A Novel DSR-based Energy-efficient Routing
Algorithm for Mobile Ad-hoc Networks
J.-E. Garcia, A. Kallel, K. Kyamakya, K. Jobmann
J.-C. Cano, P. Manzoni
Institute of Communications Engineering (IANT)
University of Hannover
Appelstrs. 9, 30167 Hannover, GERMANY
Email: garcia,kallel,[email protected]
Department of Computer Engineering (DISCA)
Polytechnic University of Valencia
Camino de Vera, s/n, Valencia, SPAIN
Email: jucano,[email protected]
Abstract— Mobile Ad-Hoc Networks (MANETs) are wireless
networks consisting of a collection of untethered nodes with no
fixed infrastructure. An important design criterion for routing
protocols in ad hoc networks is power consumption reduction.
We describe an energy-efficient mechanism that can be used by
a generic MANET routing protocol to prevent nodes from a sharp
drop of battery power. We apply the mechanism to the Dynamic
Source Routing (DSR) and propose a novel DSR-based energyefficient routing algorithm referred to as the Energy-Dependent
DSR (EEDSR).
We compare the EDDSR algorithm with two of the most recent
proposals in this area: the Least-Energy Aware Routing (LEAR)
and the Minimum Drain-Rate (MDR) mechanism. We show that
EEDSR is the best approach to reduce and balance power consumption in a wide spectrum of scenarios.
Index Terms— Mobile Ad Hoc Network, Power-aware, Overhearing
I. I NTRODUCTION
The advent of wireless communication and mobile devices
has opened the door to research on self-organizing networks.
Mobile or “Spontaneous” networks provide to the mobile users
ubiquitous communication capabilities and information access.
Mobile Ad-Hoc Networks (MANETs) [1] are wireless networks consisting of a collection of untethered nodes with no
fixed infrastructure. Nodes in a MANET participate in forwarding data packets when the two end-points are not directly within
their radio range. The MANETs present characteristics such as
dynamic topologies, bandwidth-constrained, variable-capacity
links, and energy-constrained operations that will affect protocol design [2].
Routing protocols design for MANETs is a very active research area and many proactive and reactive protocols have
been proposed [3]. Proactive protocols find routes between all
source-destination pairs regardless of the actual need for such
routes. The more traditional proactive protocol can reduce the
needed time to get a route by inducing a high routing load over
the network. Reactive protocols, on the other hand, are based
on the reduction of the routing load by initiating new routing
activities only in the presence of data packets in need of a route.
Each different approach utilize its own design criteria to optimize the tradeoff between efficiency and resources consumption. Although most of the nodes in MANETs rely on batteries
to correctly operate, only a few routing proposal have appeared
recently whose main design criteria focuses on providing an efficient power utilization. A hybrid approach, called Conditional
Max-Min Battery Capacity Routing (CMMBCR) was devised
by C.K Toh [4]. CMMBCR relies on the residual battery capacity of nodes. Its mechanism considers both the total transmission energy consumption of routes and the remaining power of
nodes. When all nodes in some possible routes have sufficient
remaining battery capacity (i.e., above a threshold γ), a route
with minimum total transmission power among these routes is
chosen. However, if all routes have nodes with low battery capacity (i.e., below the threshold), a route including nodes with
the lowest battery capacity must be avoided to extend the lifetime of these nodes. In [5] the authors propose a new metric, the
drain rate, to be used in conjunction with the residual battery
capacity to predict the lifetime of nodes according to the current traffic conditions. The authors compares the performance
of MDR with respect to CMMBCR. Finally, the Local EnergyAware Routing (LEAR) [6] is a power aware route selection
mechanism that distributes the decision on whether or not to
cooperate in forwarding nodes among all nodes in the network.
The final goal of LEAR is to equally balance the total energy
consumption among all nodes in the network.
This paper proposes a novel routing mechanism called EDDSR that tries to avoid the use of nodes with a weak battery
supply. To achieve this goal, EDDSR uses information related
to the residual energy in the route discovery procedure. EDDSR has been been implemented using DSR as the base protocol since it was proved to be one of the more efficient reactive
routing protocol in bounded networks [7].
We compared the EDDSR mechanism with the MDR and the
LEAR proposals using simulations. The simulations evaluated
a set of specified environments over dense and sparse network
scenarios with several topology and mobility conditions. We
also present the performance evaluation of the different proposals when the energy model includes the energy dissipation due
to overhearing.
This paper is organized as follows. Section II describes a
brief review of the routing schemes related with this work. Section III presents the details of the EDDSR protocol. Section IV
compares the performance of EDDSR against MDR and LEAR
by using the ns-2 simulator. Finally, concluding remarks are
found in section V.
II. R ELATED W ORK
In this section we explain the basic operation of the reactive DSR routing protocol. We then present a brief review of
two protocols related to power-aware routing algorithm that are
based on the DSR protocol, MDR and LEAR respectively.
A. The DSR Routing Protocol
In the Dynamic Source Routing (DSR) [8] each data packet
to be transmitted carries the complete sequence of nodes by
which the packets must pass to reach the target. This property
is known as source routing, and requires the sender to know
the complete route to the destination. The protocol is based
on two basic processes: (a) the route discovery process and (b)
the route maintenance process. The route discovery process
is based on flooding and is used to dynamically discover new
routes. The route maintenance process periodically detects and
notifies networks topology changes.
In the route discovery procedure a node wishing to establish
a route broadcasts a route request (RREQ). Each node receiving the RREQ appends its own address to the packet header and
rebroadcasts it. The RREQ flooding terminates when it reaches
either the destination or an intermediate node with a route to the
destination. In this case a route reply (RREP) containing the series of accumulated addresses is sent back to the source. Upon
receiving the RREP, the source node can start transmitting the
data packets towards the destination using the route recorded
in the RREP. Each node running the DSR protocol is equipped
with a route cache which maintains the routes that a node is
aware about. DSR uses the cache intensively in order to reduce
the overhead caused by the route discovery.
The major objective of the route maintenance procedure is
to detect a broken link and find a new route to the destination.
When a node along an established route detects a link disconnection due to the neighbour’s movement, it informs the source
using the route error (RERR) packet. The source then removes
the broken link from its cache and attempts to find a new route
to the intended destination.
B. The Minimum Drain Rate mechanism
The Minimum Drain Rate Mechanism (MDR) [5] introduces
a new cost function which predicts the lifetime of the nodes.
The cost function depends on the traffic load conditions and
residual battery power. The route selection is based on the MinMax Algorithm. Each node piggybacks its current cost in the
received RREQ and rebroadcasts it. Upon receiving the first
RREQ the destination sets a timer. During a specified interval REPLY TIMEOUT the node collects all incoming RREQ.
When the timer expires the destination selects the route using
the Min-Max algorithm and includes it in the generated RREP.
Note that intermediate nodes are not allowed to reply to the
source using information from their route caches. A cached
route may be suboptimal from the algorithm’s point of view
since may not reflect actual traffic load and battery rate depletion conditions.
Moreover, each source initiates periodically a new route discovery in order to obtain routes reflecting more accurately the
power condition of the nodes. This contributes to a fair distribution of the network load. However, its means that MDR
mechanism is incompatible with the route cache used in DSR
protocol. Since the aggressive use of the route cache is one of
the main optimization that fueled up the performance of DSR,
this incongruity is the main disadvantage of MDR when it is
applied to the DSR protocol.
The MDR mechanism is a fully source-destination-based algorithm. All decisions made during the routing procedure are
taken either in the source or the destination. Intermediate nodes
only are allowed to piggyback its current cost function in routing messages.
C. The Local Energy-Aware Routing (LEAR)
The key distinguishing feature of the LEAR [6] is its skill to
distribute within all nodes in a route the decision concerning
to the willingness to forward packets for a specified sourcedestination pair. Contrary to the routing criteria of MDR, in
LEAR a node participates in the route discovery process only
when its remaining energy is above a specified threshold. If its
remaining energy is below the specified threshold, it drops the
RREQ and generates a message called drop route request (DRREQ) to inform subsequent nodes about the dropped RREQ.
A RREQ message will reach the destination only if it has
passed through nodes with enough high energy level. The destination replies immediately to the first received RREQ. The
route contained in the generated RREP refers to the shortest
path among all the energy-rich routes. When the source fails
to receive a RREP after a time period, that is calculated by the
backoff algorithm used in the DSR specification, it starts a second attempt to acquire a route for the intended destination. A
node that has previously dropped a RREQ or received a DRREQ decrements its threshold by a specified adjustment value
and rebroadcasts the RREQ only if its remaining energy lies
above the new computed threshold.
The protocol makes extensive use of the route cache. It defines a message called route cache (RCAC) which is generated
by an intermediate node when it founds a corresponding entry
in its cache. This message is sent towards the destination. The
destination node replies to the first incoming RREQ or RCAC
and ignores all later messages. The RCAC is processed in a
similar manner as the RREQ. A node which receives a RCAC
forwards the packet only if its residual energy is above the
threshold. Otherwise a message called drop route cache (DRCAC) is sent towards the destination. This message provides
the same function as the DRREQ. Another message called cancel route cache (CRCAC) is sent backwards to the node that has
started sending the RCAC, so that it removes the corresponding
entry from its cache. This will enforce this node to explore
other paths when it receives the next RREQ.
III. T HE E NERGY D EPENDENT DSR M ECHANISM
(EDDSR)
We propose a power-aware optimization that can be applied
to the route discovery process of DSR. Each node will determine its willingness to participate in forwarding based on their
current energy level. We describe a novel DSR-based routing
algorithm whose main design objective is to prolong the lifetime of nodes with low energy reserves.
Each node ni in the network has to periodically compute its
residual battery power RBPi . When a node has enough residual battery it participates in the network activities behaving exactly as a DSR node. When its residual battery power has fallen
below a specified threshold, the node delays rebroadcasting of
a received RREQ by a time period which is inversely proportional to its predicted lifetime.
i
As defined in MDR, the ratio RBP
DRi represents the predicted
lifetime of node ni , where RBPi denotes the residual battery
power and DRi the drain rate, that is an estimation of how
much energy is consumed per second, on the average, by node
ni . Thus, the predicted lifetime provides an assessment about
when the battery energy of node ni will be exhausted. In [5] the
authors detail how to evaluate RBPi and DRi for each node in
the network.
The EDDSR mechanism attempts to discourage nodes with
small lifetime from participating in the route discovery process,
thus prolonging its lifetime. In fact, it is more likely that the
RREQ sent from a node with a small predicted lifetime will
be dropped by the nodes closer to the destination since in the
DSR protocol intermediate nodes only forward the first received
RREQ. The EDDSR mechanism also modifies the route maintenance process of the DSR protocol. When the energy of a
node along an active route falls below a critical threshold, it
will immediately inform the source by sending a RERR packet.
The source will try to find another route to the same destination
by initiating another route discovery process. The critical node
will be more reluctant to participate in the forwarding activities
of a new route to the destination.
Finally, the EDDSR algorithm makes use of the route cache
in a similar manner suggested by the LEAR protocol. Thus, the
RRCAC message is processed by the intermediate nodes is the
same manner as the RREQ.
is repeated for the duration of the simulation. We considered a
maximum speed of 10 meters per second and a PAUSE TIME
of 10 seconds. A total of 12 Constant Bit Rate (CBR) sources
generated UDP data packets at a sending rate of 3 packets/sec
and a packet size of 512 bytes. The signal transmission power
is 0,2818w, which corresponds to a radio range of 250m. The
total simulation time has been set to 900 simulated seconds.
IV. S IMULATION E NVIRONMENT
The simulation results presented in this paper were obtained
using the ns-2 [9] simulator. This is an object-oriented, discrete,
event-driven network simulator developed by the VINT project
research group at the University of California at Berkley. The
simulator has been extended by the Monarch Research group
at Carnegie Mellon University [10] to support node mobility, a
realistic physical layer that includes a radio propagation model,
radio network interfaces and a Medium Access Control. We
adopted the DSR protocol as the underlying routing protocol
and introduced the code related to the MDR, LEAR and EDDSR algorithms.
Node movement was modelled using the Random Waypoint
model [8]. This model is characterized by two parameters:
the maximum speed and the PAUSE TIME. Each node randomly selects a destination and a speed, where the speed value
is uniformly distributed between 0 and the maximum speed.
The node then moves to its selected destination at the selected
speed. Once it reaches the destination, it stop for a random
pause time. The pause time is uniformly distributed between 0
and PAUSE TIME. The node eventually selects a new destination and speed, and repeats the previous steps. This behavior
, where Etx , Erx , and Eo denote the amount of energy expenditure by transmission, reception, and overhearing of a packet,
respectively. N represents the average number of neighboring
nodes affected by a transmission from node ni . Eq.(1) implies
that when the network is more dense, the packet overhearing
causes more energy consumption.
A. Energy Consumption Model
A generic expression to calculate the energy required to
transmit packet p is: E(p) = i ∗ v ∗ tp Joules, where: i
is the current consumption, v is the voltage used, and tp the
time required to transmit the packet. We suppose that all mobile devices are equipped with IEEE 802.11b network interface
cards (NICs). The energy consumption values were obtained
by comparing commercial products with the experimental data
reported in [11].
The values used for the voltage and the packet transmission
ph
pd
time were: v = 5V and tp = ( 2∗10
6 + 11∗106 )sec, where
ph and pd are the packet header and payload size in bits, respectively. We calculated the energy required to transmit and
receive a packet p by using: Etx (p) = 280mA ∗ v ∗ tp and
Erx (p) = 240mA ∗ v ∗ tp , respectively.
Moreover, we account for energy spent by nodes overhearing
packets. As shown in [11], we assume the energy consumption
caused by overhearing data transmission is the same as that consumed by actually receiving the packet.
For the purpose of evaluating the effect of overhearing, we
modified the energy model to account not for the energy expenditure due to transmission and reception but also the battery
cost to be consumed by overhearing the wireless channel. Thus,
the total amount of energy, E(ni ), consumed at a node ni is determined as:
E(ni )
= Etx (ni ) + Erx (ni ) + (N − 1) ∗ Eo (ni ) (1)
V. P ERFORMANCE R ESULTS
We evaluate the performance of EDDSR mechanism compared against pure DSR, MDR and LEAR in a dense network
scenario and a sparse network scenario. We analyzed the energy
consumption behavior of the four mechanisms. We mainly concentrate on the node expiration time, i.e., the time it takes for a
node to stop working due to lack of battery capacity. To evaluate how the different layers affect the total energy consumption
we also classify the total energy spent depending on the packet
type (Application, Routing and MAC). Finally, we also study
how NIC activities contribute to the total energy expenditure.
For the purpose of investigating the effect of overhearing, we
repeated all simulation by considering the energy cost due to
the overhearing activities.
number of nodes alive
40
30
DSR-W/o-Overh
MDR-W/o-Overh
EDDSR-W/o-Overh
LEAR-W/o-Overh
DSR-W/o-Overh
MDR-W/o-Overh
EDDSR-W-Overh
10
40
30
DSR-W/o-Overh
MDR-W/o-Overh
EDDSR-W/o-Overh
20
LEAR-W/o-Overh
DSR-W-Overh
MDR-W-Overh
10
EDDSR-W-Overh
LEAR-W-Overh
0
0
150
300
450
simulation time (s)
600
750
900
Fig. 2. Node Expiration time: dense scenario, dynamic network.
We now evaluate how the NIC activities (transmissions (Tx),
receptions (Rx), overhearing (Over) and Idle) contribute to the
total power consumption. Figure 4 shows the obtained results.
We observe that, when the overhearing activities are not considered, the energy spent in Idle mode dominates the total energy
consumed for all protocols. This result underlines that to reduce
this huge energy expenditure some techniques, similar to those
proposed in [12] must be combined with the mechanisms under
study.
When the overhearing activity is considered, the most of the
consumed energy is due to the overhearing activity. This effect
hides the merit of those mechanism that try to balance the total
energy consumption.
50
20
50
number of nodes alive
A. Results with a Dense Network Scenario
We first evaluated the three different routing mechanism in a
dense network scenario. We randomly distributed a total of 50
nodes over an area that represents an open-air square field of
670 m × 670 m.
1) Static Network: Figure 1 compares how many nodes
have died over time due to the expiration of the battery capacity
in a static environment. We can clearly observe different results
between the two considered cases, namely when the energy due
to overhearing is ignored and when it is included.
In an static network basically all protocols behave similarly.
We notice a slight improvement of the EDDSR at the end of
the simulation. This behavior is due to the EDDSR use of the
rerouting technique that helps to avoid the use of nodes with
a weak battery. The relatively low performance of MDR can
be attributed to the use of longer routes, thereby increasing the
relaying load and consequently the energy consumption at a
larger number of nodes.
LEAR-W-Overh
0
150
300
450
600
750
900
simulation time(s)
Fig. 1. Node Expiration time: dense scenario, static network.
When overhearing is considered, the power aware algorithms
fail to balance the energy consumption due to the enormous
amount of energy spent in overhearing activities.
2) Dynamic Network: We now evaluate how node mobility
affects performance indexes. We repeated all the simulations
using a maximum node speed equal to 10 m/sec. When the
energy due to overhearing is considered, all protocols behaves
similarly (see Figure 2). When the energy model exclude overhearing, we observe that MDR substantially delays the time for
first node’s failure. However, EDDSR obtains the highest number of survived nodes and improves MDR in terms of the average node lifetime. The LEAR protocol suffers from the flooding problem caused by the DRREQ packets sent in a broadcast
manner. This negative effect is more clearly observed in the dynamic scenario where the route recovery procedure needs to be
executed frequently due to disconnections. This characteristic
suggests that the LEAR protocol does not offer a good scalability.
Figure 3 highlights the energy consumption depending on the
packet type. The bad performance of LEAR is mainly due to the
great amount of energy spent by control packets.
B. Results with a Sparse Network Scenario
We now present the obtained simulation results in an openair sparse network where 50 nodes have been randomly placed
in a square area of 1500 m × 1500 m.
1) Static Network: In the sparse scenario the number of
available routes is very limited. Moreover, where no mobil-
400
Routing
Energy consumption (Joules)
0
Data
300
MAC/ARP
200
100
0
DSR
Fig. 3.
work.
MDR
EDDSR
LEAR
Energy consumption per packet type: dense scenario, dynamic net-
100%
50
overh
Rx
40
Tx
number of nodes alive
Energy consumption %
Idle
75%
50%
25%
30
DSR-W/o-Overh
20
MDR-W/o-Overh
EDDSR-W/o-Overh
LEAR-W/o-Overh
LEAR-With
DSR-W-Overh
EDDSRWith
MDR-With
DSR-With
LEAR-W/o
EDDSR-W/o
MDR-W/o
DSR-W/o
0%
10
MDR-W-Overh
EDDSR-W-Overh
LEAR-W-Overh
0
0
150
300
450
600
750
900
simulation time(s)
Fig. 4. Energy consumption depending on network card activity: dense scenario, dynamic network.
Fig. 5. Node Expiration time: sparse scenario, static network.
50
40
number of nodes alive
ity is considered the distribution of the network load is quite
unbalanced. In most cases there is only one route between a
source-destination pair.
MDR presents the best performance, especially at the beginning of the simulation (see Figure 5). This result is explained
because the MDR periodically executes the route selection process thus allowing battery depletion detection at a very early
stage. Contrary to the MDR behavior, the LEAR protocol concentrates the whole traffic on a single route once it has been
first discovered. Finally, the EDDSR algorithm acts similarly
as the LEAR approach at the beginning of the simulation. The
rerouting technique used by EDDSR will be more likely not to
be able to find alternative paths because of the reduced number of routes. According to this results we argue that under
this scenario the MDR mechanism achieves a better energyconsumption balancing along the network.
We can also observe that all protocols achieve almost the
same results at the end of the simulation. It seems that the death
of some particular nodes restricts further the number of available routes making the distribution of the network load even
impossible.
2) Dynamic Network: Finally, we consider nodes mobility in the sparse network. Figure 7 shows that all the energyefficient algorithms, particularly EDDSR and MDR, outperform DSR.
All the energy-based mechanisms postpone the first node’s
battery exhaustion. The number of survived nodes at the end
of the simulation confirms also the improved performance of
LEAR, MDR and EDDSR. In such scenarios, the aggressive
use of the route cache in DSR, carries the drawback that it often
provides invalid paths. This results is an extra expenditure of
energy consumed in the exchange of control packets.
Figure 6 shows that DSR consumes a high percentage of energy due to the routing activities. The LEAR mechanism also
induces a high amount of energy expenditure. This effect is
mainly due because of the frequent use of the route discovery
process, that will make use of DRREQ packets sent in a broadcast manner.
The EDDSR and MDR approaches seem to profit from the
30
20
DSR-W/o-Overh
MDR-W/o-Overh
EDDSR-W/o-Overh
LEAR-W/o-Overh
10
DSR-W-Overh
MDR-W-Overh
EDDSR-W-Overh
LEAR-W-Overh
0
0
150
300
450
600
750
900
simulation time(s)
Fig. 6. Node Expiration time: sparse scenario, dynamic network.
mobility of the nodes. Mobility allows new routes to appear
after a route breakage. Thus, MDR and EDDSR can balance
the nodes utilization and consequently the energy consumption.
MDR does not rely on the route cache and additionally has the
ability to acquire fresh routes through the periodical initiating
of the route discovery procedure. These routes not only reflect
the residual power of the nodes, but also the actual topology
which banishes the overhead caused by the route recovery. In
the EDDSR case an intermediate node is not allowed to send a
RREP containing an invalid route back to the source. According to this appreciations we also observe a small routing energy
expenditure for both mechanisms.
VI. C ONCLUSION
We described a novel power-aware route discovery algorithm
called Energy Dependent routing algorithm. Its main goal is to
extend the average lifetime for each node while balancing the
total energy consumption among all nodes in the network. We
then modified the DSR protocol to include our proposal and
called it the energy dependent DSR protocol (EDDSR).
300
Energy consumption (Joules)
Routing
Data
MAC/ARP
200
100
0
DSR
Fig. 7.
work.
MDR
EDDSR
LEAR
Energy consumption per packet type: sparse scenario, dynamic net-
Using the ns-2 simulator, we compared EDDSR against
DSR, MDR and LEAR mechanisms. Our study proved that
MDR and EDDSR clearly outperform DSR in terms of node
lifetime especially in dynamic scenarios. The LEAR mechanism generates an high energy expenditure due to its route discovery process especially in dense networks. Thereafter, this
protocol should be used only in sparse networks with static
nodes or nodes moving with low speed.
The continuous evaluation of the energy budget of a node
along an active route in EDDSR prevent nodes from being overwhelmed by network traffic, thereby contributing to better load
balancing and a fair energy utilization. EDDSR shows a similar
behavior that MDR, however EDDSR has the additional merit
of being compatible with the use of the route cache used by
DSR.
R EFERENCES
[1] Internet Engineering Task Force, ”Manet working group charter,”
http://www.ietf.org/html.charters/manet-charter.html.
[2] S. Corson and J. Macker, ”Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations,” RFC
2501, January 1999.
[3] E.Royer and C.-K. Toh, ”A Review of Current Routing Protocols for Ad
Hoc Mobile Wireless Networks,” IEEE Personal Communications Magazine, Vol. 6, No. 2, April 1999.
[4] C.-K. Toh, ”Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks,” IEEE Communications
Magazine, June, 2001.
[5] D.Kim, J.J.Garcia-Luna-Aceves, K.Obraczka, J.Carlos Cano, P.Manzoni,
”Power Aware Routing Mechanism Based on the Energy Drain Rate Metric in Mobile Ad Hoc Networks,” 11th IEEE International Conference on
Computer Communications and Networks (IC3N), 2002.
[6] K.Woo, C.Yu, H.Y.Youn, and B. Lee, ”Non-Blocking, Localized Routing Algorithm for Balanced Energy Consumption in Ad Hoc Networks,”
International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication System, MASCOTS, 2001.
[7] J.Carlos Cano, P.Manzoni, ”A Performance Comparison of Energy Consumption for Mobile Ad Hoc Networks Routing Protocols,” IEEE/ACM
MASCOTS 2000: Eighth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2000.
[8] D.B. Johnson, D.A. Maltz, Y.C. Hu, and J.G. Jetcheva, ”The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks,” Internet
Draft, MANET Working Group, draft-ietf-manet-dsr-04.txt, November
2000,Work in progress.
[9] K. Fall and K. Varadhan, ”ns Notes and Documents,” The VINT Project.
UC Berkeley, LBL, USC/ISI, and Xerox PARC, February 2000, Available
at http://www.isi.edu/nsnam/ns/ns-documentation.html.
[10] The CMU Monarch Project. The CMU Monarch Project’s Wireless and
Mobility Enhancements to ns. http://www.monarch.cs.cmu.edu. Work in
progress.
[11] Laura Feeney and M. Nilsson, ”Investigating the Energy Consumption of
a Wireless Network Interface in an Ad Hoc Networking Environment,”
IEEE INFOCOM 2001.
[12] Suresh Singh , C. S. Raghavendra, ”PAMAS power aware multi-access
protocol with signalling for ad hoc networks,” ACM SIGCOMM Computer Communication Review July 1998.