanging data

    • Negotiation via Meta-Data

    • Resource-aware and resource-adaptive


Spin protocols
SPIN Protocols

  • SPIN-PP: a 3-stage handshake protocol for Point-to-Point media

  • SPIN-EC: SPIN-PP with a low-energy threshold for Energy Conservation.

  • SPIN-BC: a 3-stage handshake protocol for BroadCast media

  • SPIN-RL: SPIN-BC for lossy networks


How does spin pp work
How does SPIN-PP work?

D

A

B

DATA message

E

ADV message

C

REQ message


Spin ec
SPIN-EC

  • SPIN-PP with a simple energy-conservation heuristic

  • When the low-energy threshold is observed, the node reduces its participation in the protocol.


Spin bc

E

E

B

B

ADV

ADV

REQ

A

A

ADV

REQ

C

ADV

C

D

REQ

D

(1)

E

(2)

B

DATA

DATA

A

DATA

DATA

C

D

(3)

SPIN-BC


Spin rl
SPIN-RL

  • Re-request data from the original node or a random neighbor node

  • After sending out a DATA message, the node will wait for a predetermined amount of time before responding to any more request for that piece of data


Performance
Performance

  • In a point-to-point network, SPIN can deliver 60% more data for a given amount of energy than conventional approaches.

  • In a broadcast network, SPIN delivers 80% more data for a given amount of energy than conventional approaches.


Directed diffusion interests propagation

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: E

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: D

Interest Cache

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: F

Sink

Type

F

Temperature

Z

Sound

Interest Propagation Phase

Directed Diffusion (interests propagation)

Temperature A

Temperature B

A

B

C

D

E

F

Give Me The Temperature?

( sink )


Directed diffusion gradient establishment
Directed Diffusion (gradient establishment)

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: C

Temperature A

Temperature B

A

B

Data

Type: Temperature

Location: A

Value: 58

Time: 00:03

Data

Type: Temperature

Location: B

Value: 61

Time: 00:03

C

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: D

D

E

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: F

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Gradient Table - Temperature

Value

Node

Expires

1s

E

10m

2s

Z

1m

Directed Diffusion

Data

Type: Temperature

Location: B

Value: 61

Time: 00:04

Temperature A

Temperature B

A

B

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

C

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: D

D

E

Data

Type: Temperature

Location: A, B

Value: 57, 61

Time: 00:03, 00:03

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: F

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Gradient Table - Temperature

Value

Node

Expires

1s

E

10m

1s

C

10m

2s

Z

1m

Gradient Table - Temperature

Value

Node

Expires

10ms

F

10m

2s

P

1m

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Temperature A

Temperature B

A

B

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

C

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Data

Type: Temperature

Location: B

Value: 61

Time: 00:04

Interest – Reinforce

Type: Temperature

Interval: 10ms

Duration: 10m

Sink: F

Recipient: E

D

E

Interest

Type: Temperature

Interval: 1s

Duration: 10m

Sink: F

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Gradient Table - Temperature

Gradient Table - Temperature

Value

Node

Expires

Value

Node

Expires

10ms

E

10m

10ms

E

10m

2s

Z

1m

1s

C

10m

2s

Z

1m

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Temperature A

Temperature B

A

B

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

C

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Interest – Reinforce

Type: Temperature

Interval: 10ms

Duration: 10m

Sink: E

Recipient: A, B

D

E

Data

Type: Temperature

Location: B

Value: 61

Time: 00:04

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Gradient

Type: Temperature

Interval: 1s

Duration: 10m

Temperature A

Temperature B

A

B

Gradient

Type: Temperature

Interval: 10s

Duration: 10m

C

Gradient

Type: Temperature

Interval: 10s

Duration: 10m

Gradient

Type: Temperature

Interval: 1s

Duration: 10m

D

E

Interest – Negative

Type: Temperature

Interval: 5s

Duration: 10m

Sink: F

Recipient: D

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

Gradient - Negative

Type: Temperature

Interval: 5s

Duration: 10m

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Gradient Table - Temperature

Value

Node

Expires

10ms

E

10m

5s

C

10m

2s

Z

1m

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

Temperature A

Temperature B

A

B

Gradient

Type: Temperature

Interval: 10s

Duration: 10m

C

Gradient

Type: Temperature

Interval: 10s

Duration: 10m

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

D

E

Interest – Negative

Type: Temperature

Interval: 5s

Duration: 10m

Sink: F

Recipient: D

Gradient

Type: Temperature

Interval: 10ms

Duration: 10m

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

F

Give Me The Temperature?

( sink )

Interest & Data Propagation Phase


Directed diffusion
Directed Diffusion

  • Data-centric Routing

  • Quality of Data influences the network

  • Application Specific

    • What to reinforce?

      • Is it time to delivery? Is it energy conservation?

    • Gradients could be custom

      • Could be a threshold of when to send data

  • Robust to network changes

    • Multiple paths are kept

    • Reinforce those paths if necessary


Gradient Table - Temperature

Value

Node

Expires

10ms

E

DEAD

10ms

C

10m

2s

Z

1m

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

Temperature A

Temperature B

A

B

C

Gradient

Type: Temperature

Interval: 10s

Duration: 10m

X

X

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

X

D

E

Interest – Reinforce

Type: Temperature

Interval: 10ms

Duration: 10m

Sink: F

Recipient: D

X

While waiting for data along the broken path node F times out

Gradient

Type: Temperature

Interval: 5s

Duration: 10m

F

Give Me The Temperature?

( sink )

Recovering from broken links


Data aggregation

source 2

source 1

source 2

Aggregates the data before routing it

In this example average would aggregate to:

<sum, count>

source 1 & 2

Data Aggregation

Temperature Reading

(source 2)

Temperature Reading

(source 1)

Give Me The Average Temperature?

( sink )


Rumor routing
Rumor Routing

  • Nodes having observed an event send out agents which leave routing info to the event as state in nodes

  • Agents attempt to travel in a straight line

  • If an agent crosses a path to another event, it begins to build the path to both

  • Agent also optimizes paths if they find shorter ones.

Paper: David Braginsky and Deborah Estrin. Slide adapted from Sugata Hazarika, UCLA


Algorithm basics
Algorithm Basics

  • All nodes maintain a neighbor list.

  • Nodes also maintain a event table

    • When it observes an event, the event is added with distance 0.

  • Agents

    • Packets that carry local event info across the network.

    • Aggregate events as they go.


Agent path
Agent Path

  • Agent tries to travel in a “somewhat” straight path.

    • Maintains a list of recently seen nodes.

    • When it arrives at a node adds the node’s neighbors to the list.

    • For the next tries to find a node not in the recently seen list.

    • Avoids loops

    • -important to find a path regardless of “quality”


Following paths
Following Paths

  • A query originates from source, and is forwarded along until it reaches it’s TTL

  • Forwarding Rules:

    • If a node has seen the query before, it is sent to a random neighbor

    • If a node has a route to the event, forward to neighbor along the route

    • Otherwise, forward to random neighbor using straightening algorithm


Minimum cost forwarding algo mcfa gradient based routing gbr
Minimum Cost Forwarding Algo (MCFA)Gradient-based Routing (GBR)

  • Bellman-ford algorithm relaxing the distance from the BS to all the sensors

  • GBR has adjustable height for a node to divert traffic to other nodes.


Information driven sensor querying idsq constrained anisotropic diffusion routing cadr
Information-driven sensor querying (IDSQ)Constrained anisotropic diffusion routing (CADR)

  • Introduction of two novel techniques IDSQ and CADR for energy-efficient data querying and routing in sensor networks

  • the use of general form of information utility that models the information content as well as the spatial configuration of a network

  • generalization of directed diffusion that uses both the communication cost and the information utility to diffuse data


Problem formulation
Problem Formulation

  • zi (t) = h(x(t), i (t)), (1)

    x(t) is based on parameters of the sensor, i (t) and zi (t) are characteristics and measurement of sensor i respectively.

  • for sensors measuring sound amplitude

  • i = [ xi , σi 2 ] T (3)

  • xi is the known sensor position and σi 2 is the known additive noise variance

  • zi = a / || xi - x ||/2 + wi ,(4)

  • a is target amplitude,  is attenuation coefficient , wi is Gaussian noise with variance σi 2


Define belief as
Define Belief as ...

  • representation of the current a posteriori distribution of x given measurement z1, …, zN: p(x | z1, …, zN)

  • expectation is considered estimate

  • x = xp(x | z1, …, zN)dx

  • covariance approximates residual uncertainty

  •  =  (x - x)(x - x)Tp(x | z1, …, zN)dx


Define information utility as
Define Information Utility as …

The Information Utility function is defined as

Ψ: P(Rd) R

d is the dimension of x

Ψ assigns value to each element of P(Rd) indicating the uncertainty of the distribution

smaller value -> more spread out distribution

larger value -> tighter distribution


A lot more probability theory to follow on idsq and cadr
A lot more probability theory to follow on IDSQ and CADR

  • Incrementally construct sensor set to follow events

  • Mystery to pursue …


Cougar acquire
COUGAR, ACQUIRE

  • View the network as a distributed database

  • Complex queries are further divided into several sub queries


Hierarchical routing
Hierarchical Routing

  • LEACH - Low Energy Adaptive Clustering Hierarchy

    • Use clusterheads to form groups for data aggregation

    • Use TDMA with clusters, and CDMA between clusters.

    • LEACH assumes that all nodes can transmit with enough power to reach the BS if needed and that each node has computational power to support different MAC protocols.


Teen and apteen
TEEN and APTEEN

  • TEEN (Threshold-sensitive Energy Efficient sensor Network protocol)

  • APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network protocol)

  • Hard-threshold for initiating data reporting

  • Soft-threshold for incremental changes


Grid construction
Grid Construction

  • Virtual Grid Architecture routing (VGA):

    • Assumes nodes are arranged in a fixed topology

    • Select Local Aggregators (LAs) and Master Aggregators (MAs) for data collection

  • Two-Tier Data Dissemination (TTDD)

    • Assumes known sensor locations

    • Grids are formed by fixed distance between CHs

    • Other nodes can be put to sleep to save energy


Data aggregation1
Data Aggregation

  • We can reduce the data packet size by aggregating data en route

  • Parallel Process large aggregations of data in the network

  • Scale to large numbers of both sinks and sources


Optimal data aggregation
Optimal Data Aggregation

  • Optimal Data Aggregation is NP-Hard

    • Sub-optimal Algorithms:

      • Opportunistic

        • Just aggregate when possible

      • Center at Nearest Source (CNS) [1]

        • The nearest source acts as the aggregation point

      • Shortest Paths Tree (SPT) [1]

        • Sources send using shortest path if able aggregate

      • Greedy Incremental Tree (GIT) [1]

        • Recursively select the closest source to the tree

      • Clustered Diffusion w/ Dynamic Data Aggregation [2]

        • Hybrid between diffusion and clustering with the ability to aggregate data at the cluster heads

1 – Krishnamachari, Estrin, & Wicker, 2002

2 - Chatterjea & Havinga, 2003


Querying sensor networks
Querying Sensor Networks

  • TAG (Tiny Aggregation) [Madden, 2002]

    • Focus on Aggregation using SQL-like query language

    • Integrated in TinyOS

  • Cornell’s COUGAR [Bonnet, 2000]

    • Individual sensor data

    • Distributed Gathering

    • SQL-like

  • NiagaraCQ [Chen, 2000]

    • Focus on Continuous Queries

    • XML Streams

  • IrisNet [Nath, 2003]

    • Individual sensor data

    • “World-Wide Sensor Network”


Coherent based category
Coherent-based category

  • In non-coherent data processing routing, nodes will locally process the raw data before being sent to other nodes for further processing.

  • In coherent routing, the data is forwarded to aggregators after minimum processing.



Future current directions
Future/current directions

  • all share the common objective of prolonging the network lifetime.

    • Exploit redundancy

    • Tiered architectures (mix of form/energy factors)

    • Achieve desired global behavior with adaptive localized algorithms

    • Perform in-network distributed processing

    • Time and location synchronization

    • Localization

    • Secure Routing


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