Design of Energy Efficient Computations and
Protocols for Wireless Sensor Networks
Ashfaq A. Khokhar
Multimedia Systems Lab
(http://multimedia.ece.uic.edu/)
University of Illinois at Chicago
Wireless Sensor Networks
• A large number of sensor nodes with limited capability of
computation, communication and sensing.
• Nodes collaborate with each other through a wireless
channel to accomplish an assigned task.
Sample Sensor Nodes
Modern Sensor Nodes
UC Berkeley: COTS Dust
UC Berkeley: COTS Dust
UC Berkeley: Smart Dust
Courtesy
UC Berkley
UCLA: WINS
Rockwell: WINS
JPL: Sensor Webs
Features of WSN

Traffic rate is generally low
–

Sensor nodes are battery powered
–
–

typical communication frequency is in seconds or minutes.
recharging is usually unavailable
energy is an extremely expensive resource
Sensor nodes in the network coordinate with each
other to implement a certain function,
–
traffic is not random as in mobile ad hoc networks.
Motivation

Wireless Sensor Networks is one of the top
10 Technologies that will change the World in
21st Century



According to MIT Technology Review
Pervasive computing environments are
increasing
Abundance of defense, scientific, and
commercial applications
Wireless Medium Popularity

Phenomenal growth in
–
–
–

New High Bit Rate Wireless Services
–
–
–

Mobile Communications
Internet/Intranet, E-Commerce
Use of Laptops, Palmtops, and PDAs
Intranet/Internet
Multimedia: Integrated Voice, Data, Video
High quality voice and Videoconferencing
New Technology means new products/services
–
Revenue opportunities
Market Estimation

WSN: $150 Million in 2003, $7 Billion estimated
in 2010
(ON World)

Mobility infrastructure market expected to grow
from $25.7 Billion in 2004 to $34.8 Billion in
2008
(Dell’Oro Group)

Today more wireless connections than wired
lines
Typical WSN Applications
Environment monitoring
Transportation
Habitat monitoring
Office security
Industrial monitoring
Fire detection
Challenges in Wireless Sensor Networks

Software Systems
–
–
–
–
–

Networking and Communication
–

–
–
–
Signal processing
Classification
Devices
–

VLSI integration
Architectures
Deployment
Signal and Systems
–

Routing, Data Gathering, Data Dissemination
Hardware:
–

Computing
Control
Databases
Fusion
Knowledge Extraction
Sensor technologies
User Interfaces and Development Environment
Research at Multimedia Systems Lab

Software Systems
–

Networking
–
–

Power-Time Efficient Algorithms
MAC layer
Routing Layer Protocols
Signals and Systems
–
–
–
Field Estimation
Localization
Classification
Collaborative Computing over Sensor Networks

Sensors are smart:
–

Exploit these resources and communicate with sink
only when necessary
–

Similar arguments hold for computing among sinks
Develop distributed algorithms which are power-time
efficient:
–

processing, storage and communication capabilities
Power-time product is comparable to sequential counterpart
Contradicting goals:
–
–
Exploit distributed computing resources
Avoid redundant computations
Example: Conventional Distributed FFT
0
0
1
1
2
3
x2
x3
2
3
x2 + w x3
4
5
6
7
4
5
6
7
x2 - w x3
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Unbalanced Power-Aware FFT: (Ramesh et al -- Milcom 2003)
0
1
2
x2
0
1
2
x 2 + w x3
3
4
5
6
7
4
5
6
7
w*x3
3
x 2 - w x3
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Power–Time Efficient Distributed 1-d FFT Algorithm
0
1
2
x2
0
1
0
1
3
4
5
6
7
8
91
w*xx33
2
x2 + w x3
3
x2 - w x3
2
3
4
5
6
7
8
9
4
5
6
7
8
9
1
x2 0
1
2
0
x2 + w x3
1
3
1
x3
w*x
3
1
3
1
x2 - w x3
1
0
1
1
1
2
1
5
3
1
4
1
7
5
1
2
1
3
1
6
4
1
7
5
1
2
1
3
1
4
1
5
Inverse-Shuffle Complement Permutation
1
5
7
1
1
3
1
13
3
5
9
1
1
4
6
1
0
2
1
12
2
4
8
0
1
5
7
1
1
3
1
13
3
5
9
1
1
4
6
1
0
2
1
12
2
4
8
0
1
5
7
1
1
3
1
3
5
9
1
1
4
6
1
0
2
1
2
4
8
0
RESULTS [SenSys04, AICSS 2006]
Proposed/Conventional
1.4
1.2
Normalized Radio Cost
1
0.8
0.6
0.4
0.2
63
60
57
54
51
48
45
42
39
36
33
30
27
24
21
18
15
12
9
6
3
0
0
Sensors
The ratio of total number of send/receives in the proposed and conventional 64-Sensored FFT algorithm
•The Actual communication cost improvement based on our experiments result is 36%.
•Theoretical cost improvement is 42%.
•The discrepancy arises from packet dropping due to collisions.
Energy(mJ)
RESULTS
1200
Proposed
1000
Original
800
600
400
200
0
8
16
32
Number of Sensors
64
Energy consumption of the FFT computation as the number of sensors increased
•Approximately 20% energy cost reduction for N=32 and 64.
•The energy improvement is due to reduction in the FFT computation time,
as sensors are all on during the computation.
•Except for the network of 8, the overhead of id shuffle phase is justified
from the overall savings.
RESULTS
Conventional FFT
Proposed FFT
The number of packets transmitted at each time-interval during the conventional and proposed FFT
computation over a 64-sensor network sensor networks
•The Congested intervals are reduced significantly.
•Packet collision probability is reduced.
•MAC protocols can shut down the radio circuitry aggressively.
Example: Classification


Apriori determination of
categories
Train for each category
–
–

Test
–
–

Expose to sample
measurements
Compute Mean (i,j) &
Covariance (i,j)
Classifier Algorithms
Develop false positives,
beliefs, etc
Deploy
–
Identify detected object
w.r.t. categories
?
Classifier Algorithms

Traditional Signal Processing (SP) algorithms


–
–

Pro: used extensively for its known accuracy
Con: computationally intensive
Novel WSN classifiers

Sub-optimal Classifiers [Kotecha & et. el. 2005]
Influence fields [Arora & et. el. 2004]

Differentiated Surveillance [Yan & et. el. 2003]

–
–

Maximum Likelihood (ML) [Li & et. el. 2002; Duarte & Hu, 2003]
k Nearest Neighbor (kNN) [Li & et. el. 2002; Duarte & Hu, 2003]
Pro: simpler computationally
Con: novice, accuracy?
Our Goal: adapt existing SP algorithms for efficient
classification in sensornets
What’s the Problem?

Events are f × d matrices
f – modalities/features sensed
d – temporal processing dimensions



Typically f  50 & d  512
Mean  is f × d and  is f × f matrices
 -1 is also f × f
Large matrix computations for every detected event
–

Matrix multiplication
Inverse computation !
–
–
Unstable
Expensive
Power Consumption Comparison [HiPC 2006]
log of power consumption
Power consumption comparison of MAP with Jacobi (MAP-J) and
MAP with LU (MAP-L)
10
8
6
MAP-J
4
MAP-L
2
0
10
20
30
40
50
60
features
70
80
90
100
d = 512
Communication Protocols


A fresher look was needed
Energy efficiency and network life time as the
main goal
–
–
–
MAC Layer
Network Layer: Geographical Routing
Application Layer: Data Collection
Geographic Routing in WSN

Geographic routing:
–
–
–
Greedy forwarding: At each hop, data packet is relayed by a neighbor which
is geographically closer to the destination than the packet holder in terms of
Euclidean distance
Communication void(hole): a network area where greedy forwarding fails to
locate the next hop node in a packet holder’s neighborhood
Some strategies must be used to handle the void to guarantee the packet
delivery if a real path exist
Existing Geographic Routing Protocols

Beacon-based(state-based): PGSR….





Neighbor information collection: periodical beacon messages exchanged
among neighbor nodes
Next hop node is chosen from the neighbor table by the packet holder
Right/left hand rule based on planar graph is used to handle void
High communication overhead due to the beacon messages
Beacon-free(state-free): IGF, PSGP, SIF…



No neighbor information maintained in each node
Next hop node is chosen based on a competition, which is triggered by a packet
holder, among the packet holder’s neighbors
Right/left hand rule or increasing transmission power methods are used to
handle the void:
Motivation for Void Handling

Right/left-hand rule is impractical since it’s hard to build
planar graph in beacon-free geographic routing protocols

Increasing transmission power is not energy efficient

History of void handling should be maintained and
used for future data delivery
Proposed Contention-based
Geographic Routing Protocol (CGR-D)





Beacon-free
A depth first search (DFS) method is used to locate the next hop node
at each hop
An integrated cost function which combines forwarding area
determination, void handling and load balancing is defined as the
metric used in DFS method
Each node maintains a local variable (void cost) which is an indication
whether a node is in or close to a void area
The void cost is updated per sink basis and periodically reset to 0
Cost Function
If a node C is closer to the sink D than the current packet holder F:
f (d, E, r, H) = [* (1-d/R) +  * (1-E/E0) + *r + K*(1+H)]*T0
(1)
Otherwise:
f (d, E, r, H) = [* (1-d/R) +  * (2-E/E0) + *(1+r) + K*(1+H)]*T0
Where
(2)
d = dist(F, D) – dist(C, D)
dist(x,y)means distance between x and y
H = voidCost(C) - voidCost(F) voidCost(x) is the void cost value of
node x
R is radio range
E is the remaining energy of node C
E0 is the initial energy
r is a random number between [0,1]
+  +  = 1 and , ,  > 0
K, T0 are system parameters
Data Delivery Example for CGR-D
Performance Evaluation
General Data Gathering


Data gathering problem:
Sensing data and transmitting data to a sink.
Clustering based solution:
LEACH is a typical one.
Nodes organized into clusters.
Head collects data and transmits aggregated data to the sink.
Sink
Mobility of Sensor Nodes

The nearest cluster head may change especially in a highmobility scenario.

Keeping transmitting to the old cluster head consumes more
energy.
Sink
Our Solution: Low-energy Dynamic Cluster
Selection (LEDCS) Protocol [VTC 06]

As in LEACH, time is divided into rounds and at the beginning
of each round, each node i determines whether it is a cluster
head in the current round with a predefined probability.

Introduce a contention period at the beginning of each time
frame.

Nodes may join the new cluster head during this contention
period.

If a node is a cluster head in the current round, it broadcasts
this info across the network which also includes its own
moving direction and velocity in the current round.
Our Solution: Low-energy Dynamic Cluster
Selection (LEDCS) Protocol
Simulation Results
1000 sensors in a 400m x 400m area
Percentage of
contention period
considering
different velocity
of sensor nodes.
Simulation Results
Total number of
data packets
received by the
sink considering
different velocity
of sensor nodes.
Up to 80% higher
Simulation Results

Total number
of data rounds
Data Gathering in Event Driven
Applications [INSS 07]

Bursty traffic in event driven applications.
Sink
Estimation of Traffic Load

Estimate the current traffic
load at the beginning of each
round.

Based on the estimated
value, set up mathematical
model and determine optimal
number of cluster heads to
minimize the total energy
consumption.
the total energy consumption vs.
optimal number of cluster heads
Comparison of Total Energy
Consumption
Test case 1
Test case 2
Data Collection via Cross-Layer Optimization
Data Collection:
A major class of sensor network application
●Generally a spanning tree is used for data
collection over a sensor network
●
Problems:
●
Congestion is major performance bottleneck.
Goals:
To increase data delivery ratio with simpler MAC protocols
●To mitigate congestion in the network especially near the sink
node
●To increase bandwidth utilization
●
Sink
Sensor
nodes
MOTIVATION
SMAC like contention based MAC
protocols are simpler and do not
require tight synchronization.
●
A
B
C
sleep
active
active
active
sleep
active
Question: In data collection applications is it
absolutely necessary for a node to communicate
every one of its hop neighbors?
sleep
sleep
Sink
-Not if we can find route to sink!
-Node B does not have to
communicate with C, so it can stop
following C's schedule
B
A
C
Single Data Collection Tree
•Single Data Collection Tree
•Multihop communication
•Single spanning tree is constructed and
nodes forward their readings to their
parents
•Major performance bottlenecks:
• Heavy congestion near the sink node.
• High competition for the wireless
medium.
• High delays due to medium access
Multiple Data Collection Trees
Phase 1: Construct 2 different trees
Multiple Data Collection Trees
Phase 2 : Activate each tree at
different times
•Construct more than 1 collection tree
•Active trees at different times than the others
•Data collection is possible without communicating with all the neighbors
•Routing protocol should find the set of neighbors necessary to communicate
with sink.
•Decreasing the number of active nodes will mitigate congestion and increase
delivery ratio.
Single vs. Multiple Data Collection Trees
Single data collection tree:
active
sleep
1 Duty Cycle
Advantage: Simple tree construction
Disadvantages:
●
Network is highly congested near sink
●
Bandwidth is not fully utilized
●
High collision probability
Multiple Data Collection Trees
Advantages:
•Increased bandwidth utilization
•Mitigated Congestion
•Energy consumption not increased
•Less nodes are active
Disadvantage: More complex tree
construction
sleep
active
active
sleep
Simulation Results
Delivery Ratio- 10% SMAC
0.85
0.75
0.65
0.55
0.45
0.35
SMAC 50
SMAC 100
0.25
SMAC 200
0.15
1
2
3
4
5
6
Num ber Of Trees
7
8
9
Simulation Results
Delivery Ratio Regular Grid
1.2
1
0.8
0.6
50
0.4
100
150
0.2
200
0
1
2
3
4
N um be r Of Tr e e s
5
6
Simulation Results
Energy Consum ption
810
790
SMAC 50
Joules
770
SMAC 100
750
730
710
690
670
650
1
2
3
4
5
6
Num ber of Trees
7
8
9
Data Gathering in Vehicular Networks



Stationary Internet Gateways (IGWs) along the
highway which provide interfaces connecting
vehicles to the Internet, a gateway communicate
with vehicles out of its transmission range via multihop communications
Traditional 802.11 MAC solution:
Multi-hopping scheme suffers from low throughput
and starvation of packets originating from vehicles
far away from gateways due to high collision
Example: CVIA Protocol
1.
2.
3.
Divide the line between gateways into segments , length of segments is
equal to the transmission range of a vehicle
Assign TDMA slots for each segments such that only segments out of
transmission range will transmit in the same time slot– collision free
In the figure S1,3,5,7 transmit in a time slot and s2,4,6,8 transmit in
the next
time slot
CVIA Protocol
Four phases in each time slot:
1) Temporary Router Selection phase: inbound and outbound temporary
routers elected for each segments
2) Inbound router transmits collected data to outbound router
3) Outbound router collects data within its segment
4) Outbound router transmits data to the neighboring inbound router
Problems

Inter- and Intra-segment contention leads to
higher packet losses
The time length of a slot for local data
gathering phase is uniform, leading to
transmission delays

Solutions:

–
???
Distortion Analysis in Sensing Field
Measuring Spatial-temporal Correlated Data

Example of field measuring
Gaussian correlated data.

Consider the real-time data
gathering problem in a field
that data is both spatial and
temporal correlated.

Sink will do real-time data
reconstruction for the whole
field.
How many nodes should be put into the
field to minimize the total distortion?


Number of nodes
increases:
Spatial distortion
decreases while
temporal distortion
increases.
An optimal number of
nodes exists to
minimize the total
distortion.
Randomly deployed single-hop sensor
networks

Nodes randomly deployed in the field
following Poisson distribution.
1. One-dimensional case
2. Two-dimensional case
Use Voroni Cell partitions to achieve
minimal distortion within each cell
D( x1 , t1 ; x2 , t 2 )  E[(Y ( x1 , t1 )  Y ( x2 , t 2 )) 2 ]
 2  2e
n
 (( x1  x2 ) 2   2 ( t1 t 2 ) 2 )

D1 (t )    Pr{S ( r )  v}2(1  e  ( r
2
  2 ( iT  t ) 2 )
)dr
i 1  

TDMA protocol used for data collection
Randomly deployed one-hop sensor
networks
1. An optimal number of
nodes always exist to
minimize the total
distortion D.
2. when correlation intensity
α is fixed and time scaling
constant βT is increased,
the optimal value of n that
minimizes the total
distortion D decreases.
Fixed topology for multi-hop sensor
networks

We assume that each
node has a limited
transmission range R

A TDMA-based
transmission algorithm
is designed for
collision-free data
transmission from
nodes to the sink.
An Example 1D Transmission Schedule
Total distortion varies with different
coefficients (one-dimensional case)
Total distortion varies with different
coefficients (two-dimensional case)
a) α=0.1,βT=0.001
b) α=0.1,βT=0.002
c)α=0.1,βT=0.005
d) α=0.02,βT=0.002
e) α=0.05,βT=0.001
f) α=0.05,βT=0.002
Analysis for both cases


The total distortion experiences a sudden
drop for every increase of five in number of
nodes (one-dimensional case) or number of
rings (two-dimensional case).
With the increase of correlation coefficients α
and βT, for a given number of nodes, the
total distortion will increase due to the
weaker correlation of the field.
Minimum number of nodes required
given a certain distortion constraint
Correlation Coefficients
Distortion
const
raint
Minimum number of
nodes
Correlation Coefficients
Distortion
const
raint
Minimum number of
nodes n*k(n, k)
α=1, βT=0.2
12
35
α=0.1, βT=0.005
120
50(10,5)
α=1, βT=0.002
4
6
α=0.1, βT=0.002
60
90(18,5)
α=1, βT=0.002
1.5
14
α=0.1, βT=0.001
40
120(24,5)
α=0.5, βT=0.02
4
5
α=0.1, βT=0.001
120
45(9,5)
α=0.5, βT=0.002
1.5
7
α=0.05, βT=0.005
120
35(7,5)
α=0.5, βT=0.002
1
14
α=0.05, βT=0.002
40
70(14,5)
α=0.2, βT=0.002
1.5
5
α=0.02, βT=0.002
60
30(6,5)
One-dimensional grid network case
α=0.2, βT=0.002
1
6
Two-dimensional
wheel-based
40
35(7,5)
network case
α=0.02, βT=0.002
Other Problems Under Investigation





Localization
Synchronization
Upper layer communication protocols
Data fusion
Knowledge extraction