Energy Efficient Buffer Management for Group Communication in MANETs

Abstract

Performance of a multicast routing protocol may be degraded due to huge packet loss caused by various factors i.e. Contention, Congestion, inefficient buffer management, dynamic topology, mobility and node density etc. Each factor has its own impact over the limited resources. In case of communication failure, various operations are repeated simultaneously that results in excessive resource consumption. Many researchers have tried to investigate the level of resource consumption and suggested various solutions according to the identified issues. In this paper, an energy efficient buffer management scheme (EEBM) is proposed for multicast group communication. PUMA and MAODV multicast routing algorithm are extended to optimize the energy consumption. Simulation results show the performance of EEBM in terms of enhanced network performance with optimal energy consumption.

Full text

Volume 8, No. 1, Jan-Feb 2017
International Journal of Advanced Research in Computer Science
RESEARCH PAPER
Available Online at www.ijarcs.info
© 2015-19, IJARCS All Rights Reserved 83
ISSN No. 0976-5697
Energy Efficient Buffer Management for Group Communication in MANETs
Dinesh Chander
Ph.D Research Scholar
MMU University, Mullana
Ambala-India
Dr. Rajneesh Kumar
Professor, Dept. of Comp. Sc. & Engg.
MMU University, Mullana
Ambala-India
Abstract: Performance of a multicast routing protocol may be degraded due to huge packet loss caused by various factors i.e. Contention,
Congestion, inefficient buffer management, dynamic topology, mobility and node density etc. Each factor has its own impact over the limited
resources. In case of communication failure, various operations are repeated simultaneously that results in excessive resource consumption.
Many researchers have tried to investigate the level of resource consumption and suggested various solutions according to the identified issues.
In this paper, an energy efficient buffer management scheme (EEBM) is proposed for multicast group communication. PUMA and MAODV
multicast routing algorithm are extended to optimize the energy consumption. Simulation results show the performance of EEBM in terms of
enhanced network performance with optimal energy consumption.
Keywords: components; Energy Optimization; Multicast; MAODV; PUMA; Group Communication; MANET.
I. INTRODUCTION
MANETs are infrastructure less, self-organizing collection of
limited battery powered mobile nodes. Node’s battery energy
is one of most important resource that should be managed
effectively to avoid the early shutdown of nodes [1]. These
low powered mobile nodes suffer from unfair utilization of
limited resources and excessive energy consumption can
reduce the life time of the node that may interrupt some
critical ongoing communication, even worse, can cause
network partitioning [2]. In order to minimize the delay, a
temporary storage space is used by nodes, called buffer and
unfair buffer management can increase the cost of group
communication in terms of packet retransmission, congestion
and frequent routing table updates. Some traditional buffer
management techniques were developed to resolve all these
issues which are given below:
1. Drop Trail: Router drops all new packets, if buffer is
full. Packet Congestion level may be increased due to
packet retransmission.
2. Fair Queue Scheduling: It can manage multiple
buffers at same time and packet switching is also
possible for shared channel.
3. Deficit Round Robin: It uses a round robin algorithm
to handle multiple packet size but it is not suitable for
real time application due to excessive delay.
4. Random Early Detection: It uses the concept of
congestion avoidance and probability to drop a packet
increases as the buffer space is occupied. It shows its
compatibility with TCP protocol but it is not suitable
for multimedia traffic.
5. Stochastic Fair Queuing: It uses hash based fair queue
allocation logic to subdivide the traffic load into
number of possible queues.
Multicast routing protocols in MANETs do not depend upon
these buffer management techniques due to its dynamic
topological behavior. In MANETs buffer overflow occurs at
mobile nodes which may increase the congestion level that
causes packet loss. Packet retransmission phase is used to
overcome from these losses but it consumes unnecessary
energy too. Therefore, an energy aware multicast routing that
must consider the topological behavior of routing protocols is
required. Tree based routing will consume the resources to
manage the multicast tree whereas mesh based protocol will
utilize it for mesh establishment and maintenance etc. So
there is need to develop a scheme which will optimize the
energy consumption for tree and mesh based routing
protocols. In Table I we have shown different modes and
operations in which node’s energy is consumed [3, 4, 5]:
Table I: Energy consumption
Mode Description
Reception Energy required to receive a packet
Transmission Energy required to transmit a packet
Sleep Energy required to retain sleep mode
Transition Energy required to switch the State from sleep
mode to idle mode
The organization of paper is the following: the section II
gives brief overviews about proposals to achieve energy
efficient buffer management for MANETs; section III
illustrates about the proposed scheme; section IV presents the
simulation result and analysis; section V discusses the
conclusion and future scope.
II. RELATED WORK
MANETs are becoming most growing fields due to its unique
applications in the field of battle field, military networks,
disaster management and rescue operations. One of most
important constraint in MANETs is limited battery life of
mobile nodes. These nodes consume lot of energy in
communication and many times communication gets failed
due to dead nodes in between. Therefore, during the past
several years, many researchers worked to achieve the energy
efficient communication in MANETs.
G. A. Walikar et al. [3] investigated that due to large scale
resource consumption, intermediate nodes can be exhausted

Dinesh Chander et al, International Journal of Advanced Research in Computer Science, 8 (1), Jan-Feb 2017,83-88
© 2015-19, IJARCS All Rights Reserved 84
and may cause link breaks and can interrupt the end to end
communication. To ensure the end point connectivity, authors
introduced an energy aware multicast routing solution, called
EAMRP. It builds the multiple shortest routes by estimating
the residual energy of each intermediate node. Alternate
routes can be discovered to overcome from link breaks.
Simulation results show that it can manage the resources at
node level to ensure the reliable group communication, with
minimum delay and higher PD/Throughput.
X. Shen et al. [6] considered the energy consumption for
group communication over wireless networks and proposed a
method which can preserve the energy by grouping the
multiple sessions. Experimental results show that it can
improve the Throughput and also optimize the energy.
Q. Zhao et al. [7] investigated the issues related to efficient
resource utilization for multi-view video transmission over
wireless networks and developed a scheme, called multicast
fractional frequency reuse, to optimize the energy
consumption as well as to manage the bandwidth. It can
allocate the resources under the QoS constraints. Results
show that its performance in terms of energy efficiency and
bandwidth conservation for video transmission.
K.C. Rajani et al. [8] explored the performance issues of
multicast routing protocols over MANETs. These are: Tree
construction, maintenance, group Join/Leave and impact of
mobility over wireless links etc. Study shows behavior of
each routing protocol changes according to the variations in
each parameter and it also opens the new horizons for
researchers.
D. Jiang et al. [9] investigated the traditional ways of energy
conservation and developed a new scheme to optimize the
energy consumption for multicasting. In order to achieve
energy efficiency, it defines four different modes for each
node i.e. SLEEP, WALK, SEND and RECEIVE etc.
Probability of energy consumption is estimated in each
individual mode and simulation results validate it and this
data is used to switch the mode, as per the requirement.
Z.T. Chou et al. [10] investigated the issues related multicast
scalable video transmission over wireless networks and
proposed a scheduling scheme for layers. It regulates
admission control and greedy approach is used for scheduling
the packets at base layer and enhanced data layer. Simulation
results show its performance in terms of optimal energy
consumption and stable Throughput under the constraints of
network dynamics and uniform/non-uniform traffic
conditions.
F. Chiti et al. [11] developed a method for reliable and
efficient network coding scheme for multiple group
communication. Rate adapted network coding does not use
feedback channels for reliable content delivery, thus enhances
the overall efficiency of network as well as it also preserve
the network resources.
D. N. Minh Dang et al. [12] enhanced the network efficiency
by combing the multichannel MAC and power saving
scheme. Control information is exchanged using directional
antennas and channel is selected using window and beam
directions. Simulation results shows that proposed scheme
can enhance the Throughput, PDR, energy consumption and
fairness distribution etc.
Z. Zhu et al. [13] proposed method to secure the multicast
channels over wireless networks. It uses a beam formation
design to minimize the energy consumption for transmission
and at for receiver side, energy harvesting constraint is used.
Simulation results show that it can manage the network
performance by adopting the channel uncertainty.
P. Sujitha et al. [14] addressed the power consumption at
routing level and proposed a solution to conserve the energy
at node level by minimizing the number of Hops. In case of
group communication, data is forwarded using intermediate
nodes, from sender to destination. Forwarding nodes are
selected on the basis of a calculated optimized angle (600) and
other nodes are ignored, in order to preserve the energy. Ns-2
was used for simulation purpose and results show its
performance in terms of enhanced Throughput, PDR and
energy optimization etc.
K. Subramaniam et al. [15] investigated the issues related to
real time data streaming over multicast ad hoc networks.
Study shows that inefficient buffer management introduces
delay and Jitter and cause quality degradation also. Authors
resolved this issue by caching the frequently required data at
multicast tree which is created for group communication
purpose. Highest priority for real time data is set and buffer
level is adjusted dynamically as per requirements. Simulation
results show that its performance in terms of less control
overhead, latency, energy consumption and higher PDR etc.
P. Jain et al. [16] proposed a RED queue based buffer
management scheme for ad hoc routing. A buffer space is
maintained between sender and receiver. During packet
transmission, packets are stored in a temporary buffer
managed by each intermediate node. Each coming packet is
marked with a probability that it must survive till a Threshold
value which may vary according to the current queue size.
Simulation results show that packet drop can also occur due
to other reasons also i.e. Contention/Congestion etc. Results
show that it performs well as compared to DROP Trail and
PAQMAN scheme.
C. Yang et al. [17] explored the factors which can consume
the energy over heterogeneous networks and these are: Traffic
Load, Antenna‘s Ports, Cooling system and power supplies
etc. and developed an energy efficient model for multicast
services to be deployed over heterogeneous environment.
Energy consumption is optimized by estimating the ratio of
number of current users and the total available resources.
After that load is distributed using multicast channels, thus
reduces the total energy requirements. Simulation results
validate this model under the constraints of dense network.
S.L. Wu et al. [18] explored the resource consumption issues
related to wireless communication and proposed a solution to
optimize the resources for low powered mobile devices.
Proposed method allocates the limited radio resources in such
a way that maximum Throughput can be ensured. After this,
rescheduling of video layers is performed as per the ratio of
allocated resources to satisfy the energy constraints.
Simulation of multicast group communication was performed
using LTE wireless networks and results show its
performance in terms of efficient resource allocation, optimal
energy consumption and enhanced network Throughput etc. It
can be further extended by introducing multiple resource
allocation and scheduling algorithms, in order to achieve
higher degree of energy conservation.
S.Othmen et al. [19] tried to enhance the network life time
and delivered the data by selecting energy constrained
multipath routes. To minimize the delay, Routes with higher
congestion level and less residual energy are avoided.
Simulation results show its performance in terms energy
efficiency, traffic handling, QoS and minimum End to End

Dinesh Chander et al, International Journal of Advanced Research in Computer Science, 8 (1), Jan-Feb 2017,83-88
© 2015-19, IJARCS All Rights Reserved 85
Delay etc. Proposed scheme can be extended to support the
VoIP communication.
H. Al-Mahdi et al. [20] developed a solution to manage the
buffer using dynamic Hops for ad hoc networks. Buffer is
subdivided into partitions and its size varies according to the
number of current Hops. Threshold constraint is used for size
and Hops. Simulation results show that it can reduce the
packet loss as well as it offers minimum end to end delay.
X. Luo et al. [21] developed an analytical framework based
equation to manage the buffer for MANETs. Each node uses
a Threshold value for its current buffer that is used to define
the limit for number of packets, to be hold for processing.
Analytical model show that it can manage the buffer’s
occupancy ratio for each node. Simulation validates the
developed logic under the constraints of buffer occupancy
over execution time
III. PROPOSED SCHEME FOR ENERGY EFFICIENT
BUFFER MANAGEMENT
In case of group communication, a sender can share the data
with multiple receivers. Data transmission rate and data
processing rate of sender and receiver may vary for sender
and receiver. If data transmission rate is higher and its
processing rate is low, it may lead to huge packet drop at
receiver end due to buffer overflow. One possible solution is
to retransmit data packets but it consumes significant amount
of resources and may increase congestion level. Node’s
energy consumption can be optimized by regulating the data
transmission or reception rates and buffer overflow can be
avoided. EEBM sets the following constraints for buffer
management:
1. Buffer capacity is delimited by defining minimum and
maximum range for packet processing.
2. A Threshold value is used for buffer's size management
3. A data processing rate is defined at receiver's end in such a
way that it should consume all packets to avoid the packet
loss and meanwhile sender must wait for current packet
processing and should hold the next transmission.
4. A Time Out interval is used for transmission and reception,
to avoid the congestion and packet loss.
At initial stage, EEBM waits for enough packet loss, so that it
can manage the buffer space. When packet loss Threshold is
reached, EEBM increases the transmission interval of packets
at sender side, so that recently transmitted packets at
receiver’s side can be processed.
If waiting time exceeds than the allowed Threshold, packet
transmission is rescheduled, at sender side. If packets to be
rescheduled are more than the allowed limit, some of these
are dropped to maintain the buffer level. EEBM repeat all
these steps, in order to regulate the buffer space and results
show that it can reduce the extra control overhead as well, as
it can optimize the energy consumption by avoiding the
unnecessary packet retransmission. Pseudo code for the
proposed scheme is as follows:
Initialization phase:
Proc_init()
{
Initialize nodes :
n
Set initial energy :
ex
Set routing protocol :
rp
Set initial packet transmission:
Tx
Set initial packet reception:
Rx
Set initial packet transmission power:
Txp
Set initial packet reception power:
Rxp
Initialize total_buffer size
buffer size_MAX :
mxB_s
buffer size_MIN:
MnB _s
buffer size_Threshold:
ThB_s
Packet Drop_Threshold:
ThPKT >−
Packet Loss:
Pl
MAX. rate of possible packet transmission :
Mr
//Limit to process No. of packet in current buffer
Packet_Size_MAX_allowed
Packet_Size_MIN_allowed
//Sender Side:
Set
Tx
ervalTxPKT int__>−
;
Set
TimeOutTxPKTTx __>−
//Receiver Side:
Set
;int__ ervalRxPKTRx >−
Set
;_TimeWait
Set
;__ TimeoutRxPKTRx >−
}
// Group initialization Phase:
If (
exn>−
)
{
Group_Join(
IDni >−
, True);
}
If (Group_Leave(
IDni >−
)
{
>−ni
Membership=false;
};
//packet loss count
If (
Rx
->Error)
{
Pl
++;
}
If (
Pl
<
ThPl >−
)
{ Process_PKT (
pTx >−
);
}
Else
{ Buffer_mgmt(regulate_state)
}

Dinesh Chander et al, International Journal of Advanced Research in Computer Science, 8 (1), Jan-Feb 2017,83-88
© 2015-19, IJARCS All Rights Reserved 86
Proc Buffer_mgmt(regulate_state)
{ If (regulate_state)
{
Set
SizePKT _
_ allowed
If
(
ervalTxPKT int__
!=
TimeoutTxPKT __
&&
ervalRxPKT int__
!=
TimeOutTxPKT __
)
{
If
>−Rx
pkt.Size() >
SizePKT _
_allowed)
{
Drop(
pTx >−
);
}
If (
npTx >−
<=
ThBs _
)
Process_PKT(
pTx >−
);
else
Tx
->Wait_Time++;
If (
>−Tx
Wait_Time >
ThBs _
)
{
reschedule(
pTx >−
);
}
} else {
Terminate (
Tx
,
Rx
);
}
}
}
Proc reschedule(
pTx >−
)
{ If(count(
pTx >−
>
mxBs _
)
{
Drop(
pTx >−
, buffer);
}
If (!
TimeOutTxPKTTx __>−
&& !
TimeOutRxPKTTx __>−
)
{
For each
p
in
Tx
>−Tx
TimeOutTxPKTp __.
=
1_ +TIMECURRENT
;
}
}
IV. SIMULATION RESULTS AND PERFORMANCE
ANALYSIS
NS-2.35 is used for perormance analysis of MAODV and
PUMA multicast routing protocols with EEBM scheme.
Table II given below shows the simulation configuration.
Table II: Simulation configuration
Simulation Parameters
Node(s) 30
Sender 1
MAC Protocol 802.11
Terrain 1200x1200
Ad Hoc Multicast Routing
Protocol(s)
MAODV, PUMA
Simulation Time 10 Seconds
Group Size 1
Propagation Model TwoRayGround
Simulator NS-2
Node’s Speed 120ms
Queue Type DropTrail/Priority Queue
Initial Energy 10.0j
Traffic Type CBR
Packet Size 512 Bytes
IFQ Length 50
Simulation Scenario(s)
1. Normal Execution Environment:
MAODV-N
PUMA-N
2. Using EEBM:
MAODV-EEBM
PUMA-EEBM
Following graphs show the performance of normal MAODV
and PUMA with EEBM. We have studied the throughput,
Packet delivery ratio, routing load and energy consumption
parameters to show the superiority of EEBM [22].
A. Throughput
Throughput is the measure of strength of any wireless
communication system. It can be defined as the ratio of
number of data packets received successfully over the total
simulation time. Fig. 1 shows the throughput analysis of
MAODV and PUMA with EEBM and without it. It can be
observed that EEBM enhanced the overall throughput of
MAODV and PUMA.
Figure 1. Throughput analysis
B. Packet Delivery Ratio
Packet Delivery Ratio (PDR) is defined as the ratio of total
data packets received by the receiver node to the total number
of data packets transmitted by the sender node.
Mathematically, it can be calculated with the following
formula,

Dinesh Chander et al, International Journal of Advanced Research in Computer Science, 8 (1), Jan-Feb 2017,83-88
© 2015-19, IJARCS All Rights Reserved 87
PDR=
Where represents total number of data packets received
by the receiver node and represents total number of
packets sent by the sender node. Fig. 2 shows the PDR
analysis of our proposed scheme with the normal scenario of
MAODV and PUMA. This figure shows the variations in
PDR using different scenarios. It can be observed that EEBM
improved the PDR of MAODV and PUMA.
Figure 2. Packet Delivery Ratio analysis
C. Routing Load
Routing load can be calculated with the help of following
formula,
RO=
Where represents the number of control packets and
represents number of data packets. Fig. 3 shows the variations
in Routing Load using diffeent scenarios. It can be observed
from the figure that EEBM has reduced the routing overload
for MAODV and PUMA.
Figure 3. Routing Load analysis
D. Energy Consumption
As stated earlier, nodes in MANETs have limited battery life,
and if used efficiently may cause the communication failure
or sometime network partitioning. Form Fig. 4, it is clear that
our proposed scheme EEBM has consumed less energy by
managing the node’s buffer efficiently.
Figure 4. Energy Consumption analysis
V. CONCLUSION AND FUTURE SCOPE
• In this paper, various issues related to resource
consumption and buffer management were discussed
and an energy efficient buffer management method
was introduced which can manage the buffer during
packet transmission for group communication.
MAODV and PUMA, multicast routing protocols
are used for simulation purpose and results show that
EEBM enhanced the Throughput and PDR for each
protocol while minimizing the routing load and total
energy consumption.
• It can be observed that even without using EEBM,
Throughput/PDR of MAODV higher than PUMA
and EEBM enhanced these for both protocols.
Performance of PUMA suffered from excessive
routing load but EEBM reduces it and enhanced the
performance of PUMA and it is less than then
MAODV. EEBM also reduced the level of energy
consumption for PUMA and MAODV. PUMA
consumed less energy as compared to MAODV.
In future, EEBM can be extended to minimize the energy
consumption for other multicast routing protocols.
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