22-05-2012, 01:48 PM
DBET: Demand Based Energy efficient Topology for MANETs
[attachment=22710]
Abstract
Energy efficient topology in Ad-hoc networks can
be achieved mainly in two different ways. In the first method,
network maintains a small number of nodes to form a connected
backbone and the remaining nodes sleep to conserve energy. This
method is effective for low traffic networks. Energy efficiency in
the second method is achieved by power control technique. This
technique is effective in high traffic conditions. The first method
is not effective in high traffic conditions. Similarly, the second
method is not effective in low traffic networks. So, in this paper
we propose a Demand Based Energy efficient Topology (DBET)
to reduce the energy consumption for mobile ad hoc network, by
dynamically adjusting the topology for various network traffic
conditions. We have simulated our proposed protocol DBET
by using AODV [8] as routing protocol in network simulator
ns2.33 [1] and compared with AODV and SPAN [3]. The
simulation studies revealed that the proposed scheme perform
better in terms of energy, delay, and delivery ratio.
Index Terms—Energy efficient topology, Routing, MANET.
I. INTRODUCTION
Mobile Ad-hoc Networks (MANETs) are self-organizing,
self-configuring and infrastructure-less multi-hop wireless networks,
where each node communicates with other nodes
directly or indirectly through intermediate nodes without any
infrastructure. Such temporary networks can be used in battlefields,
disaster areas, military applications, mining operations
and robot data acquisition. Besides these characteristics they
present challenges like limited energy, dynamic topology, low
bandwidth and security. The description of the arrangement
of the MANETs, called topology, is usually temporary or
dynamically changed with time.
Energy conserving is one of the challenges because of
limited battery resource. The techniques which are used to
reduce the initial topology of network to save the energy
and increase the lifetime of network, with the preservence
of network connectivity, called topology control techniques.
Various techniques, in network layer, are proposed in the
literature to conserve energy. These techniques can be classified
mainly into two categories: by controlling the number
of nodes with the smaller link cost. In the first method a small
number of nodes awake to maintain the network connectivity
and remaining nodes go into sleep state to conserve energy.
This method is effective in low traffic conditions, because the
power consumption to keep nodes awake dominates the power
consumption in data transfer. In the second method, topology
is controlled by keeping lesser cost links in the network.
This method is effective in high data traffic because power
consumption in data transfer dominates the power required to
keep nodes awake. We combine the advantages of these two
techniques to dynamically adjust network topology for various
network traffic conditions.
In this paper, we present a demand based energy efficient
topology (DBET) that dynamically adjust network topology
for various network traffic conditions. We have simulated
our proposed protocol DBET by using AODV [8] as routing
protocol using network simulator ns2.33 [1] and compared
with AODV and AODV with SPAN [3]. The simulation studies
revealed that the proposed scheme perform better in terms of
energy, delay, and delivery ratio. In general network topology
is controlled by keeping small number of nodes awake as in
the first technique. The proposed DBET keeps more number
of nodes along the bulk data transfer path to conserve energy
by keeping low link cost as in the second technique.
The rest of the paper is organized as follows: the next section
provides a brief review of related studies. The third section
gives the design details of proposed DBET. Integration issues
of DBET with routing protocol is discussed in the forth section.
Simulation results along with discussions are provided in the
section 5. The last and final section concludes the paper with
same pointers to future research direction.
II. RELATED WORK
We briefly describe various techniques related to our work
topology control. Different topologies have been proposed in
the literature to reduce the energy consumption. These methods
can be classified into centralized controlling and distributed
computing methods. Ideally, for mobile ad hoc network a
topology should be computed and maintained in distributed,
asynchronous, and localized manner.
Li and Wan [6] described a distributed protocol to construct
a minimum power topology and developed an algorithm which
directly find a path whose length is within a constant factor of
the shortest path. The length of the path is measured in term of
energy consumption. This proposed algorithm used only local
information.
A topology based on minimum spanning tree, called localized
minimum spanning tree (LMST) was proposed by Li et
al. [5]. It is a localized distributed protocol with the following
properties: (1) the protocol generates a strongly connected
communication graph; (2) the degree of any node is at most
978-1-4244-9190-2/11/$26.00 ©2011 IEEE
six, and (3) the topology can be made symmetric by removing
asymmetric links without impairing connectivity.
A simple localized distributed topological control algorithm
(XTC) [12] is proposed by Wattenhpfer et al. Initially each
node u computes a total order (<u) over all its neighbors in
the network graph G with respect to decreasing link quality,
such as signal attenuation, and packet arrival rate. Then, each
node start exchanges the neighbor order information among
all neighbors. At the later point, each node locally selects
those neighboring nodes which will form its neighborhood in
the resulting topology control graph, based on the previously
exchanged neighbor order informations. It covers three main
advantages: 1) It does not assume the network graph to be
a Unit Disk Graph, 2) it works on weighted network graphs,
and 3) it does not require availability of the node position
information. Authors improved by adding node mobility and
extended XTC for mobile network [7].
An energy efficient dynamic path is maintained to send
data from source to destination for MANET is proposed in
Sheu, Tu, and Hsu [10]. Due to mobility existing paths
may not be energy efficient. So, each node in a data path
dynamically updates the path by adjusting its transmission
power. Each node in the networks determines its power for data
transmission and control packets transmission according to the
received beacon messages from its neighbors. In dynamic path
optimization technique protocols dynamically select energy
efficient path as per the requirement of dynamic topological
changes in the network [4].
Another class of topology control protocols based on the
k-nearest neighbors graph (k-NNG). In k-NNG every node is
connected to its k closest neighbors. The Local Information
No Topology protocol (LINT) [9] try to keep the number
of neighbors of a node within a low and high threshold
centered around an optimal value. But the optimal value is not
characterized. So the estimation of the number of neighbors
can be inaccurate and connectivity is not guaranteed. The
k-Neighbors (k-NEIGH) [2] protocol keeps the number of
neighbors of a node equal to, or slightly below, a given value
k. It connects each node with k-closest neighbor, instant of
require the knowledge of all the neighbors. The communication
graph that result is made symmetric by removing asymmetric
edges, which has k-upper bound neighbors. A characterization
of the critical neighbor numbers is discussed by Xue and
Kumar [13].
Another way of reducing the power consumption is by using
efficient energy path for transmitting packets. These methods
choose smaller edges in their path to reduce transmission
energy. Minimum energy consumption per packet can be
achieved by choosing the optimal energy consumed path from
source to destination. However, this technique does not take
the nodes’ energy capacity into consideration. So some nodes
may exhaust their power since energy consumption is not fair
among the nodes in the network. There for the network lifttime
decrease [11].
III. DEMAND BASED ENERGY EFFICIENT TOPOLOGY
In this section, we present a demand based energy efficient
topology (DBET) for mobile ad hoc network, which dynamically
changes the topology according to the network traffic
requirements. Initially we compute a small set of nodes, which
form a connected backbone, while the other nodes are put
off to conserve energy. These connected backbone is used for
routing the packets under low network load. When there is
a bulk data transfer between a pair of nodes, the topology
dynamically changes along the path between these nodes by
power control and route optimize technique to minimize the
power consumption.
The proposed DBET can be divided into four phases. The
first phase selects a small set of nodes that constitutes a independent
set of the network. The second phase is responsible for
electing more nodes to ensure that the selected nodes form a
connected backbone. Remaining nodes go to sleep to conserve
energy. Active node withdraw process is implement in the third
phase to remove redundant nodes in each region. To improve
the performance along the high traffic path we use the route
optimization with power control technique in the fourth phase.
In this technique, we change topology dynamically to connect
more nodes, around the routing path to minimize the total
power consumption.
[attachment=22710]
Abstract
Energy efficient topology in Ad-hoc networks can
be achieved mainly in two different ways. In the first method,
network maintains a small number of nodes to form a connected
backbone and the remaining nodes sleep to conserve energy. This
method is effective for low traffic networks. Energy efficiency in
the second method is achieved by power control technique. This
technique is effective in high traffic conditions. The first method
is not effective in high traffic conditions. Similarly, the second
method is not effective in low traffic networks. So, in this paper
we propose a Demand Based Energy efficient Topology (DBET)
to reduce the energy consumption for mobile ad hoc network, by
dynamically adjusting the topology for various network traffic
conditions. We have simulated our proposed protocol DBET
by using AODV [8] as routing protocol in network simulator
ns2.33 [1] and compared with AODV and SPAN [3]. The
simulation studies revealed that the proposed scheme perform
better in terms of energy, delay, and delivery ratio.
Index Terms—Energy efficient topology, Routing, MANET.
I. INTRODUCTION
Mobile Ad-hoc Networks (MANETs) are self-organizing,
self-configuring and infrastructure-less multi-hop wireless networks,
where each node communicates with other nodes
directly or indirectly through intermediate nodes without any
infrastructure. Such temporary networks can be used in battlefields,
disaster areas, military applications, mining operations
and robot data acquisition. Besides these characteristics they
present challenges like limited energy, dynamic topology, low
bandwidth and security. The description of the arrangement
of the MANETs, called topology, is usually temporary or
dynamically changed with time.
Energy conserving is one of the challenges because of
limited battery resource. The techniques which are used to
reduce the initial topology of network to save the energy
and increase the lifetime of network, with the preservence
of network connectivity, called topology control techniques.
Various techniques, in network layer, are proposed in the
literature to conserve energy. These techniques can be classified
mainly into two categories: by controlling the number
of nodes with the smaller link cost. In the first method a small
number of nodes awake to maintain the network connectivity
and remaining nodes go into sleep state to conserve energy.
This method is effective in low traffic conditions, because the
power consumption to keep nodes awake dominates the power
consumption in data transfer. In the second method, topology
is controlled by keeping lesser cost links in the network.
This method is effective in high data traffic because power
consumption in data transfer dominates the power required to
keep nodes awake. We combine the advantages of these two
techniques to dynamically adjust network topology for various
network traffic conditions.
In this paper, we present a demand based energy efficient
topology (DBET) that dynamically adjust network topology
for various network traffic conditions. We have simulated
our proposed protocol DBET by using AODV [8] as routing
protocol using network simulator ns2.33 [1] and compared
with AODV and AODV with SPAN [3]. The simulation studies
revealed that the proposed scheme perform better in terms of
energy, delay, and delivery ratio. In general network topology
is controlled by keeping small number of nodes awake as in
the first technique. The proposed DBET keeps more number
of nodes along the bulk data transfer path to conserve energy
by keeping low link cost as in the second technique.
The rest of the paper is organized as follows: the next section
provides a brief review of related studies. The third section
gives the design details of proposed DBET. Integration issues
of DBET with routing protocol is discussed in the forth section.
Simulation results along with discussions are provided in the
section 5. The last and final section concludes the paper with
same pointers to future research direction.
II. RELATED WORK
We briefly describe various techniques related to our work
topology control. Different topologies have been proposed in
the literature to reduce the energy consumption. These methods
can be classified into centralized controlling and distributed
computing methods. Ideally, for mobile ad hoc network a
topology should be computed and maintained in distributed,
asynchronous, and localized manner.
Li and Wan [6] described a distributed protocol to construct
a minimum power topology and developed an algorithm which
directly find a path whose length is within a constant factor of
the shortest path. The length of the path is measured in term of
energy consumption. This proposed algorithm used only local
information.
A topology based on minimum spanning tree, called localized
minimum spanning tree (LMST) was proposed by Li et
al. [5]. It is a localized distributed protocol with the following
properties: (1) the protocol generates a strongly connected
communication graph; (2) the degree of any node is at most
978-1-4244-9190-2/11/$26.00 ©2011 IEEE
six, and (3) the topology can be made symmetric by removing
asymmetric links without impairing connectivity.
A simple localized distributed topological control algorithm
(XTC) [12] is proposed by Wattenhpfer et al. Initially each
node u computes a total order (<u) over all its neighbors in
the network graph G with respect to decreasing link quality,
such as signal attenuation, and packet arrival rate. Then, each
node start exchanges the neighbor order information among
all neighbors. At the later point, each node locally selects
those neighboring nodes which will form its neighborhood in
the resulting topology control graph, based on the previously
exchanged neighbor order informations. It covers three main
advantages: 1) It does not assume the network graph to be
a Unit Disk Graph, 2) it works on weighted network graphs,
and 3) it does not require availability of the node position
information. Authors improved by adding node mobility and
extended XTC for mobile network [7].
An energy efficient dynamic path is maintained to send
data from source to destination for MANET is proposed in
Sheu, Tu, and Hsu [10]. Due to mobility existing paths
may not be energy efficient. So, each node in a data path
dynamically updates the path by adjusting its transmission
power. Each node in the networks determines its power for data
transmission and control packets transmission according to the
received beacon messages from its neighbors. In dynamic path
optimization technique protocols dynamically select energy
efficient path as per the requirement of dynamic topological
changes in the network [4].
Another class of topology control protocols based on the
k-nearest neighbors graph (k-NNG). In k-NNG every node is
connected to its k closest neighbors. The Local Information
No Topology protocol (LINT) [9] try to keep the number
of neighbors of a node within a low and high threshold
centered around an optimal value. But the optimal value is not
characterized. So the estimation of the number of neighbors
can be inaccurate and connectivity is not guaranteed. The
k-Neighbors (k-NEIGH) [2] protocol keeps the number of
neighbors of a node equal to, or slightly below, a given value
k. It connects each node with k-closest neighbor, instant of
require the knowledge of all the neighbors. The communication
graph that result is made symmetric by removing asymmetric
edges, which has k-upper bound neighbors. A characterization
of the critical neighbor numbers is discussed by Xue and
Kumar [13].
Another way of reducing the power consumption is by using
efficient energy path for transmitting packets. These methods
choose smaller edges in their path to reduce transmission
energy. Minimum energy consumption per packet can be
achieved by choosing the optimal energy consumed path from
source to destination. However, this technique does not take
the nodes’ energy capacity into consideration. So some nodes
may exhaust their power since energy consumption is not fair
among the nodes in the network. There for the network lifttime
decrease [11].
III. DEMAND BASED ENERGY EFFICIENT TOPOLOGY
In this section, we present a demand based energy efficient
topology (DBET) for mobile ad hoc network, which dynamically
changes the topology according to the network traffic
requirements. Initially we compute a small set of nodes, which
form a connected backbone, while the other nodes are put
off to conserve energy. These connected backbone is used for
routing the packets under low network load. When there is
a bulk data transfer between a pair of nodes, the topology
dynamically changes along the path between these nodes by
power control and route optimize technique to minimize the
power consumption.
The proposed DBET can be divided into four phases. The
first phase selects a small set of nodes that constitutes a independent
set of the network. The second phase is responsible for
electing more nodes to ensure that the selected nodes form a
connected backbone. Remaining nodes go to sleep to conserve
energy. Active node withdraw process is implement in the third
phase to remove redundant nodes in each region. To improve
the performance along the high traffic path we use the route
optimization with power control technique in the fourth phase.
In this technique, we change topology dynamically to connect
more nodes, around the routing path to minimize the total
power consumption.