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Reducing Power Consumption in Sensor Network Using Sensor MAC Protocol

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Wireless sensor networks are quickly gaining popularity due to the fact that they are potentially low cost solutions to a variety of real world challenges. Their low cost provides a means to deploy large sensor arrays in a variety of conditions capable of performing both military and civilian tasks. This technology consists of some of the electronic devices which work to run this system successfully and all those have some amount of power consumptions. It is a challenge of maximizing the processing capabilities and energy reserves of Wireless sensor nodes while also securing them against attackers. So, finally we have decided to work on finding out the optimum solution for controlling the power and saving energy. There are number of ways to reduce power consumption and MAC protocol is one of them. So we describe Sensor MAC protocol to reduce power consumption.
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Reducing Power Consumption in Sensor Network
Using Sensor MAC Protocol
Nilay Patel, Mayuresh Bhalekar, Pandya Nirav, Syed Rizvi, Khaled Elleithy
Computer Science and Engineering Department
University of Bridgeport, Bridgeport, CT, 06604
Abstract- Wireless sensor networks are quickly gaining
popularity due to the fact that they are potentially low cost
solutions to a variety of real world challenges. Their low cost
provides a means to deploy large sensor arrays in a variety of
conditions capable of performing both military and civilian
tasks. This technology consists of some of the electronic
devices which work to run this system successfully and all
those have some amount of power consumptions. It is a
challenge of maximizing the processing capabilities and
energy reserves of Wireless sensor nodes while also securing
them against attackers. So, finally we have decided to work on
finding out the optimum solution for controlling the power and
saving energy. There are number of ways to reduce power
consumption and MAC protocol is one of them. So we describe
Sensor MAC protocol to reduce power consumption.
I. INTRODUCTION
A sensor network is a group of specialized
transducers with a communications infrastructure
intended to monitor and record conditions at diverse
locations. Commonly monitored parameters are
temperature, humidity, pressure, wind direction and
speed, illumination intensity, vibration intensity, sound
intensity, power-line voltage, chemical concentrations,
pollutant levels and vital body functions. A sensor
network consists of multiple detection stations called
sensor nodes, each of which is small, lightweight and
portable. Every sensor node is equipped with a transducer,
microcomputer, transceiver and power source. The
transducer generates electrical signals based on sensed
physical effects and phenomena. The microcomputer
processes and stores the sensor output. The transceiver,
which can be hard-wired or wireless, receives commands
from a central computer and transmits data to that
computer. The power for each sensor node is derived
from the electric utility or from a battery.
Sensory data comes from multiple sensors of
different modalities in distributed locations. The smart
environment needs information about its surroundings as
well as about its internal workings; this is captured in
biological systems by the distinction between
exteroceptors and proprioceptors. The challenges in the
hierarchy of: detecting the relevant quantities, monitoring
and collecting the data, assessing and evaluating the
information, formulating meaningful user displays, and
performing decision-making and alarm functions are
enormous.
The information needed by smart environments is
provided by Distributed Wireless Sensor Networks, which
are responsible for sensing as well as for the first stages of
the processing hierarchy. The importance of sensor
networks is highlighted by the number of recent funding
initiatives, including the DARPA SENSIT program,
military programs, and NSF Program Announcements.
A. Problem Identification
In wireless network it is an important task to make a
system in such a way where power consumption is
decrease and efficiency should be increase. As almost all
equipment used in this technology or task are run by an
electricity or saved power (energy). This technology used
in such a way where energy consumption has to me
minimum in terms of getting more efficient, accurate and
cost effective output. Power is very important in wireless
sensor network so it is required to find out some solution
to minimize energy consumption in wireless sensor
network.
There are number of nodes involved in WSN all
nodes are likely to relay on limited battery power.
Transmitting at unnecessary high power not only reduces
the life time of nodes and network but also introduce
excessive interferences.
II. RELATED WORK
Now we will emphasis on Medium Access Control
Protocol for wireless network manage the usage of the
radio Interface. A medium-access control (MAC)
protocol designed for wireless sensor networks. Wireless
sensor networks use battery-operated computing and
sensing devices. A network of these devices will work
together for a common application such as
environmental monitoring.
We expect sensor networks to be deployed in an ad
hoc fashion, with individual nodes remaining largely
inactive for long periods of time, but then becoming
suddenly active when something is detected. These
characteristics of sensor networks and applications
prompt a MAC that is different from traditional wireless
MACs in almost every way: energy conservation and
self-configuration are primary goals, while per-node
fairness and latency are less important. MAC uses three
novel techniques to reduce energy consumption and
support self-configuration.
To reduce energy consumption in listening to an idle
channel, nodes periodically sleep. Neighboring nodes
form virtual clusters to auto-synchronize on sleep
schedules. Inspired by PAMAS, S-MAC also sets the
radio to sleep during transmissions of other nodes.
Unlike PAMAS, it only uses in-channel signaling. S-
MAC applies message passing to reduce contention
latency for sensor-network applications that require
store-and-forward processing as data move through the
network. Wireless sensor networks have an additional
aspect: as sensor nodes are generally battery-operated,
energy consumption is very important. The radio on a
sensor node is usually the component that uses most
energy. Not only transmitting costs energy; receiving, or
merely scanning the air for communication, can use up
to half as much, depending on the type of radio.
III. PROPOSED SOLUTION
A. PAMAS (Power Aware Multi-Access Protocol)
In this paper we develop a new multi-access protocol
for ad hoc radio networks. The protocol is based on the
original MAC protocol with the addition of a separate
signaling channel. The unique feature of our protocol is
that it conserves battery power at nodes by intelligently
powering off nodes that are not actively transmitting or
receiving packets. The manner in which nodes power
themselves off does not influence the delay or throughput
characteristics of our protocol. We illustrate the power
conserving behavior of PAMAS via extensive simulations
performed over ad hoc networks containing 10--20 nodes.
Our results indicate that power savings of between 10%
and 70 % are attainable in most systems.
B. Sensor-MAC (S-MAC): Medium Access Control for
Wireless Sensor Networks
S-MAC is a medium-access control (MAC) protocol
designed for wireless sensor networks. Wireless sensor
networks use battery-operated computing and sensing
devices. A network of these devices will work together
for a common application such as environmental
monitoring. We expect sensor networks to be deployed in
an ad hoc fashion, with individual nodes remaining
largely inactive for long periods of time, but then
becoming suddenly active when something is detected.
These characteristics of sensor networks and applications
motivate a MAC that is different from traditional wireless
MACs such as IEEE 802.11 in almost every way: energy
conservation and self-configuration are primary goals,
while per-node fairness and latency are less important.
S-MAC uses three novel techniques to reduce energy
consumption and support self-configuration. To reduce
energy consumption in listening to an idle channel, nodes
periodically sleep. Neighboring nodes form virtual
clusters to auto-synchronize on sleep schedules. Inspired
by PAMAS, S-MAC also sets the radio to sleep during
transmissions of other nodes. Unlike PAMAS, it only uses
in-channel signaling. Finally, S-MAC applies message
passing to reduce contention latency for sensor-network
applications that require store-and-forward processing as
data move through the network.
Fig. 1 (a). The S-MAC duty cycle, the arrow indicates transmitted
and received messages
Fig. 2(b). T-MAC with adaptive active times
T
ABLE
I
TYPICAL POWER CONSUMPTION OF WSN
Modes Typical current Power consumption
Transmit 32mA 95mW
Receive 18mA 55mW
Ideal 8mA 25mW
Sleep 20mA 60mW
C. T-MAC Protocol
Above figure shows the basic scheme of the T-MAC
protocol. Every node periodically wakes up to
communicate with its neighbors, and then go to sleep
again until the next frame. Meanwhile, new messages are
queued. Nodes communicate with each other using a
Request-To-Send RTS), Clear-To-Send (CTS), Data,
Acknowledgement (ACK) scheme, which provides both
collision avoidance and reliable transmission. This
scheme is well known and used, for example, in the IEEE
802.11. A node will keep listening and potentially
transmitting, as long as it is in an active period. An active
period ends when no activation event has occurred for a
time TA.
Now we discuss about S-MAC Protocol which is
called sensor MAC protocol and try to minimize energy
consumption using sleep/listen schedule. There are three
main energy wastage events occur at a MAC layer and are
follows: (i) collision (ii) overhearing and (iii) idle listing.
Collision result in energy waste due to re transmission of
crashed packets. Overhearing occur when a particular
node listening for transmission which is not for it. And
idle listening occurs when a node is looking for any
possible data. All these cause waste of unnecessary
energy. So power wasted by overhearing and idle listing
is also important as collision. The main idea of S-MAC
protocol is to put a node to sleep mode time to time to
reduce energy wasted when above event occurs. A
particular node goes into sleep mode when it is not
engaged in any kind of transmission and when its
neighbors are involve in transmission and moreover this
will reduce collision and overhearing. This cause reduces
in listing time resulting saving the power.
A cycle of S-MAC have listen and sleep state. A
sensor node follow pre-define schedule to wakeup or
sleep in following condition(i)when a neighbor is
communicating (ii)node wakeup when a neighbor finish
communication if it need to relay packet. This is done by
only overhearing neighbor‘s RTS (Ready To Send) and
CTS (Clear To Send) exchange before a node goes to
sleep to reduce latency caused by sleeping.
D. Queuing Model for S-MAC
We consider a system made up of N interfacing
nodes. And a traffic arrival is at the rate of λ packets per
unit time. But in WSN events are sensed randomly. So the
total arrival rate to the channel is Nλ. The number of
packets are serviced per unit time is called channel
service rate and it is denoted by µ by shared channel. In
this way the service time is calculated by the sum of delay
components which is sleep delay due to lost transmission,
contention time and transmission delay. Now we discuss
sleep delay encountered by a packet.
E. Sleep Latency
Sleep delay can be occur in two situation that the
packet is new arrival or it’s from the queue.If an incoming
packet sees empty queue then the packet is serviced as
current cycle only when it arrives within current
contention window(αT),otherwise it has to wait for next
cycle as shown in figure below.
Now let say if packet is arrived at random time then
arriving packet sees am empty queue and still it suffers
from sleep delay and it caused by unfortunate combining
of two independent events: empty queue and missing
contention period. this event is given by
(
)
(
)
1
1 1P
ρ α
Where ρ=Nλ.
This is the probability that a node’s queue is non-
empty, and T is total cycle time. If the packet arrives at
any time instant equally likely after the contention period
then the delay caused by sleep can be calculated as
1
1
2
S T
α
=
(1)
If the packer is from queue than the sleep delay can
be avoided if adaptive listening causes the next hop node
by overhearing neighbor’s RTS/CTS exchanges, to wake
up in time to relay the packet queued and scheduled to
transmit from previous hop node. But adaptive listing
works only in alternative hopes, so sleeping will cause a
node to miss its neighbor’s RTS/CTS exchanges.
Considering the effect of adaptive listening and the
probability that an incoming packet sees a non-empty
queue and encounters a sleep delay is given as:
2
P
βρ
= (2)
Fig 2. The sleep/listen cycle.
Where
2
h
h
β
= and h is number of hops traversed from
the source to destination. The delay encountered here
calculated as:
2
1
2
S T
α
=
(3)
And so overall sleep latency is given by
1 1 2 2
S PS P S
= +
(4)
F. Total Latency
In addition to sleep delay a packet suffers from
contention delay and transmission delay, which are
computed as follows. Contention delay is the time a node
spends to win contention, which is also called channel
access delay. The number of times a node will attempt to
contend for the channel before success in a given backoff
stage, is a geometrical random variable with a probability
1/C. Thus the expectation of the total time required to win
contention is given by,
( )
1
1 0
1 2
2
i
m
W pW p
C
=
+ +
=
(5)
Where, W denotes minimum contention window size and
m is the maximum number of backoff stages. The
probability of packet collision, p, is defined as the
probability that two or more nodes transmit in the same
slot time and is derived in as:
1
1
1 1
N
P
C
= (6)
where N is the number of interfering nodes. Equations (5)
and (6) can be solved numerically to obtain the values of
p and C. Transmission delay (T) is just the time for the
radio to transmit a packet, which is a function of channel
data rate. The total service time is given as:
1
S C T
u
= + +
(7)
And the overall latency encountered by a packet is given
by the sum of service time and the queuing delay obtained
for an M/G/1 system by applying the Pollaczek-Khinchin
formula. The average latency is written as:
(
)
2 2
1
1
(1 )
s
N u
L
u s
λ σ
ρ
+
= +
(8)
Where, σ
2
is the variance of the service time distribution
and ρ is equal to Nλ
IV. PERFORMANCE EVALUATION
Numerical results are obtained using the formulation
described in the previous section and Table I lists the
important parameters used in the analysis. For instance
the following configurations are used in the simulation:
Channel bandwidth is 20 Kbps, N= 5, and Data Packet
size 50Bytes.
Figure 3 shows the performance of SMAC for
varying duty cycle values. Duty cycle is defined as the
fraction of total cycle time that a node listens, i.e., L/T in
Figure 2.From Figure 3, as expected, average latency per
packet is high at low duty cycles, because nodes sleep for
longer duration of time and introduce large sleep delay.
However energy consumed by a node increases with duty
cycle since the node ideally listens for extended period of
time. The details for all the configuration parameters are
presented in Table II. All simulation and experiments
have performed based on the mathematical equations
derived in the previous section along with these parameter
values.
Fig 3.Latency and energy consumption results of SMAC
obtained from queuing modeling.
A. Simulation Environment
To validate our results, we simulated the performance
of SMAC. A simple five-node network topology was
used. Four nodes generate exponentially distributed traffic
to a single sink node. Simulation parameters are listed in
table below.
B. Simulation Parameters
For the same network scenario, average energy
consumption and latency obtained from analytic modeling
were compared with simulation results. Fig 3 shows the
results for average latency per packet at varying duty
cycles. At 95% confidence intervals, it shows the
simulation and analytical results are in reasonably good
agreement. Other simulation data points show similar
pattern, but are not included for the clarity of the figure.
The figure shows that at low duty cycle, i.e., a node sleeps
for a longer duration; the difference in packet latency for
different arrival rates is large. This is because, at high
arrival rates the demand for the channel is much higher
than that at low arrival rates, therefore the performance is
degraded much more at high arrival rates. As the duty
cycle increases, the difference in packet latency for low
and high arrival rates tends to disappear. The figure
reaffirms the intuition that low duty cycle operation is
appropriate for low arrival rates but can cause excessive
latency at high arrival rates.
Fig 5 shows the results for average energy
consumption obtained using analysis and simulation,
respectively. Again the simulation results are at 95%
confidence interval. The figure shows that the differences
in the average energy consumption for different arrival
rates increases as duty cycle increases. This is because, at
low duty cycle, sleep behavior dominates energy
consumption. As the duty cycle increases, packet
transmission tends to dominate energy consumption.
Therefore, low duty cycle operation is effective way to
limit energy consumption regardless of the traffic load.
V. CONCLUSION AND FUTURE WORK
This is the first protocol to use sleep/active schedules
and it offers major decrease in energy consumption and
overcome of latency problem. We quantified the
performance impact of sleep in a sensor MAC protocol by
queuing analysis and simulation. Our results demonstrate
the tradeoff between latency and energy consumption
under varying duty cycles and for different packet arrival
rates. As future work, we plan to study the performance
impacts of sleep on the nodes that play different roles in
Fig.4 Latency results for SMAC obtained from queuing
analysis and simulation
Fig.5 Energy consumption results for SMAC obtained from
queuing analysis and simulation
TABLE II
DETAILS OF PARAMETERS
Channel bandwidth 20 kbps
Average packet size 50 Bytes
RTS,CTS,ACK size 30 Bytes
Reception power 13mW
Transmission Power 24.75mW
Idle Power 13mW
Sleep Power 15µW
the network such as ordinary, gateway, cluster head
nodes, etc.
REFERENCES
1. Mark A Perillio & Wendi B Heinemann, department of
electrical and computer Engg, University of Rochester.
2. Directional MAC protocol for ad-HOC network with
synchronization, By IEEE library, university of Florida.
3.”power control in distributed MACby V.Srinivasan &
Kee Chang Chua.
4. Subah Ramakrishna, Hong Huang, Manikanden
Balakrishnan Klipsch School of Electrical Engineering
New Mexico State UniversityLas Cruces, USA
... The sensor nodes consume power for sensing, communicating and data processing. This power is derived from the electric utility or from a battery [4]. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust. ...