Transcript Routing Protocols for Sensor Networks

Routing Protocols
for
Sensor Networks
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
Acknowledgements
E.
Magistretti (U. Bologna Italy)
J. Kulik (MIT; BBN Co.)
R. R. Choudhury, P. Kyasanur & N. Vaidya (UIUC)
P. Desai (UFL)
D. Braginsky and D. Estrin (UCLA)
S. Hazarika, W. Chen, Y. Gong & X. Liu (UMASS)
T. Kwon & Mjnam (SNU Korea)
R. Peterson & D. Rus (Dartmouth C.)
H.C. Chung, K. Ghoshal & J. Krishna (TAMU)
C. Tavoularis (Cornell )
G. Dong (Virginia U.)
WSN
Dartmouth College
Concepts
Application:
From UMASS
Military
Environmental
From UMASS
Future Health
Circulatory Net
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
General Properties (1)
 Mainly
for Information Collection
 Single Owner
 Up to Hundreds of Thousands of Nodes
 Disposable Nodes
 Cheap Nodes
 Security Concerns
General Properties (2)
 Bounded
Directed Stream (from/to Sink)
 Somewhat Limited Computation Capability
 Limited Communication Capability
 Limited Power Resources
 Node may not have Unique ID
 Common case - Stationary Nodes
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
General Architecture (1)
Sensor Network Node Main Components
 Sensor
Unit
 ADC – Analog Digital Converter
 CPU – Central Processing Unit
 Power Unit
 Communication Unit
General Architecture (2)
General Requirements (1)
 Varying
Network Size
 Inexpensive Nodes Equipment
 Long Lifetime (Power) 
Load-Balancing
 Self-Organization
 Re-tasking and Querying Capability
General Requirements (2)
 Sensible
Data Aggregation
 Consolidation of Redundant Data
 Application Awareness
 Tradeoff
Communication for Computation
 Possible Mobility
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
Protocol Classification (1)
 Proactive
–
First Compute all Routes;
Then Route
 Reactive –
Compute Routes On-Demand
 Hybrid –
First Compute all Routes;
Then Improve While Routing
Protocol Classification (2)
 Direct
–
Node and Sink Communicate Directly
(Fast Drainage; Small Scale)
 Flat
(Equal) –
Random Indirect Route
(Fast Drainage Around Sink; Medium Scale)
 Clustering
(Hierarchical) –
Route Thru Distinguished Nodes
Protocol Classification (3)
 Location
Aware –
Nodes knows where they are
 Location-Less –
Nodes location is unimportant
 Mobility Aware –
Nodes may move –
Sources; Sinks; All
Protocol Classification (4)
 Unicast
–
One-to-One Message Passing
 Multicast (actually Local Broadcast) –
Node-to-Neighbors Message Passing
 Broadcast –
Full-Mesh – Source to Everyone
Protocol Classification (5)
Query Models:

Historical Queries: Analysis of historical data
“What was the watermark 2h ago in the southeast?”

One-time Queries: Snapshot view
“What is the watermark in the southeast?”

Persistent Queries: Monitoring over time
“Report the watermark in the southeast for the next 4h”
Protocol Classification (6)
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
Low Energy Adaptive Clustering Hierarchy
Protocol Highlights
1 - LEACH – Discussed …
 Self-Organizing
– Adaptive Clustering
 Cluster-Heads elect themselves –
Now – “Random Round-Robin”
Future – Power-Based Probability
 Nodes die in random
 Stationary Sink
 Localized Coordination
 Data Fusion
Low Energy Adaptive Clustering Hierarchy
Main Drawbacks
1 - LEACH (2)
 “Hot
Spot” Problem
(Nodes on a path from an event-congested area
to the sink may drain)
 Inadequate
for Time-Critical Applications
 Stationary Sink – Maybe Unpractical
 Basic Algorithm assumes any node can
communicate with sink – limited scale
Low Energy Adaptive Clustering Hierarchy
Main Procedures
1 - LEACH (3)
 Works
in Rounds, each with
Set-Up (Short) and Steady-State (Long)
 Set-Up Phase - subdivided:
– Advertisement (I am a Cluster-Head)
– Cluster Set-Up (I am in your Cluster)
– Schedule Creation (This is your slot)
 Steady-State
Phase:
– Data Transmission using TDMA
Low Energy Adaptive Clustering Hierarchy
Main Procedures
1 - LEACH (4)
Everyone uses the same channel
 Different clusters use different CDMA codes
 Code chosen in random

Cluster-Head communicate with Sink
 Can be extended to Hierarchical Clustering

Low Energy Adaptive Clustering Hierarchy
Illustrations
1 - LEACH (5)
Low Energy Adaptive Clustering Hierarchy
Illustrations
1 - LEACH (6)
Power-Efficient Gathering in Sensor Information Systems
Protocol Highlights
2 - PEGASIS (1)
 Token-Passing
Chain-Based
 Considered Near-Optimal (in a sense)
 Nodes die in random
 Stationary Nodes and Sink
 Every node have a global network map
 Data Fusion
 Greedy chain construction
Power-Efficient Gathering in Sensor Information Systems
Main Drawbacks
2 - PEGASIS (2)
 Stationary
Nodes
 Global Information
Limited Scale:
 Information travels many nodes
 Assumes any node can communicate
with sink
Power-Efficient Gathering in Sensor Information Systems
Main Procedures
2 - PEGASIS (3)
 Greedy
Algorithm Construct Chain –
Start at a node far from sink and
gather everyone neighbor by neighbor
 Node i (mod N) is the leader in round i
Nodes passes token thru the chain to leader
from both sides
 Each node fuse its data with the rest
 Leader transmit to sink

Power-Efficient Gathering in Sensor Information Systems
Illustrations
2 - PEGASIS (4)
Power-Efficient Gathering in Sensor Information Systems
Illustrations
2 - PEGASIS (5)
Rounds Until Death
Threshold sensitive Energy Efficient Sensor Network
Protocol Highlights
3 - TEEN (1)
 LEACH
based Clustering
 Smart data transmission (Saves Power)
 Nodes dynamic reconfiguration ability
 Suits for Time-Critical applications
Threshold sensitive Energy Efficient Sensor Network
Main Drawbacks
3 - TEEN (2)
 “Hot
Spot” Problem
 Cluster-Heads need to listen constantly
 Wasted time-slots
 Can’t distinguish dead nodes
 Other LEACH problems…
Threshold sensitive Energy Efficient Sensor Network
Main Procedures
3 - TEEN (3)
 LEACH
Proactive Clustering
 Node transmit in timeslot only if both:
– Value greater then a Hard Threshold (HT)
– Value differs from last transmitted value
(SV ) by more then a Soft Threshold (ST)
 After
transmission SV is reset
Threshold sensitive Energy Efficient Sensor Network
Illustrations
3 - TEEN (4)
Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network
Protocol Highlights
4 - APTEEN (1)
 Improved
(Adaptive - Hybrid) TEEN
 All TEEN Features
 More flexible logic and timeslots
 Multi-type Queries:
– Historical (What was the temp. then?)
– One-time (What’s the temp. now?)
– Persistent (Tell me the temp for 2 hours)
 Can
distinguish dead nodes
Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network
Main Drawbacks
4 - APTEEN (2)
 LEACH
problems…
 Complex logic
Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network
Main Procedures
4 - APTEEN (3)
 LEACH
Proactive Clustering
 Node transmit in timeslot only if both:
– Value greater then a Hard Threshold (HT)
– Value differs from last transmitted value
(SV ) by more then a Soft Threshold (ST)
Or If did not transmit for a max time (TC )
Or if queried by some sink
 After
transmission SV is reset
Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network
Illustrations
4 - APTEEN (4)
Power Consumption:
 As could be expected –
APTEEN is better the LEACH
but not as good as TEEN
Sensor Protocol for Information via Negotiation
Protocol Highlights
5 - SPIN (1)
Network-wide Broadcast Limited by
Negotiation and using Local Communication
 Flooding problems solved:

Implosion – same data from many neighbors
Detection of overlapping regions
Excessive resources consumption (Blindness)

Needs only Localized Information
 Data
Fusion
 Two main protocols SPIN-PP & SPIN-BC
Sensor Protocol for Information via Negotiation
Main Drawbacks
5 - SPIN (2)
 Broadcast
- Limited Scale –
every node handles O(n) messages
 Data is updated throughout network –
unnecessary in many cases
 Network lifetime - not clear
 High degree nodes = High power needs
Sensor Protocol for Information via Negotiation
Main Procedures
5 - SPIN (3)
SPIN-PP (Point-to-Point Communication)
 Data is described by meta-data ADV msg.
 Node has data  sends ADV to neighbors
 If neighbor do not have data  sends REQ
 Node responds by sending the DATA
 This process continues around the network
 Nodes may aggregate their data to ADV
 In a Lossy Network ADV may be repeated
periodically and REQ if not answered
Sensor Protocol for Information via Negotiation
Main Procedures
5 - SPIN (4)
SPIN-BC (Local Broadcast Communication)
 ADV and DATA sending like PP (but in B.C.)
 Since only one REQ answer is needed, any
node waits a random interval and B.C. REQ
only if none was received yet.
 The rest – like SPIN-PP
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
ADV
Node with data
Node with data advertises to all its neighbors
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
REQ
Node with data
Neighbor requests for data and it is sent
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
DATA
Node with data
Node with data advertises to all its neighbors
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
ADV
Node with data
Receiving node sends ADV to neighbors
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
Node with data
Already
has data
(or dead)
REQ
Receiving neighbors requests for data.
Sensor Protocol for Information via Negotiation
Illustrations
5 - SPIN (5)
Node with data
DATA
Receiving node sends ADV to neighbors
Directed Diffusion
Protocol Highlights
6 - DD (1)

Hybrid Data Centric Routing –
Looking for Named Data
Query–Response Model
 Performs Better than Flooding


Robust and Fault Tolerant (bypass faults)
 Localized Interactions
 Data
Fusion - Application Specific Filters
Directed Diffusion
Main Drawbacks
6 - DD (2)
 “Hot
Spot” Problem near sink
 Periodic Broadcasts of “Interest”
Reduces Network Lifetime
 Trade-off: Energy Efficiency vs.
Robustness and Scalability
 Complex Data Aggregation may Lead to Expensive Node
Directed Diffusion
Main Procedures
6 - DD (3)
A Query (Interest) is Broadcasted by a node
(sink)
 Query Reaches Relevant Sensor Sources
 This Sets-Up Exploratory Gradients
 Once Data is Available in a Source
it is Sent Back via Reinforced Path
 Failing Links / Nodes are being Gradually
Bypassed

Directed Diffusion
Illustrations
6 - DD (4)
Source
CLASS_KEY IS INTEREST_CLASS
LONGITUDE_KEY GE 10
LONGITUDE_KEY LE 50
LATITUDE_KEY GE 100
LATITUDE_KEY LE 120
SENSOR EQ MOVEMENT
INTENSITY GE 0.6
CONFIDENCE GE 0.7
INTERVAL IS 10
EXPIRE_TIME IS 100
Sink
Interest = Interrogation
Gradient = Who is interested
Directed Diffusion
Illustrations
6 - DD (4)
FilterAttrVec
CLASS_KEY EQ DATA_CLASS
SENSOR EQ MOVEMENT
INTENSITY GE 0.7
Source
3. addFilter (FilAttrVec, FilterCallback)
1. subscribe (InterestAttrVec, Callback)
2. subscribe (AttrVec, ApplCallback)
InterestAttrVec
CLASS_KEY EQ INTEREST_CLASS
LONGITUDE_KEY IS 35
LATITUDE_KEY IS 110
SENSOR IS MOVEMENT
Sink
Interest = Interrogation
Gradient = Who is interested
Directed Diffusion
Illustrations
6 - DD (4)
Interests Setting up
gradients
Source
Sink
Interest = Interrogation
Gradient = Who is interested
Directed Diffusion
Illustrations
6 - DD (4)
Sending data …
Source
4. h = publish (SensedAttrVec)
5. send (h, SensedAttrVec)
SensedAttrVec
CLASS_KEY IS DATA_CLASS
LONGITUDE_KEY IS 35
LATITUDE_KEY IS 110
SENSOR IS MOVEMENT
INTENSITY IS 0.8
CONFIDENCE IS 0.7
Sink
Low rate event
Directed Diffusion
Illustrations
6 - DD (4)
Source
m1a
m1b
m2
m2
6. FilterCallback.recv (Message
m1)
m2
CLASS_KEY IS DATA_CLASS
LONGITUDE_KEY IS 35
LATITUDE_KEY IS 110
SENSOR IS MOVEMENT
INTENSITY IS 0.8
CONFIDENCE IS 0.8
7. sendMessage (Message new)
Low rate event
Directed Diffusion
Illustrations
6 - DD (4)
Source
8. ApplCallback.recv (NRAttrVec)
Sink
Low rate event
Directed Diffusion
Illustrations
6 - DD (4)
… and Reinforcing the
best path
CLASS_KEY IS INTEREST_CLASS
LONGITUDE_KEY GE 10
LONGITUDE_KEY LE 50
LATITUDE_KEY GE 100
LATITUDE_KEY LE 120
SENSOR EQ MOVEMENT
INTENSITY GE 0.6
CONFIDENCE GE 0.7
INTERVAL IS 1
EXPIRE_TIME IS 90
Source
Sink
Low rate event
Reinforcement = Increased interest
Directed Diffusion
Illustrations
6 - DD (5)
Source
Sink
Recovering
from node failure
Low rate event
High rate event
Reinforcement
Directed Diffusion
Illustrations
6 - DD (5)
Source
Sink
Stable path
Low rate event
High rate event
Directed Diffusion
Illustrations
6 - DD (6)
Source
Sink
Recovering
from link failure
Low rate event
High rate event
Reinforcement
Directed Diffusion
Illustrations
6 - DD (6)
Source
Stable path
Low rate event
Sink
Reinforcement
High rate event
Use: “Interests set up gradients drawing down data”
Minimum Cost Forwarding
Protocol Highlights
7 - MCF (1)
Cost-Field min Cost from Node to Sink on
Optimal Path
 Slop-Down the Cost-Fields to Get to Sink
 Minimize Multiple Transmissions using
Back-Off Algorithm Based on Node Cost
 Localized Communication

Minimum Cost Forwarding
Main Drawbacks
7 - MCF (2)
 High
Time Complexity (due to back-off)
 Many Sinks – Large Cost Tables
 Cost Field Set-Up Time O(N)
 No Load-Balancing
Minimum Cost Forwarding
Main Procedures
7 - MCF (3)
Broadcast ADV msg. and get Answers from
all Sinks  Create Cost-Fields
 Calculate Back-Off Timer Proportional to
Cost per each Sink
 Needed Information Sent thru Slop
 If no ACK until Timer Expires – Resend ADV

Minimum Cost Forwarding
Cost
Illustrations
7 - MCF (4)
A
B
Timeline
C
Minimum Cost Forwarding
Illustrations
7 - MCF (5)
A=150
110
B = 120
S = 200
C = 90
130
50
100
60
90
Sink = 0
Two-Tier Data Dissemination
Protocol Highlights
8 - TTDD (1)

Grid Structure Clustering
 Stationary

Location-Aware Nodes
Mission Aware – Infrequent Changes
 Greedy
Geographical Forwarding –
Building Grid
 Localized Communication
Two-Tier Data Dissemination
Main Drawbacks
8 - TTDD (2)
 No
Mobile Sensors
 Requires Location Information
 Grid Nodes may Drain
Two-Tier Data Dissemination
Main Procedures
8 - TTDD (3)
Grid Build using Greedy Algorithm and
Location Awerness
 Node Floods Messages to Dissemination
Nodes
 Dissemination Nodes Forward to Sink
 If a Node Fails – Grid is Fixed

Two-Tier Data Dissemination
Illustrations
8 - TTDD (4)
Dissemination Node
Data Announcement
Source
Data
Sink
Immediate Query
Dissemination
Node
TTDD Basics
Two-Tier Data Dissemination
Illustrations
8 - TTDD (5)
Dissemination Node
Trajectory
Forwarding
Data Announcement
Source
Data
Immediate
Dissemination
Node
Sink
Immediate
Dissemination
Node
Trajectory
Forwarding
TTDD Mobile Sinks
Two-Tier Data Dissemination
Illustrations
8 - TTDD (6)
Dissemination Node
Data Announcement
Trajectory
Forwarding
Source
Data
Immediate
Dissemination
Node
TTDD Multiple Mobile Sinks
Random Walks
Protocol Highlights
9 - RW (1)
Finding a Random Walk over a Grid
 Multi-path Routing
Different Routes
 Load Balancing
at Different Times
 Large Scale Networks
 Nodes Assumed to be Mostly Stationary
 No Location Information Needed
 Little State Information
 Localized Communication

Random Walks
Main Drawbacks
9 - RW (2)
 Topology
may not be Practical
(Nodes are Assumed to be Located at
Cubic Grid Junctions)
Random Walks
Main Procedures
9 - RW (3) - RSG
Regular Static Graphs
 Find coordinates differences (Dx, Dy) using
Distributed Bellman Ford (local comm.)
 For every node compute probability of
moving on X and Y
(By the diagonal to the destination)
 On each node move to a adjacent one on X
or Y using that probability. Adjust near end.
All Paths together draws a straight “Banana”
Random Walks
Main Procedures
9 - RW (4) - ISG
Irregular Static Graphs (Some dead nodes)
 Same as RSG but…
 If one adjacent node is missing – go to the
other (with p=1).
 If both are missing – go to a neighbor
whose B-F distance to the destination is
strictly smaller than the current node
(This will create a detour).

(Could optimize by not getting to that node…).
Random Walks
Main Procedures
9 - RW (5) - DG
Dynamic Graphs (Nodes may sleep and wake)
 Same as ISG but…
 When a node changes state: the one-hop
neighbors change B-F labels and possibly
trigger further label (distances) changes
 Concerns:
– Delays in propagating updates
– Sensitivity to inaccuracies in labels
Random Walks
0
2
…...
N-1
S
u4
P4
1
2
u3 P3
v
de[3,2]=2
0
1
P2
P1 u1
[3,2]
...
Illustrations
9 - RW (6) - RSG
u2
N-1
R
Random Walks
0
10,1
1,1
20,1
3
1,1
2,3
1
2,3
1/2
0,0
2,2
1/2
1/2
1
1/3
1
1,1
1
3,1
ISG
2,1
4,1
1
2
0,1
1
1/2
0,0
1
1,4
1,3
1/2
ISG
10,1
1
3
1
1
4,1
2
1
1/3
3/4
4,1
1/3
1/2
1
2/3
1/2
2/3
1
6,2
1,7
1/2
1,1
2/3
3,3
0
1
3
1/4
3,3
1,1
1
1,4
1/2
2,6
3/4
0
1
2
1/3
1,4
1/2
1,10
2/3
1,10
2/3
1
1/2
3
1/4
1,20
2
1/3
0
1
1/2
Illustrations
9 - RW (7) – RSG vs. ISG
1
7,1
Random Walks
Illustrations
9 - RW (8) – RSG vs. ISG
A Random walk by flipping a fair coin
RSG (DG Similar)
ISG
Load Distribution - Narrow
Random Walks
Illustrations
9 - RW (9) – RSG vs. ISG
A Random walk by RSG/ISG algorithms
RSG (DG Similar)
ISG
Load Distribution - Flat
Rumor Routing
Protocol Highlights
10 - RR (1)
 Observation:
for many application any
arbitrary path will do –
No Need for a Shortest Path
 Nodes are Densely Distributed
 Bidirectional Links
 Localized Communication
 Stationary Nodes
 Meet Trails of Queries and Events
Rumor Routing
Main Drawbacks
10 - RR (2)
 Attractive
only when the ratio between
events and queries is inside a threshold
where it is not attractive to flood
neither.
 Optimal parameters depend heavily on
topology (but can be adaptively tuned)
 Does not guarantee delivery
Rumor Routing
Main Procedures
10 - RR (3)
Movement on the net is done by several
agents, trying (randomly) to walk straight.
 Every node maintains lists of neighbors and
events (how to get to the reporting node).
 An agent coming from and event is
updating nodes it visits.
 An agent coming from a query is searching
for ways to the reporting nodes.
 High probability the lines will intersect.

Rumor Routing
Illustrations
10 - RR (4)
Event 1
Event 2
Knows Event 1
Agent
Knows Event 2
Knows Both Event
Rumor Routing
Illustrations
10 - RR (5)
Event
Source
Query
Source
Agenda
General
Properties
Architectures and Requirements
Routing Protocols Classification
10 Suggested Routing Protocols:
LEACH
DD
PEGASIS
MCF
TEEN
TTDD
APTEEN
RW
SPIN
RR
Conclusions
WSN
will spread to many applications
Properties and Requirements are both
Unique and Diversified
Routing Protocol choice
is and probably will continue to be
Application Driven
More Analysis, Simulations and new
Ideas are needed for every category
References (1)




Q. Jiang, D. Manivannan, Routing Protocols for Sensor Networks,
IEEE Consumer Communications and Networking Conference
(CCNC'04), 2004.
R. Jurdak, C. V. Lopes, P. Baldiy, A Framework for Modeling Sensor
Networks, 19th Annual ACM Conference on Object-Oriented
Programming, Systems, Languages, and Applications (OOPSLA'04),
2004.
W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, EnergyEfficient Communication Protocol for Wireless Microsensor
Networks, IEEE Proceedings of the IEEE International Conference
on System Sciences, 2000.
S. Lindsey, C. S. Raghavendra, PEGASIS: Power Efficient GAthering
in Sensor Information Systems, IEEE Aerospace Conference, 2002.
References (2)




A. Manjeshwar and D. P. Agrawal, TEEN: A Protocol for Enhanced
Efficiency in Wireless Sensor Networks, Proceedings of the 1st
International Workshop on Parallel and Distributed Computing
Issues in Wireless Networks and Mobile Computing (with
IPDPS'01), 2001.
A. Manjeshwar and D. P. Agrawal, APTEEN: a hybrid protocol for
efficient routing and comprehensive information retrieval in
wireless sensor networks, Proceedings of the International Parallel
and Distributed Processing Symposium (IPDPS'02), 2002.
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