Energy-Efficient Data Gathering in Sensor and Actor
Networks: A High Bit-rate Image Sensing Application
Rajnish Kumar, Mina Sartipi, Junsuk Shin, Ramanuja Vedantham, Yujie Zhu,
Faramarz Fekri, Umakishore Ramachandran, Raghupathy Sivakumar
Application
Distributed Source Coding of Correlated Data
from Image Sensors
Mutual Exclusion for Command Delivery from
Base Station to LED Array Actors
Heterogeneous wireless sensor and actor network consisting of mica2 motes with light
sensors, LED array actors, IPAQs with image sensors (cameras), where
Light sensors report the light readings periodically to the Base Station (BS)
LED array actors are turned on based on the light readings
Base station sends a command to cameras to turn on the camera after LED arrays are on
Cameras send the image data to the BS
Motivation:
Motivation:
 Need for mutual exclusion in the acting ranges of the LED arrays
 Mutual Exclusion in WSANs: Execute a given command exactly once (or desired
number of times) for any particular location irrespective of the distribution of actors
 Relaxed Definition: Choose a minimal set of actors such that the overlap between
acting regions is minimal
Goal:
Minimize the total number of transmissions for the three phases for energy-efficient
communication
Illustration of Mutual Exclusion:
 Definitions for illustration
 Rm: Region covered by set of actors
already included as part of actor cover
 ri and rj: New area covered by actor i
and j respectively
 ni and nj: New overlap area for actor i
and j respectively
 oi and oj : Old overlap area for actor i
and j respectively
 Image sensors have correlated data.
 Distributed source coding can exploit correlation structure with low power algorithms
Correlation Model:
X1, X2 : I.I.D binary sequence;
Prob [ Xi =0 ] = Prob [ Xi=1 ] = 1/2.
Prob [ X1 ≠ X2 | X1 ] = p
BSC X2
X1
p
Distributed Source Coding:
X1 and X2 have correlated information.
Goal: Compressing X2:
 Without communicating with X1
 With the knowledge that X1 is present at the decoder
Modeling Distributed Source Coding with Parallel Channels:
Non-uniform Channels
Sensor Stack with Cross-layering support for
efficient Image sensor placement
Energy-efficient communication strategy from
Light sensors to Base Station
X2
k
Motivation:
Motivation:
 Need for energy-efficient communication from light sensors to sink
 Traditional communication strategy conveys information between the sender and the
receiver using energy (EbT) only
 Energy consumption is keb, where k is the length of the bit-stream and eb is
energy per bit
 Can we use time as an added dimension to convey information?
Communication through Silence (CtS):
 A new communication strategy that conveys information using silent periods in
tandem with small amount of energy
 The energy consumption for CtS is always 2eb irrespective of the amount of
information being sent
EbT
97
 Cross-layering can help in better camera placement for the application considered
 Without cross-layering, there is information overlap across layers
 Modules make inefficient decision
– DFuse application needs routing information to decide about role migration
1
Role
Assignment
9
3
8
HSN
Routing
6
Time Sync
Service
7
MAC
0 0
0 1
97 !
1
1 0 0 0 0 1 1

1 0 0 0 0 1 1
Information Exchange Service:
97
1.
CtS
START
1 2 …. 97 !
2.
Efficient use of limited
memory
Simple interface for
information sharing
Data Fusion Layer
Data Service Layer
In-stack fusion
Information
Exchange
Service
Extensibility
Radio
STOP
4.
Next-hop selection,
Logical naming, Packet
scatter/gather
AttributeValue publish/
subscribe
Connection
Asynchronous delivery
(A) Stack Lay-out
5.
Helper
Service
Layer
Medium Access, Error
Control, Radio Control
Medium Access Layer
3.
\ Logic
Application
Application
Complex event notification
(B) Functionalities
PX2
(1-R)n
Wireless
Channel
X2
P'X2
Future Work:
 Distributed source coding for image sensors
Sensor Stack with Cross-layering support:
1 0
Decoder
Conclusions:
 Energy savings for distributed source coding: 40%
 Energy savings for cross-layer support: 110%
 Energy savings for energy-efficient communication: 88%
 Energy savings for Mutual exclusion for LED array actors: 55%
 Overall expected energy savings: 88 + 55 + 110+ 40 = 293%
2
Flood
Routing
X1
Conclusions and
Future Work
Application,
Data Fusion
4 5
Correlation
Channel
Use non-uniform LDPC code for channel coding.
Sensor Stack without Cross-layering support:
1. Fusion Requirement
2. Data transmission requirement
3. Neighborhood
4. Data transmission requirement
5. Data transmission requirement
6. Neighborhood, Topology
7. Time synchronization accuracy
requirement
8. Data transmission requirement
9. Role schedule, Duty cycle
information
Systematic
(X2 ,PX2 )
Channel
n
Encoder
Rate R
X2
Rn
Localization,
Synchronization
Service
Implement the algorithm on image sensors to evaluate energy saving benefits
 Cross-layer support for image sensor placement
 Implement the IES architecture for the heterogeneous testbed for data fusion
 Energy-efficient communication from light sensors to the BS
 Implement CtS communication strategy from light sensors to the BS
 Mutual exclusion for LED array actors
 Implement mutual exclusion on LED arrays to minimize energy consumption