Int. J. Communications, Network and System Sciences, 2009, 7, 627-635
doi:10.4236/ijcns.2009.27070 Published Online October 2009 (http://www.SciRP.org/journal/ijcns/).
Load Balanced Routing Mechanisms for Mobile Ad Hoc
Networks
Amita RANI1, Mayank DAVE2
1Department of Computer Science & Engi neering, University Institute of Engi neering & Technology, Kurukshetra, India
2Department of Computer Engineering , National Institute of Technology, Kurukshetra, India
Received May 11, 2009; revised July 11, 2009; accepted August 24, 2009
ABSTRACT
Properties of mobile ad hoc networks (MANET) like dynamic topology and decentralized connectivity make
routing a challenging task. Moreover, overloaded nodes may deplete their energy in forwarding others pack-
ets resulting in unstable network and performance degradation. In this paper we propose load-balancing
schemes that distribute the traffic on the basis of three important metrics – residual battery capacity, average
interface queue length and hop count along with the associated weight values. It helps to achieve load bal-
ancing and to extend the entire network lifetime. Simulation results show that the proposed load-balancing
schemes significantly enhance the network performance and outperform one of the most prominent ad hoc
routing protocols AODV and previously proposed load balanced ad hoc routing protocols including DLAR
and LARA in terms of average delay, packet delivery fraction and jitter.
Keywords: Load Balanced Routing, Residual Battery Capacity, Hop Count, DLAR, LARA
1. Introduction
The proliferation of devices that do not depend upon
centralized or organized connectivity has led to the de-
velopment of mobile ad hoc networks (MANETs). These
are the infrastructure-less networks where each node is
mobile and independent of each other. Due to unorgan-
ized connectivity and dynamic topology, routing in
MANET becomes a challenging task. Moreover, con-
straints like lower capacity of wireless links, error-prone
wireless channels, limited battery capacity of each mo-
bile node etc., degrade the performance of MANET
routing protocols. Heavily-loaded nodes may cause con-
gestion and large delays or even deplete their energy
quickly. Therefore, routing protocols that can evenly
distribute the traffic among mobile nodes and hence can
improve the performance of MANETs are needed.
Routing protocols in MANETs are classified into three
categories: proactive, reactive and hybrid routing proto-
cols. Most of the prominent routing protocols like
AODV [1], DSR [2] use hop count as the route selection
metric. But it may not be the most efficient route when
there is congestion or bottleneck in the network. It may
lead to undesirable effects such as longer delays, lower
packet delivery fraction and high routing overhead. Also
some nodes that may lie on multiple routes spend most
of their energy in forwarding of packets and deplete their
energy quickly. Consequently they leave the network
early. In this paper we present novel load-balancing
mechanisms/schemes for MANETs that focus on distrib-
uting the traffic on the basis of combination of following
three metrics:
 hop count
 residual battery capacity and
 average number of packets queued up in the in-
terface queue of a node lying on the path from
source to destination/traffic queue.
These three metrics along with associated weight values
decide the path to be selected for data transmission. The
results of simulations indicate that the proposed schemes
outperform a prominent ad hoc routing protocol AODV
and previously proposed load balanced ad hoc routing
protocols including DLAR [3] and LARA [4] in terms of
average delay, packet delivery fraction and jitter.
The rest of this paper is organized as follows. Section
2 discusses the work related to currently proposed load
balanced ad hoc routing protocols. Section 3 details the
proposed schemes in order to balance the load on various
routes. Section 4 describes the methodology, perform-
ance metrics used and simulation results. Finally Section
5 concludes the paper.

A. SHABAN ET AL. 628
2. Related Work
Load balanced routing aims to move traffic from the ar-
eas that are above the optimal load to less loaded areas,
so that the entire network achieves better performance. If
the traffic is not distributed evenly, then some areas in a
network are under heavy load while some are lightly
loaded or idle. There are various proposed algorithms for
load balanced routing. In Dynamic Load Aware Routing
(DLAR) protocol [3] routing load of a route has been
considered as the primary route selection metric. The
load of a route is defined as the summation of the load of
nodes on the route, and the load of a node is defined as
the number of packets buffered in the queue of the node.
To utilize the most up-to-date load information when
selecting routes and to minimize the overlapped routes,
which cause congested bottlenecks, DLAR prohibits in-
termediate nodes from replying to route request mes-
sages.
Another network protocol for efficient data transmis-
sion in mobile ad hoc networks is Load Aware Routing
in Ad hoc (LARA) [4] networks protocol. In LARA,
during the route discovery procedure, the destination
node selects the route taking into account both the num-
ber of hops and traffic cost of the route. The traffic cost
of a route is defined as the sum of the traffic queues of
each of the nodes and its neighbors and the hop costs on
that particular route. Thus, the delay suffered by a packet
at a node is dependent not only on its own interface
queue but also on the density of nodes. In routing with
load balancing scheme (LBAR) [5], the destination col-
lects as much information as possible to choose the op-
timal route in terms of minimum nodal activity (i.e the
number of active routes passing by the node). By gather-
ing the nodes activity degrees for a given route the total
route activity degree is found. Load Sensitive Routing
(LSR) protocol [6] is based on DSR. In LSR the load
information depends on two parameters: total path load
and the standard deviation of the total path load. Since
destination node doe not wait for all possible routes, the
source node can quickly obtain the route information and
it quickly responds to calls for connections. Correlated
Load-Aware Routing (CLAR) [7] protocol is an on-de-
mand routing protocol. In CLAR, traffic load at a node is
considered as the primary route selection metric and de-
pends on the traffic passing through this node as well as
the number of sharing nodes. Alternate Path Routing
(APR) protocol [8] provides load balancing by distribut-
ing traffic among a set of diverse paths. By using the set
of diverse paths, it also provides route failure protection.
Reference [9] gives a comparative study of some of the
load balanced ad hoc routing protocol.
All The protocols discussed above concentrate on traf-
fic balancing and do not emphasize on energy issues. A
number of routing protocols that consider energy issues
in MANETs have been proposed. On the basis of route
selection criterion, there are mainly two categories of the
energy efficient routing protocols. The first class [10–12]
selects the path that consumes the least energy to trans-
mit a single packet from source to destination, aiming at
minimizing the total energy consumption along the path.
The second one [13–15] intends to protect the overused
nodes against breakdown, aiming at maximizing the
whole network lifetime.
3. Proposed Schemes to Achieve Load
Balancing
A number of routing protocols proposed for MANETs
use shortest route in terms of hop count for data trans-
mission. It may lead to quick depletion of resources of
nodes falling on the shortest route. It may also result in
network congestion resulting in poor performance.
Therefore, instead of hop count a new routing metric is
required that can consider the node’s current traffic and
battery status while selecting the route. The idea is to
select a routing path that consists of nodes with higher
residual battery power and hence longer life.
We define the required parameters, as follows: The
terms used in this paper have been defined as follows:
1) Route Energy (RE): The route energy of a path is
the minimum of residual energy of nodes (rei) falling on
a route. Higher the route energy, lesser is the probability
of route failure due to exhausted nodes.
2) Traffic queue (tq): The traffic queue of a node is the
number of packets queued up in the node’s interface.
Higher is its value, more occupied the node is.
3) Average Traffic Queue (ATQ): It is the mean of
traffic queue of nodes from the source node to the desti-
nation node. It indicates load on a route and helps in de-
termining the heavily loaded route.
4) Hop count (HC): The HC is the number of hops for
a feasible path.
3.1. Scheme 1
The first scheme proposed in this paper tends to deter-
mine the routes in such a way that the routes consisting
of nodes with lower residual battery capacity are avoided
for data transmission even if they are short and less con-
gested. This scheme tries to make a fair compromise
between three route selection parameters i.e. hop count,
residual battery capacity and traffic load.
A MANET can be represented as an undirected graph
G(V, E) where V is the set of nodes (vertices) and E is
the set of links (edges) connecting the nodes. The nodes
may die because of depleted energy source and the links
can be broken at any time owing to the mobility of the
nodes.n|nєV, n has an associated traffic queue tq(n)
and residual battery energy rei. A path between two

A. RANI ET AL. 629
nodes u and v is given as
P(u, v) = (u, e(u, x), x, e(x, y), y, ......., e(z, v), v)
It can be emphasized that a path between any two
nodes is a set consisting of all possible paths between
them. Formally, P(u, v) = {P0 , P1 , ...., Pn} where each Pi
is a candidate path between u and v.
Let HC(Pi ) be the hop count corresponding to path Pi
between u and v. Weight of path Pi defined as:
W(Pi)= W1 * RE(Pi) - W2 * ATQ(Pi) - W3 * HC(Pi ) (1)
where RE( Pi) = min {ren1, ren2, ..., renm} and n1, n2,...,
nm are the nodes making up the path.
ATQ(Pi ) =(tq(n1)+tq(n2)+ ...+tq(nm))/m-1 (2)
The fields having adverse contribution to traffic dis-
tribution are built into negative coefficients in Equation
(1). Also the weight values are calculated such that W1 +
W2 + W3 = 1.
The idea is to find a path from source to destination
with maximum weight such that from the very beginning
the path determined is energy efficient and there is a fair
compromise between a short route and a light-loaded
route. In this scheme RE has been given maximum
weightage, i.e. W1 is maximum and W2 and W3 are equal.
We call this path Energy Aware Load-balanced Path
(EALP).
Supposing that i є {0,1,2,…,n}, P(s,d) = {P0, P1,…, Pn}
for given source s and destination d, we can define the
problem mathematically as:
EALP(s,d) = Pi with
W(Pi) = max {W(P1), W(P2),…, W(Pn)} (3)
W1, W2 and W3 are constants.
In proposed scheme routes are determined on demand.
A source node initiates the route discovery process by
broadcasting a route request (RREQ) packet whenever it
wants to communicate with another node for which it has
no routing information in its table. On receiving a RREQ
packet, a node checks its routing table for a route to the
destination node. If the routing table contains the latest
route to the destination node, the intermediate node sends
a RREP packet along the reverse path back to the source
node also appending the weight value for the route.
When a source node receives more than one RREP
packet for a RREQ, it compares the weight values of the
routes and selects the route with maximum weight.
However, if an intermediate node has no information of
the destination node, it adds its own traffic queue value,
compares and finds the minimum of residual battery ca-
pacity field of RREQ packet with its own residual battery
capacity and updates residual battery capacity field of
RREQ packet, increments the hop count by one and re-
broadcasts the route discovery packet. When destination
node receives a route request packet, it waits for a certain
amount of time before replying with a RREP packet in
order to receive other RREQ packets. Then destination
node computes ATQ and the weight value for each fea-
sible path using Equation (2) and using weight function
as given in Equation (1) respectively. The route with
highest weight value is selected as the routing path and a
RREP packet is sent back towards the source node on the
selected path.
In the algorithm discussed above weight values are
constant, which is its limitation as when route selection
procedure starts there are more chances of network con-
gestion because of flooding of many RREQ packets si-
multaneously. Moreover, nodes have maximum battery
energy during initial phases. Therefore, the requirement
is to change the above algorithm such that when the bat-
tery energy of nodes is high, emphasis is on selecting a
short and light loaded route. As battery energy of nodes
decreases we tend to conserve energy, compromising on
short and lightly loaded route.
3.2. Scheme 2
Another scheme has been proposed in this paper in
which weight values (W1 , W2 and W3 ) are adaptive to
the network status, instead of being constant. More
weight age is given to find short and less congested
routes during initial route discovery procedure, as the
possibility of network congestion is high due to flooding
of many RREQ packets simultaneously. Also, nodes
have maximum battery energy during initial phases.
However, as the time elapses battery energy of nodes
decreases, therefore, we tend to conserve energy, com-
promising on short and lightly loaded routes. The adap-
tive behavior of the protocol has been implemented by
computing the proportion of route energy and initial en-
ergy of nodes assuming that all nodes are similar with
equal initial battery energy. Therefore, as per Scheme 2,
weight value of a route is computed as:
W(Pi) = (1-α) * RE(Pi) –α/2*(ATQ(Pi) + HC(Pi )), (4)
where,
α =min(RE(Pi))/ IE;0≤ α≤1 (5)
and gives the proportion of battery capacity left. Initially
when nodes have high residual battery energy α is
maximum, route selection is mainly done on the basis of
hop count and average traffic load as can be seen from
Equation (4). As nodes battery energy decreases with the
passage of time α decreases and 1- α increases leading to
more weightage to the route energy parameter.
3.3. Scheme 3
The scheme proposed next uses location information to
limit the broadcast of RREQ packets. When an interme-
diate node receives a RREQ packet it uses the location
information before broadcasting the RREQ packets fur-
ther. Only the nodes that are closer to the destination
than the source node are allowed to broadcast RREQ

A. SHABAN ET AL. 630
packets further. By doing so a broadcast storm can be
avoided resulting in less congested routes. Flowchart
given in Figure 3 gives the details of this algorithm.
A source node while starting a route discovery process,
computes its distance w.r.t. the destination node, appends
this value in the RREQ packet along with the fields as
used in Scheme 2 and broadcasts it further. An interme-
diate node on receiving a RREQ packet, compares its
distance to the destination node with the distance value
stored in the RREQ packet. If its distance is longer, it
drops the RREQ packet else it compares the energy value
in the record of the RREQ packet with its own energy
and assigns the lesser energy as the new energy value in
the packet. It also adds its own traffic queue to the traffic
queue already recorded in the packet and updates hop
count by 1. It then broadcasts the packet further. By do-
ing so only those nodes that are closer to the destination
node than the source node participate in route selection
procedure resulting in reduced routing overhead. This
procedure has been explained with the help of Figures 1
and 2.
3.4. Example
As shown in Figure 1, we assume that there are three
feasible paths from source node S and destination node D
- Path I: (S,A,E,H,J,D), Path II: (S,B,F,K,D), Path III:
(S,C,G,I,L,M).
Corresponding to Figure 1, the nodes on Path I (S,A,
Figure 1. Route energy and average traffic queue of each
feasible path for high residual battery capacity of node s.
Figure 2. Route energy and average traffic queue of each
feasible path for low residual battery capaci ty of nodes.
E,H,J,D), energies of intermediate nodes between source
and destination are (450, 400, 433, 413); thus RE1 =
min(450, 400, 433, 413) = 400. Similarly, for Path II
RE2=410 and for Path III RE3 = 420.
The traffic queue length of all the intermediate nodes
between source and the destination as shown in Figure 1,
for Path I (S,A,E,H,J,D), ATQ1 = 25, HC1 = 5. For Path
II (S,B,F,K,D), ATQ2 = 36, HC2 = 4 and for Path III
(S,C,G,I,L,M), ATQ3 = 44, HC3 = 6.
The destination node on receiving a RREQ packet
waits for certain amount of time before replying with a
RREP packet in order to receive more RREQ packets.
According to first scheme the weight values are constant.
After performing many simulations, we have determined
that we get most favorable results for W1=0.6, W2=W3=
0.2. On substituting these weight values and parameters
as described above in Equation (1) we get W3>W2>W1.
Hence, Path III is the most suitable route and hence is
selected for data transmission.
For the other two schemes we compute the value of α
as per Equation (5). On substituting α in Equation (1), we
get W1>W2>W3 i.e. initially when the nodes have high
residual battery capacity, more weightage is given to the
short and lightly loaded route while route selection.
However, a trade-off between hop count and ATQ is still
maintained in order to avoid congested routes. As the
battery energy of nodes diminishes, more emphasis is
given on selecting the routes with high residual battery
power. Although the routes selected may be longer. In
this situation, Scheme 1 still results in W3>W2>W1, how-
ever, for Schemes 2 and 3, the weight values have
changed from W1>W2>W3 for high residual battery ca-
pacity to W3>W2>W1 for low energy nodes. This com-
parison has been illustrated in Table 1 and Table 2.
Table 1. Comparison of schemes for high vales of route
energy.
Route weight
Path RE
(IE=500) ATQHC
Scheme 1 Scheme 2 & 3
P1 400 25 5
P2 410 36 4
P3 420 44 6
W3>W2>W1 W1> W2>W3
Table 2. Comparison of schemes for low values of route
energy.
Route weight
Path RE
(IE=500) ATQHC
Scheme 1 Scheme 2 & 3
P1 200 20 5
P2 210 31 4
P3 220 49 6
W3>W2>W1 W3>W2>W1

A. RANI ET AL. 631
Figure 3. Flowchart depicting proposed algorithm.
4. Performance Evaluation
In this section we describe our simulation environment
and performance metrics.
4.1. Performance Metrics
We have used ns-2 simulator version 2.29 to analyze the
proposed algorithms. Our solution has been compared
against AODV and two of the previously proposed load
balanced ad hoc routing protocols - DLAR and LARA.
We use the following performance metric to evaluate the
performance of each scheduling algorithm:
 Packet Delivery Fraction: It gives the ratio of the
data packets delivered to the destination to those
generated by the sources, which reflects the degree
of reliability of the routing protocol.
 Normalized Routing Load: The number of routing
control packets per data packet delivered at the
destination.
 Average End-to-End Delay: This is the average
overall delay for a packet to traverse from a

A. SHABAN ET AL. 632
source node to a destination node. This includes
the route discovery time, the queuing delay at a
node, the transmission delay at the MAC layer,
and the propagation and transfer time in the wire-
less channel. As delay primarily depends on op-
timality of path chosen, therefore, this is a good
metric for comparing the efficiency of underlying
routing algorithms.
 Jitter: Jitter is defined as the delay variation be-
tween each received data packets. It gives an idea
about stability of the routing protocol.
 Average Residual Battery Capacity: This metric
depicts the amount of energy consumption of
nodes with respect to time period.
4.2. Simulation Environment
Our simulation scenario consists of 50 nodes moving at
maximum velocity of 20m/s in a 600m x 600m grid area
with a transmission range of 100m with 25 and 37 TCP
flows. Each source node transmits packets at a rate of
four packets per second, with a packet size of 1024 bytes.
We run simulation for pause times of 0, 100, 200, 300,
400, 500, 600, 700 and 900 seconds. The mobility of a
node is defined by random waypoint model. This model
forces nodes to move around with two predefined pa-
rameters, maximum velocity and pause time. Each node
moves to a random destination at random velocity. They
stay there for predefined time and then move to a new
destination. Also it is the most widely used mobility
model in previous studies. The size of the interface
buffer of each node for simulation is taken as 50 packets.
Each experiment is conducted four times and the average
result has been considered.
4.3. Simulation Results
4.3.1. Pa cket Deliver y Fractio n
Figure 4 and Figure 5 show the packet delivery fraction
of each protocol for 50 nodes with 25 and 37 sources
respectively. The proposed schemes perform very well
irrespective of the node’s pause time and outperform
AODV, DLAR and LARA. In high mobility scenarios,
many route construction processes are invoked. When a
source floods a RREQ packet to recover the broken route,
many intermediate routes reply with the routes cached by
overhearing packets during the initial route construction
phase. A number of these cached routes overlap existing
routes. Nodes that are part of multiple routes become
congested and can not deliver the packets further result-
ing in poor performance of AODV. Although DLAR and
LBAR also achieve a better performance than AODV, the
effectiveness of load balancing is not salient compared
Figure 4. Packet delivery fraction vs. pause time for 25
sources.
Figure 5. Packet delivery fraction vs. pause time for 37
sources.
with our schemes. The performance of proposed schemes
is almost similar. However, the reason for lower packet
delivery fraction at some points for third scheme is in-
ability of the network to find out a route to the destina-
tion because of restricted number of RREQ packets. The
results also show that the packet delivery fraction re-
duces with increase in load in the network.
4.3.2. Normalized Routing Load
Figure 6 and Figure 7 show normalized routing load of
each protocol for 50 nodes with 25 and 37 sources re-
spectively. Horizontal axis of the figures represent the
pause times. As expected, normalized routing load for
first two proposed schemes is comparatively higher than
AODV protocol. However, in the third proposed algorithm

A. RANI ET AL. 633
Figure 6. Normalized routing load vs. pause time for 25
sources
Figure 7. Normalized routing load vs. pause time for 37
sources.
we try to restrict the broadcast of RREQ packets, which
results in lower routing load than the routing load of
AODV, DLAR and LARA protocols. It has also been
observed from Figure 6 and Figure 7 that normalized
routing load increases with increase in number of sources
in the network.
4.3.3. A ve rage End-to - E n d Delay
Figure 8 and Figure 9 plot the average end-to-end delay
for variations of node’s pause time for 50 nodes with 25
and 37 sources respectively. Proposed algorithms have
much improved average end-to-end delay than AODV
and other two load balanced routing protocols i.e. DLAR
and LARA. We can see that the end-to-end delay in-
creases for all the protocols with increase in load as can be
seen in Figures 8 and 9. The reason is the increased con-
tention at MAC level due to increase in load. The packets
now have to wait longer in the interface queue before be-
ing transmitted. Here, AODV suffers maximum delay as it
often routes the packets around heavily loaded nodes.
DLAR and LARA make better choice of routes than
AODV. The proposed algorithms make best decision
among all these protocols. The results are more noteworthy
because even for highly dynamic topology (i.e. pause
time = 0) and static topology (i.e. pause time = 900),
proposed algorithms achieve significantly lower delay
than rest three protocols. This is due to the effective
routing strategy adopted for load balancing and their try
to route packets along a less congested route to avoid
overloading of some nodes.
4.3.4. Ji tter
Figure 10 and Figure 11 show delay variation of received
packets (jitter) versus pause time for 50 nodes with 25
and 37 sources respectively. It can be seen that jitter is
considerably lower for proposed algorithms than AODV
DLAR and LARA protocols, even for highly dynamic
Figure 8. Average end-to-end delay vs. pause time for 25
sources.
Figure 9. Average end-to-end delay vs. pause time for 37
sources.

A. SHABAN ET AL. 634
topology (i.e. pause time = 0) and nearly static topology
(i.e. pause time = 900) as well. This behavior is as an-
ticipated because delays mainly occur in queuing and
medium access control processing. These delays are re-
duced in proposed schemes by routing the packets to-
wards nodes that are less occupied also taking into ac-
count more efficient nodes in terms of energy.
4.3.5. Average Residual Battery Capacity
Figure 12 compares the average residual battery capacity
of nodes for AODV and the proposed schemes w.r.t.
simulation time. It is evident from the figure that the rate
of energy consumption is much higher for AODV than
the proposed protocols. The reason is the energy aware
load balancing behavior of proposed schemes. Initially
when battery energy of nodes is high, energy consump-
tion rate for the first proposed scheme is the least. This is
due its behavior of energy considerations while balanc-
ing the load, even if the node energy is high. The per-
formance of other two protocols improves with the reduc-
Figure 10. Jitter vs. pause time for 25 sources.
Figure 11. Jitter vs. pause time for 37 sources.
Figure 12. Average residual battery capacity of nodes.
tion in battery energy, because as the battery capacity of
nodes decreases, routes with higher residual battery ca-
pacity are considered irrespective of its length and load.
As can be inferred from the Figure 12, a MANET em-
ploying third proposed strategy for routing has maximum
residual battery capacity. It is due to restricting the
broadcast of packets. As a result of which a proportion of
energy spent by nodes in forwarding RREQ packets re-
mains conserved.
5. Conclusions
In this paper, we presented some schemes for load bal-
ancing in mobile ad hoc networks. The proposed
schemes are based on a new metric based on weighted
combination of three parameters. The three parameters
responsible for final route selection are - the average
traffic queue, the route energy, and the hop count. And,
the weights corresponding to these parameters may be
fixed or adaptive to the network status, depending upon
the load balancing scheme. By taking these three pa-
rameters together the traffic is deviated from high loaded
routes towards routes possessing higher energy and less
loaded. In proposed strategies a load balanced routing
path is selected among all feasible paths on the basis of
weight value calculated for each path. In a feasible path,
the higher the weight value, the higher is its suitability
for traffic distribution. The performance of the schemes
is evaluated by simulation. The result of simulation indi-
cates that, compared with previous load balanced routing
schemes DLAR and LBAR, the proposed schemes ex-
hibit a better performance in both moderately loaded and
highly loaded situations. In addition, we have shown that
the average residual battery capacity of nodes and hence
network lifetime is higher in case of proposed schemes
than AODV protocol.
6. References
[1] C. E. Perkins and E. M. Royer, and S. R. Das, “Ad hoc

A. RANI ET AL. 635
on-demand distance vector routing,” Internet Draft,
draft-ietf-manet-aodv-05.txt, March 2000.
[2] D. B. Johnson and D. A. Maltz, “The dynamic source
routing protocol for mobile ad hoc networks,” IETF Draft,
1999.
[3] S. J. Lee and M. Gerla, “Dynamic load aware routing in
ad hoc networks,” Proc. ICC, Helinski, Finland, pp.
3206–3210, June 2001.
[4] V. Saigal, A. K. Nayak, S. K. Pradhan, and R. Mall,
“Load balanced routing in mobile ad hoc networks,” El-
sevier Computer Communications, Vol. 27 pp. 295–305,
2004,.
[5] H. Hassanein and A. Zhou, “Routing with load balancing
in wireless ad hoc networks,” Proc. ACM MSWiM,
Rome, Italy, pp. 89–96, July 2001.
[6] K. Wu and J. Harms, “Load sensitive routing for mobile
ad hoc networks,” Proc. IEEE ICCCN, Phoenix, AZ, pp.
540–546, Oct. 2001.
[7] J.-W. Jung, D. I. Choi, K. Kwon, I. Chong, K. Lim, and
H.-K. Kahng, “A correlated load aware routing protocol
in mobile ad hoc networks,” ECUMN, LNCS 3262, pp.
227–236, 2004.
[8] M. R. Pearlman, Z. J. Hass, P. Sholander, and S. S.
Tabrizi, “On the impact of alternate path routing for load
balancing in mobile ad hoc networks,” Proc. of First An-
nual Workshop on Mobile and Ad Hoc Networking and
Computing, Mobihoc, Boston, MA, USA, pp. 3–10, Au-
gust 2000.
[9] A. Rani and M. Dave, “Performance evaluation of modi-
fied AODV for LOAD balancing,” Journal of Computer
Science, Vol. 3, pp. 863–868, 2007.
[10] S. Singh, M. Woo, and C. Raghavendra, “Power-aware
routing in mobile ad-hoc networks,” Proceedings of the
4th Annual ACM/IEEE International Conference on Mo-
bile Computing and Networking (MobiCom), Dallas, TX,
USA. New York, NY, pp. 181–190, Oct 25–30, 1998.
[11] A. Srinivas and E. Modiano, “Minimum energy disjoint
path routing in wireless ad-hoc networks,” Proceedings of
the 9th Annual International Conference on Mobile
Computing and Networking (MobiCom), San Diego, CA,
USA. New York, NY, pp. 122–133, Sep 14–19, 2003.
[12] M. Subbarao, “Dynamic power-conscious routing for
manets: An initial approach,” Proceedings of the 50th
IEEE Vehicular Technology Conference, VTC, Vol. 2,
pp. 1232–1237, Sep 19–22, 1999.
[13] C. K. Toh, “Maximum battery life routing to support
ubiquitous mobile computing in wireless ad-hoc net-
works,” IEEE Communications Magazine, Vol. 39, pp.
138–147, 2001.
[14] N. Gupta and S. R. Das, “Energy-aware on-demand rout-
ing for mobile ad-hoc networks,” Proceedings of the 4th
International Workshop on Distributed Computing,
IWDC, Capri, Italy, pp. 164–173, Sep 8–11, 2002.
[15] L. Y. Li, C. L. Li, and P. Y. Yuan, “An energy level
based routing protocol in Ad-hoc networks,” Proceedings
of the IEEE/WIC/ACM International Conference of In-
telligent Agent Technology (IAT’06), Hong Kong, China.
Los Alamitos, CA, pp. 306–313, Dec 18–22, 2006.