International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
Lifetime Improvement of Wireless Sensor
Network UsingMulti-hop Adaptive
Modulation
Gurdeep Singh#1,KmandeepKaur#2
M.Tech, Deptt. of ECE Ramgarhia Institute of Engineering
and Technology Phagwara
Abstract— The lifetime of the WSNs depends only on
energy system. To keep the cost and size of these sensors
small, they are equipped with small batteries. In large
number of applications, it is impossible to charge and
replace the batteries to extend the lifetime of the wireless
sensor network.. The node's energy consumption optimize
through the multi-hop adaptive modulation scheme that
used in node communications. In this work, Adaptive
Modulation is proposed for lifetime improvement in
wireless sensor Networks. The objective of proposed
adaptive modulation approach is to save the energy of
lowest energy nodes by increasing energy consumption of
other nodes that has highest residual energy.. The results
show that with adaptive modulation we can minimize the
energy consumption of node. In term of lifetime, existing
adaptive modulation giving 50% better result than fix
modulation whereas proposed approach is giving 72%
better result than fix modulation approach.
Keywords—wireless sensor network; Multi-hop
adaptive modulation; lifetime; 2FSK,4FSK,8FSK.
I.
INTRODUCTION
In this approach modulation order, vary accordance
with node's residual energy. Main objective of this
approach is to save the energy of that node whose
energy consumption is more or which is being use
more in network as compared to other network nodes.
Energy consumption of the node depends upon the
selection of the modulation order. Algorithm is base
on transmitting faster in those nodes with more
remaining energy, and transmitting slowly in those
nodes with lower battery levels. We can fast the
transmission process by increasing the modulation
order and can decrease the transmission time with
decreasing its modulation order. Transmit faster
means consume more energy with lower delay and
transmit slow means consume less energy with higher
delay. The idea is to steal to the rich node
transmission time in order to give it to the poor node.
Thus, nodes with high energy will spend a little bit
more energy and low energy nodes will save energy.
This concept led to increase the lifetime in a path of
nodes[1].
For this work we selected M-ary NC FSK
modulation. Which is implemented on wireless
sensor network model for comparison of result.
ISSN: 2231-5381
II.
RELATED WORKS
In [2] authors discussed several energy-efficient
approaches have been investigated, including network
protocols as well as cross-layer designs. At the
physical layer, modulation, coding, adaptive resource
allocation, and cooperative relays have been pursued
to effect energy efficiency in the overall system
performance; however, all are built on the premise
that the power-supplying batteries are ideal and
linear. This implies that the battery power
consumption equals the total power required by all
energy-consuming modules, including signal
processing, hardware circuitry and transmitter
modules. In fact, part of the battery‟s capacity (stored
energy) may be waste during its discharge process.
For this reason, the lifetime of battery-driven sensor
nodes depends not only on the power required by
energy-consuming modules, but also on the unique
nonlinear characteristics of batteries. Realistic
nonlinear battery models are available, and have been
consider in the rapidly evolving area of battery driven
system design. Compared to conventional low power
designs, the latter has the potential to markedly
improve the lifetime of battery-powered systems.
However, existing battery-driven approaches have so
far dealt with hardware and software optimization of
a single node, or, routing aspects of energyconstrained networks; but not with modulation and
communication issues at the physical layer. There is
clearly a need to integrate available battery models in
the design and evaluation of modulation and
communication schemes for WSNs.
In [3] authors studied on Battery power efficiency
of PPM and FSK in Wireless Sensor networks.
Orthogonal modulation appropriate for the energy
limited WSN setups, have been investigated under the
assumption that batteries are linear and ideal, but their
effectiveness is not guaranteed when more realistic
nonlinear battery models are considered. They
compare the battery power-efficiency of two pulsebased modulations: PPM and on-off keying (OOK).
Instead of using the non-coherent receiver, they
consider coherent reception for both PPM and OOK.
In addition to the error-performance oriented bit-errorrate (BER) criterion, they also adopt the rate-oriented
cutoff rate criterion. With the battery criterion, they
exploited the fact that the cutoff rate is maximized by
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International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
optimizing the transmission probability of „1‟ in
OOK. To ensure fair comparisons between M-ary
PPM and binary OOK, an M-dependent duty-cycle
factor is also introduced to equate the bandwidth
efficiencies of the two modulation formats. They also
adopt a realistic empirically derived non-linear battery
model and consider analog circuit power
consumption. They analyzed that FSK is more power
efficient than PPM in sparse WSNs, the power
advantage of FSK over PPM is no more than 3DB,
whereas in dense WSNs the power advantage of PPM
over FSK can be much more significant as the size of
M increases.
In [4] authors compared the performance of pulsebased modulations, namely pulse position modulation
(PPM) and on-off keying (OOK), both of which are
suitable for WSNs due to their low complexity
transceivers. The comparison is based on a general
model that integrates typical WSN transmission and
receptions module with realistic non-linear battery
models. They analyzed and compared the battery
power efficiency of PPM and OOK using coherent
detection, and with bit error rate (BER) and cutoff rate
criteria They considered coherent reception for both
PPM and OOK. In addition to the error-performance
oriented bit-error-rate (BER) criterion, they also
adopted the rate-oriented cutoff rate criterion. With
the battery criterion, they exploited the fact that the
cutoff rate is maximized by optimizing the
transmission probability of „1‟ in OOK.. With the
BER and cutoff rate criteria, their system model and
the BPER comparisons accounted for the transmit
power, the analog circuit power consumption and the
battery non-linearity. Both the analysis and simulation
show that PPM is more battery power efficient in
WSN with high cut-off rate.
In [5] Abdellah Chehri
proposed adaptive
modulation which is not optimal because in it energy
saving is done only in one node who has lowest
residual energy among all other nodes.
Our approach tried to balance the energy spending of
4 nodes (two lowest energy nodes and two highest
energy nodes), in a stipulated time without producing
delay.
III.
PROBLEM FORMULATION
One basic feature of WSNs is to send information in
short distances and forward them many times in order
to achieve energy efficiency. This geographical
model is known as multi-hop wireless network. Due
to the different characteristics of each node, as for
instance transmission power parameters, electronic
parameters, distance to the next node the energy
consumption of each node is different. This means
that in a path of nodes, the node with higher
consumption of energy will determine the path's
lifetime. Fix modulation consume same energy for all
the nodes. It does not matter its initial energy is low
or high, which lead to same energy consumption in
all nodes. The node, which has lowest initial energy
ISSN: 2231-5381
its energy consumption is same as the other nodes
that have high initial energy. With this strategy
lowest energy node dead early as compared to other
nodes and path connectivity break early. This shows
that the lifetime of the path is depending upon the
lifetime of lowest energy node. That lead to wastage
of energy of all other node after the first node dead
and decrease the lifetime of network. To save this
wastage of energy adaptive modulation is used which
reduce the energy consumption of lowest energy
node. Our aim is to develop an algorithm for
transmission that achieves the longest lifetime over
the all network taking into account the battery energy
levels of each node. One way to solve it is by the use
of adaptive modulation where a modulation scheme is
select and its parameter varies dynamically
depending on the requirements of the system at a
given time.
.
A. Algorithim Formulation
Step 1: Deployment of Nodes
Step 2: Selection of Path.
Step 3: Check residual energy(RE) of Nodes in the
network.
Step 4: If Node's RE is less than threshold (TH) level
and it is minimum or second minimum in the path
then use M=2.
i.e. NC-2-FSK
Step 5: If Node's RE is greater than TH level and it is
maximum or second maximum in the path then use
M=8
i.e. NC-8-FSK
Step 6: Else use M=4
B. Calculation of the nodes's Energy consumption
The energy consumption of the transmitter nodes
is occur due to Radio Frequency signal generation
and due to electronic components necessary for
transmission. We taken both type energy consumption
techniques for computation of the results[5].
Energy consumption for one packet transmission
is
Ei = t * ( Ptx,i + pc )
(1)
in (1) Pc = Circuit power Consumption and E =
Transmission Energy, Ptx = transmission Power.
Transmission time for Non coherent M-FSK is
computed by the equation (2)
t =
Q / (2*B log2 M)
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(2)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
Q = number of bits,
C6: Multiplexing Control..
B i = Bandwidth.
Table 1. Input parameters
M = Modulation Order.
Ptx,i = 2 * ns *g1-1 * log ( ( 2 * ( 1- (1 - ps ) )-1
(3)
Ptx, i = transmission power for the ith hop.
ns = power spectral density,
g1 = Gain factor.
Ps = probability of symbol error.
Circuit's power consumption is = Pc = Plo + Pfilter
Parameter
Value
Area
50*50 meters
Bandwidth(B)
10 KHz
Antenna Gain(G1)
30db
Path loss(Ns)
-174
Pfilter
2.5mw
PLo
1.8mW
Symbol Error Rate(Ps)
10-3
(4)
Plo = power of Local oscillator .
L
∑ Ti
(5)
≤
Tmax
i=1
Ti is the individual transmission time of a node.
Above equation ensures that the data should send
over the path within the chosen time delay.
IV.
Table 1 is showing the parameters which are
taken account for simulation purposes.
SIMULATION AND RESULTS
In this study, we deployed sensor node using a
two-dimensional uniform distribution in a 50m x 50
m.. For transmission Multi-hop data transmission
topology has selected..In Next step we designed Mary adaptive transceiver that can transmit and receive
data up to NC-8FSK adaptively [6] as shown in figure
1.
Fig. 1. Adaptive NC-MFSK transceiver
C1: De-multiplexing Control
.
C2: Modulation and Thresholds control
C3Bandwidth Control
A. Comparison of Energy Consumption for fix,
existing adaptive and proposed Adaptive
Modulation approaches:
The Figure 2 is showing relationship between fix
modulation, existing multi-hop existing adaptive
modulation and proposed multi-hop adaptive
modulation approaches. It clears that energy
consumption for fix modulation is same in all nodes,
it does not matter its initial energy is minimum or
maximum. In the existing
multi-hop adaptive
modulation approach, it is clear that the node1 which
has minimum energy its energy expenditure is also
minimum and node 10 that has maximum initial
energy its energy expenditure is maximum.
In case of proposed adaptive modulation technique
node that has lowest energy among all other nodes, its
modulation order is decreased by one to save the
energy consumption of the node. Decreasing
modulation order lead to increase its transmission
time and reduce the energy consumption of the node.
To recover this increased transmission time
modulation order of the other node that has highest
residual energy is increased by one that increases
energy consumption of the node and reduced its
transmission time to recover the delay. Comparison
of the proposed technique is done with existing
techniques. Results shows that proposed technique is
giving better result than the existing approaches.
.
C4: Sampling Control
C5: Demodulation Control.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 33 Number 4- March 2016
approach gave 44% better result than the existing
adaptive modulation approach. So we can say that it is
well-organized approach for lifetime improvement of
WSNs.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Wayne Tomasi," Advanced Electronic Communications
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L. van Hoesel andT. Nieberg, J. Wu., “Prolonging the
lifetime of wireless sensor networks by cross-layer
interaction,” IEEE Transaction on Wireless Communication,
vol. 11, no. 6, pp. 78-86, Dec. 2004..
Q. Tang, L. Yang, G. B. Giannakis, T. Qin,"Battery power
efficiency of PPM and FSK in wireless sensor networks",
IEEE Trans. on Wireless Commun., vol. 6, no. 4, pp. 1308 1319, April 2007.
Fengzhong Qu, Liuquing Yang and Ananthram Swami."
Battery Power Efficiency of PPM and OOK in Wireless
Sensor Network, IEEE Trans. on Wireless Communication
2007.
Abdellah Chehri," Energy-Aware multi-hop transmission for
Sensor Networks based on Adaptive Modulation." IEEE 2010
J. G. Proakis, "Digital Communications, 4th ed. MA:
Addison", Wesley, 1972..
Fig.2. Comparison of the Energy consumption of the used
approaches
.
B.
Improved Lifetime
Fig.3. Lifetime Improvement
Figure 3 show comparison of fix modulation versus
adaptive modulation and fix modulation versus
proposed adaptive modulation approach. Existing
multi-hop adaptive modulation approach gave 47
percent better result than fix modulation approach and
proposed multi-hop adaptive modulation approach
gave 68% better result than fix modulation approach.
We can say that proposed adaptive modulation
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