An Efficient Hybrid Data Gathering Scheme in
Wireless
Sensor Networks
Ayon Chakraborty
Swarup Kumar Mitra
Mrinal Kanti Naskar
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Contents
Wireless Sensor Networks
Issues in Wireless Sensor Networks
Data Gathering Algorithms
Efficient Hybrid Data gathering Scheme
Simulation Results
Conclusion
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Wireless Sensor Networks
Assembly of sensor nodes for
monitoring any physical quantity or
phenomenon
An Underwater Sensor Network
May be data collection or event
driven network.
Applications of data collection sensor
networks vary widely from climatic data
collection , seismic and acoustic
underwater monitoring to surveillance
and national security, military and
health care.
A Simple Sensor Deployment
In case of data collection networks,
sensor nodes are required to transmit
periodically to the Base Station
generally located far off from the
sensed region.
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Contents
Wireless Sensor Networks
Issues in Wireless Sensor Networks
Data Gathering Algorithms
Efficient Hybrid Data gathering Scheme
Simulation Results
Conclusion
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Issues In Wireless Sensor Networks
Delay in the Network
Data from sensor network are typically time sensitive , so
it is important to receive the data in a timely manner
(delay).
Limited –Energy in Batteries
These networks should function for as long
possible .But it may be inconvenient or
impossible to recharge node batteries
Therefore ,all aspects of the node ,from the
hardware to the protocols must be designed to
be extremely energy efficient
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Energy Constraints in Sensor Nodes
A Wireless Sensor
 Nodes are inherently batterypowered
Since the nodes are deployed in
inaccessible regions, battery
replenishment is not possible
Performance parameter like
Network Lifetime and Delay
becomes important
“Energy x delay” product is one of
the keys for prolonging network
lifetime
Sensor Deployment for volcano monitoring
Hence Data Collection Schemes
to be devised
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Contents
Wireless Sensor Networks
Issues in Wireless Sensor Networks
Data Gathering Algorithms
Efficient Hybrid Data gathering Scheme
Simulation Results
Conclusion
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Data Collection Algorithms
Several Data Collection algorithms have been devised
 Direct Scheme
 LEACH
(Low Energy Adaptive Clustering Hierarchy)
 PEGASIS (Power Efficient Gathering in Sensor Information Systems )
 Hierarchical Binary PEGASIS
 LBEERA (Load Balance and Energy Efficient Routing Algorithm)
 SHORT (Shortest Hop Routing Tree for Wireless Sensor Networks )
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Direct Scheme
BS
Node Deployment Scenario
C1
C0
C2
Advanced Digital and Embedded Systems Lab
C3
MCKVIE, Department of ECE
First Order Radio Model
ξ
elec
ξ amp
k
d
= electronics energy per bit ,
= amplification energy per bit per area (meter square)
= Total no of bits
= distance between nodes or between node and base station
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
LEACH
BS
N
N
N
CH
N
N
N
N
N
N
N
N
CH
CH
N
N
N
N
N=Sensor Nodes, CH=Cluster Head, BS=Base Station
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
PEGASIS
Round 0
BS
Node Deployment Scenario
C1
C0
C2
Advanced Digital and Embedded Systems Lab
C3
MCKVIE, Department of ECE
Hierarchical Binary PEGASIS
BS
C0
C0
C0
C2
C1
C1
Advanced Digital and Embedded Systems Lab
C3
MCKVIE, Department of ECE
Contents
Wireless Sensor Networks
Issues in Wireless Sensor Networks
Data Gathering Algorithms
Efficient Hybrid Data gathering Scheme
Simulation Results
Conclusion
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
SHORT
4
1
2
5
3
6
7
Communication Pairs in the first slot,,node 2 is the leader
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
SHORT
4
1
2
5
3
6
7
Communication Pairs in the second slot, node 2 is the leader
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Proposed Scheme
SHORT
Fig 3(a) Communication Pairs in the first slot,,node 2 is the leader
Fig 3(b) Communication Pairs in the second slot ,node 2 is the leader
HYBRID =SHORT + LBEERA
Fig 3(c) Communication Pairs in the third slot ,node 2 is the leader
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Hybrid Data Collection Scheme
For Round i
d =Distance between the node and the Base Station (BS)
E residual = Remaining energy of the node
BS
2
SL=Super Leader
L=Leader
L=Max (Pi) = E residual / di2 and SL=Max( L)
SL
2
7
5
1
7
7
7
L2
L2
7
4
3
7
7
7
6
7
7
7
Advanced Digital and Embedded Systems Lab
7
7
MCKVIE, Department of ECE
Parameters Used in Simulation
Assumption
We consider the first order radio model
In our simulations we take ξ elec = 50 nJ/bit, ξ amp= 100 pJ/bit/m2 and
ξ elec = ξ amp as mentioned in with k = 2000 bits.
Sensor nodes are homogeneous and energy constrained with uniform energy
It is assumed that the channel is symmetric so that the energy spent in transmitting
from node i to j is the same as that of transmitting from node j to
located at (25,150).
Energy of 0.1J/node
No mobility of the Sensor Nodes
i. Base Station is
Sensing Area
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Calculation of Delay, Message Complexity, Energy* Delay product and Mean Delay
•
Thus message passing complexity is linear, i.e. O(N )
•
Thus ‘energy x delay’ product have a complexity of O(NlogN).
•
Delay
N1+N2 + … + NM =N nodes
1st Slot (N1 /2 + N2 /2 + … + NM /2) groups.
Delay of (log(MNi) + 1) time slots to
Base station
tth slot each of the (N1 /2t + N2 /2t + … + NM /2t) transmitter nodes delay of (log(MNi) – t + 1) time slots
• MEAN DELAY= MD
l
o
g
N
l
o
g
M
i
{
(
N
j
[
l
o
g
N
i

i

2
]
/
2
)
}


MD=
i

1j

1
N
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Contents
Wireless Sensor Networks
Issues in Wireless Sensor Networks
Data Gathering Algorithms
Efficient Hybrid Data gathering Scheme
Simulation Results
Conclusion
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Simulation Results
Comparison of "Energy x Delay”
of different algorithm
vs
different nodes
Advanced Digital and Embedded Systems Lab
Comparison of network
lifetime of different algorithm
vs
number of nodes
MCKVIE, Department of ECE
Simulation
Fraction of Packets Successfully reaching the Base station with Retransmission attempts the upper dark portion of the Bars shows the
range of the fraction ,the tips indicating maximum and minimum
fraction ( Simulated In TOSSIM )
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Simulation Results
Performance Comparison of different Routing Schemes
(a)
Performance Metrics
Network of 50 nodes
PEGASIS
BINAR
Y
SHORT
LBEERA
FND
705
510
1482
1143
HND
2182
1432
2169
2160
LND
2575
1897
2413
Energy Consumption
(*10^-3 Joule per round)
11.5
15.54
Delay
31.20
0.36
Network Lifetime
(rounds)
(slots per round)
Mean
Energy*Delay
(Joule*slot)
(a)
HDS
1511
PEGASIS
BINARY
Network of 100 nodes
SHORT
LBEERA
HDS
849
514
1427
1377
1455
2177
2587
1744
2533
2400
2583
2533
2613
2945
2271
2992
2504
2989
12.31
13.96
12.07
20.12
25.87
19.85
24.42
19.46
6.30
6.82
8.42
6.85
66.02
7.38
7.71
17.82
7.74
0.097
0.084
0.117
0.082
1.328
0.19
0.153
0.435
0.145
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Conclusion
Our proposed algorithm overcomes the losses
incurred from all other data gathering schemes,
hence the network lifetime override its
performance. The fertility of our work lies that we
can embedded the features of packet
transmission in sensor motes like MicaZ or Mica2.
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
References
1. Lindsey, S., Raghavendra, C.S. : PEGASIS: Power Efficient Gathering in
Sensor Information Systems, In Proceedings of IEEE ICC 2001 (2001) 11251130
2. Yang, Y., Wu, H.H., Chen, H.H. : SHORT: Shortest Hop Routing Tree for
Wireless Sensor Networks, IEEE ICC 2006 proceedings , (2006)
3. Levis, P. : TinyOS Programming , (2006)
4. Lindsey, S., Raghavendra C.S., and Sivalingam, K. : Data Gathering in Sensor
Networks using energy*delay metric, In Proceedings of the 15th International
Parallel and Distributed Processing Symposium, (2001) 188-200
5. Yu1, Y., Wei, G. : Energy Aware Routing Algorithm Based on Layered Chain
in Wireless Sensor Network, 1-4244-1312-5/07/$25.00 © 2007 IEEE.
6. Heinzelman, W., Chandrakasan, A., Balakrishnan, H. : Energy- Efficient
Communication Protocol for Wireless Microsensor Networks, IEEE
Proceedings of the Hawaii International Conference on System Sciences,
(2000).
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE
Thank You
Advanced Digital and Embedded Systems Lab
MCKVIE, Department of ECE