ENERGY EFFICIENT ROUTING ALGORITHM
Content
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
ENERGY EFFICIENT ROUTING ALGORITHM
Suparna Bhakat1, Ashok Shaky2
1
Research Scholar, Saroj Institute of Technology And Management, Lucknow.
Assistant Professor, Computer Science Department, Saroj Institute of Technology And Management, Lucknow
[email protected]
2
Abstract: The main aim of energy efficient routing is to
minimize the energy required to transmit or receive packets also
called as active communication energy. Inactive energy is the
energy which not only tries to reduce the energy consumed
when a mobile node stays idle but also listens to the wireless
medium for any possible communication requests from other
nodes. To conserve energy, many energy efficient routing
protocols have been proposed. Networks of small, inexpensive,
disposable, smart sensors are emerging as a new technology with
tremendous potential. Wireless sensor networks can be randomly
deployed inside or close to phenomenon to be monitored. The
advantage of these networks is the fact that they are selfconfiguring, which means that a sensor network can be deployed
randomly on a battlefield, in a disaster area or in an inaccessible
area without the need for human intervention. The energy
supplies of nodes are not replenished or replaced and therefore
nodes only participate in the network for as long as they have
energy. This fact necessitates energy efficiency in the design of
every aspect of such nodes. Energy consumption in sensor nodes
occurs mainly due to computational processing and, to a greater
extent, communication. The routing protocol employed by these
sensor nodes can minimize the number of transmissions that
nodes make as well as the computational complexity of routing
path selection. It is therefore of critical importance that the
routing protocol be designed with energy efficiency in mind.
I.
INTRODUCTION
Lifetime of wireless sensor node is correlated with the
battery current usage profile. By being able to estimate the
energy utilization of the sensor nodes, routing protocols and
applications are able to construct informed decisions that
enhance the lifetime of the sensor network. However, it is in
general not feasible to measure the energy consumption on
sensor node platforms. Reducing energy consumption and size
are significant research topics in order to make wireless sensor
networks (WSN) deployable. As most WSN nodes are battery
powered, their lifetime is extremely reliant on their energy
consumption. Due to the low cost of an individual node, it is
more cost effectual to substitute the entire node than to locate
the node and replace or recharge its battery supply. Node
lifetime is a frequently discussed topic in platform design and
analysis. In the last couple of years new platforms have
demonstrated several new techniques for reducing power
leakage during sleep time. Hardware components are
characterized at a very detailed level to simulate power
consumption of a node as close as possible. Another method
uses hybrid automata models for analyzing power utilization of
a node at the operating system level. In this paper describes an
energy measurement system based on a node current
consumption usage. To guesstimate the life span of activity
monitoring system, the energy characteristics of sensor node is
measured obliquely. One node is connected in series to a
resistor. Using oscilloscope, voltage drop over the resistor is
calculated. Current is calculated using values given by the
oscilloscope.
II.
ENERGY EFFICIENT ROUTING
A network that can function as long as possible is an ideal
network. In an ad- hoc system the main limitation is the
availability of power. Power is consumed on resources such as
running the onboard electronics, the number of processes
running and overheads required to maintain connectivity. The
computing devices consist of mobile batteries in an adhoc
network that communicates over the wireless medium. The
memory space and the processing capacity of the nodes
increase at a very quick speed, the battery method lags far
behind. Hence, energy efficient protocols are derived to
conserve energy and to increase the network life time as well
as increase the device and network operation time. In
particular, energy efficient routing may be the most important
design criterion for MANETs, as mobile nodes will be
powered by batteries with limited capacity. Overall network
lifetime decreases because of the power failure of a mobile
node. Also the ability to forward packets on behalf of others
decreases. For this reason, many research efforts have been
applied to develop energy-aware routing protocols. Instead of
average case the worst case i.e when a first node dies out is
maximized.Some energy proficient routing protocol includes
Local Energy- Aware Routing based on AODV
(LEARAODV), Power-Aware Routing based on AODV (PARAODV), and Lifetime Prediction Routing based on AODV
(LPR- AODV).
III.
PROPOSED ALGORITHM
The main aspire of the algorithm is to provide an energy
saving conception for the mobile adhoc networks. Energy
saving conception depends on numerous other factors like
delay in the network, no of hops between source and target.
These techniques are implemented in the network layer of
the protocol which takes care of the routing theory. As
there are no routers here the intermediate hoping acts like
routing and thus this algorithm tends to diminish all the
parameters applied in the network layer. Dynamic Source
Routing only stores an arbitrary path between a source and
destination pair during its route discovery phase. When a
RREQ packet is sent by a source then it is flooded till it
reaches the target and on reaching the packets are destroyed
and the path traversed is simply cached there. But in this
algorithm all the multipaths are first analyzed and their
energy loss and transmission delay along with energy loss
and the path is collected statistically. From there the
minimum and the best path on the basis of hop count and
energy loss is calculated. Hence the destination stores only
this path in its cache for the chosen source.
Hence based on this two important factors and the
formula mentioned above for computation a better energy
efficient algorithm was developed. Sometimes in a MANET,
the number of hops proves negative in determining the least
energy path as the nodes are mobile and infrastructure less,
so distance parameter obviously overcomes the difficulty
with the hop counts here. But still comparison between
algorithms of dynamic source routing and proposed algorithm
can be done on the basis of hop counts.
Numerous
general
assumptions
taken
during
establishment of an ad hoc network are as follows:
1) All the nodes partake in the transmission
44 | P a g e
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
2) The diameter of MANETs should be very
IV.
SIMULATION AND RESULT
diminutive (mostly within 30 hops)
We have taken the simulation environment as
3) The speed of the nodes should be modest
omnet++. This simulation IDE is freely available for
4) Link must be unidirectional with two proper in
academic purposes. The network of particular dimension was
gates and the out gates for each node.
constructed in the Omnet++ IDE and the diverse
5) Just one node can be sending and one receiving
component of the project is as follows:
node during a transmission in the network.
(1) Ned file
In this research implemented proposed algorithm taking 5,
(2) Network file
10, 60 nodes in the network respectively. In each network a
(3) Header file <Packet_h.h>
fixed node was chosen as the source node and then a set of
(4) C++ file
destination was chosen. Then for each source and
This structure was repeated for networks of dimension
destination pair the minimum energy loss path was
5, 10 and 60. Every time the simulation returns the average
calculated.
minimum hop count and energy loss. All the values were
recorded in a table after every simulation for each size of
Working of Proposed Algorithm
network for proposed algorithm. More over the network life
The working of proposed algorithm is as follows:
is also improved as the packets are not back flooded and all the
Initialization parameters:
multiple paths from source to destination is traversed in order
1) Setup a network with N nodes and E edges
to collect the best among them.
2) Select a node S as the source node
3) Select a set of nodes from N apart from S to act as
V.
CONCLUSION
target nodes.
In
this
paper
we
proposed
a routing approach with the aim
4) Setup the delay parameter in the channel.
to minimize the energy required to transmit or receive packets
5) Initialize the type of the packet with parameters
also called as active communication energy. Inactive energy is
like hop count, energy and path.
the energy which not only tries to reduce the energy consumed
6) The edges E of network (connections) are
when a mobile node stays idle but also listens to the wireless
unidirectional as per the assumptions.
medium for any possible communication requests from other
7) Therefore two distinct types of gates are taken for
nodes. Transmission power control approach method and load
input and output
distribution method are the two methods which diminishes
8) Initial packets hop count set as 0 and also energy
active communication energy. The sleep or power-down mode
=0.0mW
method reduces inactive energy. Both the protocol has specific
For (each target node in the set)
benefits and drawbacks and therefore is applicable for certain
{
situations. We also implemented our approach using Omnet++
Initiate from the source node
for the network with nodes 5, 10 and 60. Using this simulation
While(packet− > path[ ] isnotnull)
we showed the discovered routes for packet forwarding and we
If(node==target node)
also calculated the energy consumed for different path. Out
Accumulate packet parameters for further calculation
simulator always select the path that involves least energy loss.
else
Another major advantage of this algorithm is that it
{
prevents back flooding of the packets. If a node is already
Int gaterange= count for the no of out gates for the
added to the packet path then no more flooding of packets
node
occur to that particular node. This not only saves the
Copy the Route Request (RREQ) message packets
network obstruction but also enhances the life span of the
Packet(hop_count) = packet(hop_count) + 1
packets and the network. The major disadvantage of this
Determine energy En with the following formula
algorithm is that it follows breadth first search approach for
En = (packet(hop_count) ∗ delay) + (x ∗ packet_size)
the path discovery or intermediate node discovery. Hence all
+ const
the child nodes of a particular node added to the path is
(here x required for calculation will be that of the
also traversed even if they lead to a dead end after some
sending as well as of the intermediate nodes.)
iterations.
Packet− > energy = En
REFERENCES
For(eachgate <= gaterange)
[1] IEEE 802.11e Working Group, Specific Requirements Part 11:
{
Wireless LAN Medium Access Control (MAC) and Physical
Get node connected to gate
Layer (PHY) Specifications, Amendment 8: Medium Access
I f (packet−> path does not contain node)
Control (MAC) Quality of Service Enhancements, IEEE
{
Standard, 2005.
Add node to packet− >path
[2] D. Johnson, Y. Hu and D. Maltz, “The Dynamic Source Routing
Send copy packets to node through gate
protocol (DSR) for mobile ad hoc networks for IPv4”, IETF
}
RFC 4728, vol.15, pages 153-181, 2007.
[3] Lima, L. Calsavara, “A Paradigm Shift in the Design of Mobile
}
Applications Advanced
Information
Networking
and
}
Applications Workshops”, AINAW 2008, 22nd International
Get next node from packet− >path}
Conference on Vol. 25, Issue 28, pages 1631-1635, March 2008.
Hop[i]= least hop count from the collected data
[4] Y.Yoo and D.P. Agrawal, “Why it pays to be selfish in
Energy[i]= least energy loss from the accumulated data
MANETs”, IEEE Wireless Communications Magazine, vol.
i=i+1
13, no.6, pages 87-97, 2006.
Pick another node from the set as destination node}
[5] S. Toumpis and D. Toumpakaris, “Wireless ad hoc networks
and related topologies applications and research challenges”,
45 | P a g e
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
vol. 123, no. 6, pages 232- 241, June 2006.
[6] Y.Natchetoi, H.Wu, “Service-oriented mobile applications for ad
hoc networks”, IEEE, ICSC, pages 405-412, 2008.
[7] Jorma Jormakka, Henryka Jormakka, and Janne V, “A
Lightweight Management System for a Military Ad Hoc
Network”, COIN 2007, LNCS 5200, pages 533–543, SpringerVerlag Berlin Heidelberg 2008.
[8] Ostermaier, Benedi kt, Dotzer, Florian, “Enhancing the security
of local danger warnings in VANETs, IEEE, pages 422431,ARES-2007
46 | P a g e
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
ENERGY EFFICIENT ROUTING ALGORITHM
Suparna Bhakat1, Ashok Shaky2
1
Research Scholar, Saroj Institute of Technology And Management, Lucknow.
Assistant Professor, Computer Science Department, Saroj Institute of Technology And Management, Lucknow
[email protected]
2
Abstract: The main aim of energy efficient routing is to
minimize the energy required to transmit or receive packets also
called as active communication energy. Inactive energy is the
energy which not only tries to reduce the energy consumed
when a mobile node stays idle but also listens to the wireless
medium for any possible communication requests from other
nodes. To conserve energy, many energy efficient routing
protocols have been proposed. Networks of small, inexpensive,
disposable, smart sensors are emerging as a new technology with
tremendous potential. Wireless sensor networks can be randomly
deployed inside or close to phenomenon to be monitored. The
advantage of these networks is the fact that they are selfconfiguring, which means that a sensor network can be deployed
randomly on a battlefield, in a disaster area or in an inaccessible
area without the need for human intervention. The energy
supplies of nodes are not replenished or replaced and therefore
nodes only participate in the network for as long as they have
energy. This fact necessitates energy efficiency in the design of
every aspect of such nodes. Energy consumption in sensor nodes
occurs mainly due to computational processing and, to a greater
extent, communication. The routing protocol employed by these
sensor nodes can minimize the number of transmissions that
nodes make as well as the computational complexity of routing
path selection. It is therefore of critical importance that the
routing protocol be designed with energy efficiency in mind.
I.
INTRODUCTION
Lifetime of wireless sensor node is correlated with the
battery current usage profile. By being able to estimate the
energy utilization of the sensor nodes, routing protocols and
applications are able to construct informed decisions that
enhance the lifetime of the sensor network. However, it is in
general not feasible to measure the energy consumption on
sensor node platforms. Reducing energy consumption and size
are significant research topics in order to make wireless sensor
networks (WSN) deployable. As most WSN nodes are battery
powered, their lifetime is extremely reliant on their energy
consumption. Due to the low cost of an individual node, it is
more cost effectual to substitute the entire node than to locate
the node and replace or recharge its battery supply. Node
lifetime is a frequently discussed topic in platform design and
analysis. In the last couple of years new platforms have
demonstrated several new techniques for reducing power
leakage during sleep time. Hardware components are
characterized at a very detailed level to simulate power
consumption of a node as close as possible. Another method
uses hybrid automata models for analyzing power utilization of
a node at the operating system level. In this paper describes an
energy measurement system based on a node current
consumption usage. To guesstimate the life span of activity
monitoring system, the energy characteristics of sensor node is
measured obliquely. One node is connected in series to a
resistor. Using oscilloscope, voltage drop over the resistor is
calculated. Current is calculated using values given by the
oscilloscope.
II.
ENERGY EFFICIENT ROUTING
A network that can function as long as possible is an ideal
network. In an ad- hoc system the main limitation is the
availability of power. Power is consumed on resources such as
running the onboard electronics, the number of processes
running and overheads required to maintain connectivity. The
computing devices consist of mobile batteries in an adhoc
network that communicates over the wireless medium. The
memory space and the processing capacity of the nodes
increase at a very quick speed, the battery method lags far
behind. Hence, energy efficient protocols are derived to
conserve energy and to increase the network life time as well
as increase the device and network operation time. In
particular, energy efficient routing may be the most important
design criterion for MANETs, as mobile nodes will be
powered by batteries with limited capacity. Overall network
lifetime decreases because of the power failure of a mobile
node. Also the ability to forward packets on behalf of others
decreases. For this reason, many research efforts have been
applied to develop energy-aware routing protocols. Instead of
average case the worst case i.e when a first node dies out is
maximized.Some energy proficient routing protocol includes
Local Energy- Aware Routing based on AODV
(LEARAODV), Power-Aware Routing based on AODV (PARAODV), and Lifetime Prediction Routing based on AODV
(LPR- AODV).
III.
PROPOSED ALGORITHM
The main aspire of the algorithm is to provide an energy
saving conception for the mobile adhoc networks. Energy
saving conception depends on numerous other factors like
delay in the network, no of hops between source and target.
These techniques are implemented in the network layer of
the protocol which takes care of the routing theory. As
there are no routers here the intermediate hoping acts like
routing and thus this algorithm tends to diminish all the
parameters applied in the network layer. Dynamic Source
Routing only stores an arbitrary path between a source and
destination pair during its route discovery phase. When a
RREQ packet is sent by a source then it is flooded till it
reaches the target and on reaching the packets are destroyed
and the path traversed is simply cached there. But in this
algorithm all the multipaths are first analyzed and their
energy loss and transmission delay along with energy loss
and the path is collected statistically. From there the
minimum and the best path on the basis of hop count and
energy loss is calculated. Hence the destination stores only
this path in its cache for the chosen source.
Hence based on this two important factors and the
formula mentioned above for computation a better energy
efficient algorithm was developed. Sometimes in a MANET,
the number of hops proves negative in determining the least
energy path as the nodes are mobile and infrastructure less,
so distance parameter obviously overcomes the difficulty
with the hop counts here. But still comparison between
algorithms of dynamic source routing and proposed algorithm
can be done on the basis of hop counts.
Numerous
general
assumptions
taken
during
establishment of an ad hoc network are as follows:
1) All the nodes partake in the transmission
44 | P a g e
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
2) The diameter of MANETs should be very
IV.
SIMULATION AND RESULT
diminutive (mostly within 30 hops)
We have taken the simulation environment as
3) The speed of the nodes should be modest
omnet++. This simulation IDE is freely available for
4) Link must be unidirectional with two proper in
academic purposes. The network of particular dimension was
gates and the out gates for each node.
constructed in the Omnet++ IDE and the diverse
5) Just one node can be sending and one receiving
component of the project is as follows:
node during a transmission in the network.
(1) Ned file
In this research implemented proposed algorithm taking 5,
(2) Network file
10, 60 nodes in the network respectively. In each network a
(3) Header file <Packet_h.h>
fixed node was chosen as the source node and then a set of
(4) C++ file
destination was chosen. Then for each source and
This structure was repeated for networks of dimension
destination pair the minimum energy loss path was
5, 10 and 60. Every time the simulation returns the average
calculated.
minimum hop count and energy loss. All the values were
recorded in a table after every simulation for each size of
Working of Proposed Algorithm
network for proposed algorithm. More over the network life
The working of proposed algorithm is as follows:
is also improved as the packets are not back flooded and all the
Initialization parameters:
multiple paths from source to destination is traversed in order
1) Setup a network with N nodes and E edges
to collect the best among them.
2) Select a node S as the source node
3) Select a set of nodes from N apart from S to act as
V.
CONCLUSION
target nodes.
In
this
paper
we
proposed
a routing approach with the aim
4) Setup the delay parameter in the channel.
to minimize the energy required to transmit or receive packets
5) Initialize the type of the packet with parameters
also called as active communication energy. Inactive energy is
like hop count, energy and path.
the energy which not only tries to reduce the energy consumed
6) The edges E of network (connections) are
when a mobile node stays idle but also listens to the wireless
unidirectional as per the assumptions.
medium for any possible communication requests from other
7) Therefore two distinct types of gates are taken for
nodes. Transmission power control approach method and load
input and output
distribution method are the two methods which diminishes
8) Initial packets hop count set as 0 and also energy
active communication energy. The sleep or power-down mode
=0.0mW
method reduces inactive energy. Both the protocol has specific
For (each target node in the set)
benefits and drawbacks and therefore is applicable for certain
{
situations. We also implemented our approach using Omnet++
Initiate from the source node
for the network with nodes 5, 10 and 60. Using this simulation
While(packet− > path[ ] isnotnull)
we showed the discovered routes for packet forwarding and we
If(node==target node)
also calculated the energy consumed for different path. Out
Accumulate packet parameters for further calculation
simulator always select the path that involves least energy loss.
else
Another major advantage of this algorithm is that it
{
prevents back flooding of the packets. If a node is already
Int gaterange= count for the no of out gates for the
added to the packet path then no more flooding of packets
node
occur to that particular node. This not only saves the
Copy the Route Request (RREQ) message packets
network obstruction but also enhances the life span of the
Packet(hop_count) = packet(hop_count) + 1
packets and the network. The major disadvantage of this
Determine energy En with the following formula
algorithm is that it follows breadth first search approach for
En = (packet(hop_count) ∗ delay) + (x ∗ packet_size)
the path discovery or intermediate node discovery. Hence all
+ const
the child nodes of a particular node added to the path is
(here x required for calculation will be that of the
also traversed even if they lead to a dead end after some
sending as well as of the intermediate nodes.)
iterations.
Packet− > energy = En
REFERENCES
For(eachgate <= gaterange)
[1] IEEE 802.11e Working Group, Specific Requirements Part 11:
{
Wireless LAN Medium Access Control (MAC) and Physical
Get node connected to gate
Layer (PHY) Specifications, Amendment 8: Medium Access
I f (packet−> path does not contain node)
Control (MAC) Quality of Service Enhancements, IEEE
{
Standard, 2005.
Add node to packet− >path
[2] D. Johnson, Y. Hu and D. Maltz, “The Dynamic Source Routing
Send copy packets to node through gate
protocol (DSR) for mobile ad hoc networks for IPv4”, IETF
}
RFC 4728, vol.15, pages 153-181, 2007.
[3] Lima, L. Calsavara, “A Paradigm Shift in the Design of Mobile
}
Applications Advanced
Information
Networking
and
}
Applications Workshops”, AINAW 2008, 22nd International
Get next node from packet− >path}
Conference on Vol. 25, Issue 28, pages 1631-1635, March 2008.
Hop[i]= least hop count from the collected data
[4] Y.Yoo and D.P. Agrawal, “Why it pays to be selfish in
Energy[i]= least energy loss from the accumulated data
MANETs”, IEEE Wireless Communications Magazine, vol.
i=i+1
13, no.6, pages 87-97, 2006.
Pick another node from the set as destination node}
[5] S. Toumpis and D. Toumpakaris, “Wireless ad hoc networks
and related topologies applications and research challenges”,
45 | P a g e
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 1 (Jan-Feb 2015), PP. 44-46
vol. 123, no. 6, pages 232- 241, June 2006.
[6] Y.Natchetoi, H.Wu, “Service-oriented mobile applications for ad
hoc networks”, IEEE, ICSC, pages 405-412, 2008.
[7] Jorma Jormakka, Henryka Jormakka, and Janne V, “A
Lightweight Management System for a Military Ad Hoc
Network”, COIN 2007, LNCS 5200, pages 533–543, SpringerVerlag Berlin Heidelberg 2008.
[8] Ostermaier, Benedi kt, Dotzer, Florian, “Enhancing the security
of local danger warnings in VANETs, IEEE, pages 422431,ARES-2007
46 | P a g e
