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On using Multiple Quality Link Metrics with Destination Sequenced Distance Vector Protocol for Wireless Multi-Hop Networks



In this paper, we compare and analyze performance of five quality link metrics forWireless Multi-hop Networks (WMhNs). The metrics are based on loss probability measurements; ETX, ETT, InvETX, ML and MD, in a distance vector routing protocol; DSDV. Among these selected metrics, we have implemented ML, MD, InvETX and ETT in DSDV which are previously implemented with different protocols; ML, MD, InvETX are implemented with OLSR, while ETT is implemented in MR-LQSR. For our comparison, we have selected Throughput, Normalized Routing Load (NRL) and End-to-End Delay (E2ED) as performance parameters. Finally, we deduce that InvETX due to low computational burden and link asymmetry measurement outperforms among all metrics.
arXiv:1203.2730v1 [cs.NI] 13 Mar 2012
N. Javaid, A. Bibi, Z. A. Khan$, K. Djouani,♯
Department of Electrical Engineering, COMSATS, Islamabad, Pakistan.
$Faculty of Engineering, Dalhousie University, Halifax, Canada.
F’SATIE, TUT, Pretoria, South Africa.
LISSI, Universit´e Paris-Est Cr´eteil (UPEC), France.
In this paper, we compare and analyze performance of five
quality link metrics for Wireless Multi-hop Networks (WMhNs).
The metrics are based on loss probability measurements; ETX,
ETT, InvETX, ML and MD, in a distance vector routing pro-
tocol; DSDV. Among these selected metrics, we have im-
plemented ML, MD, InvETX and ETT in DSDV which are
previously implemented with different protocols; ML, MD,
InvETX are implemented with OLSR, while ETT is imple-
mented in MR-LQSR. For our comparison, we have selected
Throughput, Normalized Routing Load (NRL) and End-to-
End Delay (E2ED) as performance parameters. Finally, we
deduce that InvETX due to low computational burden and link
asymmetry measurement outperforms among all metrics.
Index TermsDSDV, OLSR, ETX, Inverse ETX, ML,
MD, ETT, IBETX, ELP, distance vector,loss probabilities
A routing protocol is responsible for significant performance
from the underlying wireless network. A routing link met-
ric is a key component of a routing protocol. As, it finds all
possible end-to-end routes and also the fastest route. Mini-
mum Hop-count; non-quality link metric is the most popular
and is IETF standard metric [1]. It is appropriately used by
Wireless Ad-hoc Networks, where the objective is to find new
paths as fast as possible in the situations where quality paths
cannot be found quickly and/or can not efficiently work be-
cause of higher rates of node mobility. Moreover, hop-count
is the simplest to calculate and it avoids any computational
burden on the routing protocol. It is obvious from its equa-
tion: Hop CountPe2e
Quality Link Metrics (QLMs) are firstly introduced in [2],
for successful delivery of data packets in static networks. Ef-
ficiency of static WMhNs depends upon low routing latency,
minimized routing load, and less end-to-end delay (E2ED).
To achieve proficient performance of a protocol in such net-
works a realistic QLM is needed.
Several QLMs have been proposed, like, Expected Trans-
mission Count (ETX)[2], Expected Transmission Time (ETT)
[3], Interference and Bandwidth Adjusted ETX (IBETX) [4],
Expected Link Performance (ELP) [5], Minimum Loss (ML)
[6], Minimum Delay (MD)[7] and Inverse ETX (InvETX) [8].
The metrics, ETX, ML, MD and InvETX have already been
implemented [8] with a proactive routing protocol, Optimized
Link State Routing (OLSR) [9] using Link State routing tech-
nique. While, ETX, IBETX, and ELP are implemented with
Destination Sequenced Distance Vector (DSDV) [10] based
on distance vector routing algorithm. Distance vector rout-
ing uses the next hop information during exchanging the rout-
ing information, whereas link state information contains the
whole topological information. In this paper, we implement
ETX, ML, MD, ETT and InvETX in distance vector protocol
DSDV. Original ETX is implemented with DSDV[2].
In this paper, we have implemented five QLMs; ETX, In-
vETX, ETT, ML and MD with DSDV which is based on dis-
tance vector algorithm. Previouse implementation of QLMs
in routing protocols have not considered routing load for per-
formance measurements. We, in our previous work [8] have
considered routing load while analyzing the performance of
OLSR which is a link state based proactive routing protocol.
Now, in the same way, we are evaluating the performance of
those link metrics along with ETT in this paper. Moreover,
ML and MD are implemented in OLSR, and ETT is imple-
mented with MR-LQSR, on the other hand, we implement
these metrics along with InvETX in DSDV.
[2] is the very first work launching the idea of quality routing
by proposing ETX. In this section, we discuss five QLMs,
among all are based on ETX except MD. A detailed study on
ETX-based metrics can be found in [1].
(1) ETX: Forward and reverse loss rates and link asym-
metries of the links are measured by calculating the loss prob-
abilities of links by broadcasting probe packets. In this ap-
proach, each node is supposed to periodically send out a broad-
cast probe packets only to the neighbors without any retrans-
mission. Nodes track the number of successfully received
probes from each neighbor during a sliding window time;
10 seconds, and include this information in their own probes.
Nodes can calculate reverse loss probability; dr, directly from
the number of probes they receive from a neighbor in the time
window, and they can use the information about themselves
received in the last probe from a neighbor to calculate forward
loss probability;df.
ET XPe2e=X
(2) InvETX: remarkably avoids the computational over-
head and thus achieves least delay [8]. ETX calculates the
inverse of probability of success (product of forward and re-
verse probabilities) but as the names implies, InvETX directly
computes probabilities.
I nvET XPe2e=X
(3) ETT: of a link as a ”bandwidth-adjusted ETX” is de-
fined in [3]. Authors consider the link bandwidth to obtain
the time spent in transmitting a packet. They start with ETX
and divide by with link bandwidth. Let Sdenote the size of
the packet and Bthe bandwidth (raw data rate) of the link l.
ET Tl=E T Xl×tl(3)
ET Tl=E T Xl×(SF
ET TPe2e=X
Forward and reverse loss rates of links is measured in
ETX portion of ETT. These lost rates are calculated through
broadcast prob packet as in [2]. The problem of determining
the bandwidth of each link is more complex. For the measure-
ment of bandwidth, ETT uses the technique of packet pairs,
i.e, after every minute, each node is supposed to send two
back-to-back probe packets to each of its neighbors. First
probe of size of 137 bytes, while the second probe packet
is comparatively heavier and is of 1137 bytes. Upon receiv-
ing these two probes, neighbor measures the time difference
between the reception of the first and the second probe and
acknowledges the sender with the difference. To estimate the
bandwidth, sender takes the minimum of 10 consecutive sam-
ples and then divides the size of the second probe packet by
the minimum size sample.
(4) ML: is based on ETX with the aim of selecting the
path with the minimum loss probability. It uses the probabil-
ity of successful transmissions, and not the inverse probabil-
ity, as in ETX. Another difference of ML with ETX is that
ETX finalizes the end-to-end route with two or three links,
whereas, like InvETX and IBETX, ML also considers the
longer paths. The whole route’s probability is given by the
product of the links’ probabilities instead of the sum of their
inverse probabilities (like ETX). It has the advantage of elim-
inating the routes with high loss rate, and the disadvantage
that some low quality links may still be taken into account in
choosing a given route, since the metric considers only the
total probability product [11].
(5) MD: With MD metric, routing table computation is
based on the total minimum transmission delay. The trans-
mission delay measurements come from a variant of a link
capacity estimation technique, known as Ad-hoc Probe. The
technique takes into account the differences in clock synchro-
nization, thus providing a more reliable measurement. A dis-
advantage is that this metric considers routes which have nodes
sharing a collision domain with many other nodes, and this
tends to degrade the communication on such routes. The Ad-
Hoc Probe algorithm uses packet-pairs to measure the packet
dispersion [11]. The formula used to calculate packet disper-
sion Tfrom the packet pair sample is given by:
T=Trecv2,i Trecv 1,i (8)
T= (Trecv2,i Tsend,i δ)(Trecv 1,i Tsend,i δ)(9)
Where δis clock offset of nodes, Tsend,i is packet sending
time stamped by sender, whereas Tr ecv1,i, and Tr ecv2,i are
receiving time of each packet stamped by receiver node.
Here, we discuss the direct influences of mathematical design
of QLMs on the performance of routing protocol implement-
ing it and indirect affects on efficiency of the underlying net-
work being operated by respective protocol.
(1) Link asymmetry: Link asymmetry can be used by
QLMs to check the loss ratios of a link in both directions;
forward and backward. If the asymmetry of the link is not
determined correctly then route entry is downgraded to uni-
directionality questionable. If a route request is received over
such a link, the node delays forwarding it while it issues a
direct, one-hop unicast route request back to the questionable
neighbor. If acknowledgement is received back to the sender,
then node forwards the original route request and confiscates
the blacklist entry, otherwise, request is dropped by node. In
ETX, invETX and ML account link loss ratios, in both direc-
tions of link.
(2) Low computational overhead: For routing metric,
necessary computations should be considered that must not
consume memory, processing capability and the most impor-
tant; battery power [8]. They discuss the case of three widely
used routing link metrics for wireless routing protocols: ETX,
invETX, ETT and ML, as in Fig.1.
(3) Low routing overhead (routing latency and rout-
ing load): Routing overhead is discussed in detail in [12].
Wireless networks have limited bandwidth Computing a link
metric in such a way that it generate extra routing overhead
reduce packet delivery.
2000 3000 4000 5000 6000 7000 8000 9000 10000
No of nodes
Execution time(ms)
100 200 300 400 500 600 700 800 900 1000
Networkload [kb/s]
Throughput [kb/s]
100 200 300 400 500 600 700 800 900 1000
Networkload [kb/s]
100 200 300 400 500 600 700 800 900 1000
Networkload [kb/s]
Fig. 1. Simulation Results for Modeled Framework
(4) Trade-offs: Generally, a protocol achieves higher through-
put values at the cost of increased routing latency in the case
of static networks. Whereas, in mobile networks, where link
breakage is frequent cause more routing load to obtain better
throughput from the network [8]. A QLM in a specific sce-
nario supposed to a suitable trade-off between routing latency
and routing load to achieve optimal performance.
This section provides the details concerning the simulation
environment. We compare the performance of ETX, InvETX,
ML, ETT and MD in NS-2. The window wused for link
probe packets is chosen to be of size 10s. The wireless net-
work consists of 50 nodes randomly placed in an area of 1000m
x 1000m. The 20 source-destination pairs are randomly se-
lected to generate Continuous Bit Rate (CBR) traffic with
a packet of size 640bytes. To examine the performance of
QLMs under different network loads, the traffic rate is varied
from 1 to 10 packets per second. For each packet rate, the sim-
ulations are run for five different topologies for 900seach and
then their mean is used to plot the results. Wireless networks
suffer from bandwidth and delay. Because of on-demand na-
ture, the reactive protocols are best suited to cope with these
issues for mobile scenario where change in topology is fre-
quent. We are dealing with static networks where proactive
protocols work at their best because of getting the picture of
whole topology and independent of the data generation. Per-
formance of QLMs has been evaluated and then compared
with three performance parameters; throughput, E2ED, and
(1) Throughput: Among selected five QLMs; ETX (eq.1),
InvETX (eq.2), ETT (eq.6), ML (eq.7) and MD (eq.9), MD is
not considering link asymmetry. While ETT, ETX, and ML
are introducing computational overhead, as depicted in Fig.1.
Forward and reverse probes are sent by ETX, ETT, InvETX
and ML to check link asymmetry periodically. Asymmetric
links and computation overhead can increase drop rate by in-
troducing computational delay and delivering data to unreli-
able path due to lack of correct assumption about link status.
ETX due to computational overhead increases delay, there-
fore, produces lower throughput value. ETT uses extra probes
after every minute by sending two packets of 137 bytes and
1137 bytes to estimate the bandwidth. This not only intro-
duces routing load due to extra probes at routing layer but
also computational overhead is introduced by estimating the
bandwidth from these probes. In higher network loads, net-
work is more sensitive for routing load and delay. Computa-
tional overhead and routing load lead to more drop rate in high
data traffic rates. Lack of link asymmetry in MD produces
more drop rates. ML uses product of drop rates for a link
as well as for a complete path, and introduces computational
overhead (as shown in Fig.1). InvETX produces lowest com-
putational overhead (Fig.1) by taking the product of forward
and reverse delivery rates of a link and selecting the maxi-
mum InvETX value of a path which is sum of individual links
of a path. So, InvETX achieves high throughput, as obvious
from Fig.2. Unlike MD, InvETX calculates link asymmetry
as well as contains low routing load (Fig.4) by avoiding the
use of extra probes after every minute like in ETT.
(2) E2ED: is the time a packet takes to reach the desti-
nation from the source. We have measured it as the mean of
Round Trip Time (RTT) taken by all packets. Computational
delay and longer paths cause latency for end-to-end path cal-
culation. MD estimates the paths based upon one way de-
lay, while ETT selects paths with shorter delay based on the
bandwidth estimation. Therefore in medium and low network
loads, both metrics produce lower delay (Fig.3), while in high
network loads, ETT due to computational overhead and MD
due to lack of measuring the link asymmetry augments E2ED.
InvETX as compared to ETX and ML has lowest computa-
tional overhead thus selecting the paths with low delay, as
shown in Fig.3.
(3) NRL: is the number of routing packets transmitted by
a routing protocol for a single data packet to be delivered suc-
cessfully at destination. ETT and MD both are supposed to
calculate the paths with low latency. In ETT, bandwidth es-
timation is considered to select a path with low latency. MD
produces the lowest value of routing load (Fig.4) because it
generates delivery measurement probes only in forward di-
rection. On the other hand, ETX, InvETX and ML send both
forward and reverse delivery probes to check link asymmetry
thus producing more routing load as compared to MD. The
highest NRL among the selected metrics is produced by ETT,
because it uses forward and reverse delivery probes with a pair
of packets for calculating low E2E path. Each node is sup-
posed to send two back-to-back probe packets to each of its
neighbor after every minute. First probe packet is small and
having the size of 137 bytes, while the second probe packet
is comparatively heavier and of 1137 bytes. Upon receiving
these two probes, neighbor computes the time difference be-
tween the reception of the first and the second probes and
sends acknowledgement value back to the sender. For band-
width estimation, sender takes the minimum of 10 consec-
utive samples and then divides the size of the larger probe
packet by the minimum sample value.
For computing routes by using QLMs, loss probabilities may
be required. In OLSR, while computing the loss probabilities,
modified HELLO messages are used. Whereas, DSDV is sup-
posed to send extra small probes for measuring the loss prob-
abilities and this leads to more routing load. As in this work,
we are considering the routing load as a metric to evaluate
the performance; therefore, we first discuss the route mainte-
nance operation of DSDV. DSDV periodically exchanges link
state updates with its neighbors to maintain the recent infor-
mation about connectivity in the network. Moreover, routes
are updated thorough trigger updates also. The periodic route
updates are flooded with full dump period, while trigger up-
date flooding takes place through incremental dumps only
when a link is broken in an active route. Although, the trigger
route update operation may appear surplus because of the em-
ployment of link state monitoring periodically, it has certain
advantages. Monitoring the link status periodically leads to
routing loops which are eliminated in trigger route updates us-
ing the latest sequence numbers. To keep updated all nodes in
a network with topological information, each routing protocol
has to exchange routing packets generating routing overhead.
For this overhead the protocol has to pay some cost in the
form of energy consumed per packet. So, we define this cost
in the equation given below to calculate the routing overhead
in terms of packet cost. First two costs in eq.10 (eq.10a, 10b)
are the same as we have defined in [12]. We define third part
(eq.10c) for this work. CDS DV
Emetric shows the cost of those
packets which are to be sent for measuring a QLM.
Etotal =CDS DV
Eper +CDSD V
Etri +CDS DV
Emetric (10)
Expressions for CDSDV
Eper and CDSD V
Etri are same as in [12]
and can be further studied from [13].
EP er =Zτ1
(Perr davg +davg
(Perr )i+1
ET ri =Zτ2
(1 Pnlb)nPerr davg +davg
(Perr )i+1 i
Emetric =CET X,I nvE T X,ML
EQLM (10c)
EQLM = (αdf+αdr)×τNL (11)
EQLM +ZτNL αsprobes +αlprobes (12)
EQLM = 2 ×αdf×τN L (13)
Where, αdfand αdrare the rates of forward and reverse
probe deliveries. τN L is the total network life time or total
simulation time. Similarly, for ETT, αsprobes and αlprobes
are the rates of exchange of small and large probes.
Routing protocols are responsible for finding efficient route
selection mechanism for reliable communication by select-
ing optimal routes. There are two types of link metrics; non-
quality link metrics and quality link metrics. In this paper, we
have compared and analyzed the performance of five qual-
ity link metrics which are based on loss probability measure-
ments; ETX, ETT, InvETX, ML and MD. For comparison,
we have selected distance vector routing algorithm protocol;
DSDV. We implemented ML, MD, InvETX and ETT in DSDV
and computed computational burden of loss probability mea-
surements in ETX, InvETX and ML. It is analyzed that ETX
and ML produce more computational burden when compared
with InvETX. MD does not measure the link asymmetry, thus
fails to achieve appreciable throughput for the operating pro-
tocol. InvETX due to low computational overhead and ac-
curate link asymmetry measurement outperforms in DSDV
among five selected quality link routing metrics.
[1] Javaid, N. et al., ”Performance Study of ETX Based
Wireless Routing Metrics”, IC4, pp. 1-7, 2009.
[2] De Couto et al., ”A High-Throughput Path Metric for
Multi-Hop Wireless Routing”, ACM Mobicom, 2003.
[3] Richard D. et al., ”Routing in Multi- Radio, Multi-Hop
Wireless Mesh Networks”, ACM Mobicom, 2004.
[4] Javaid, N. et al., ”Interference and Bandwidth Ad-
justed ETX in Wireless Multi-hop Networks”, 53rd
IEEE Globecom Workshop SaCoNAS-II, 2010.
[5] Usman A. et al., ”An Interference and Link-Quality
Aware Routing Metric for Wireless Mesh Networks,”.
IEEE 68th VTC, 2008.
[6] D. Passos, et al., ”Mesh network performance measure-
ments”, I2TS, 2006.
[7] W. Cordeiro et al.,” Providing quality of service for
mesh networks using link delay measurements”, 16th
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[8] N. Javaid, et al. ”Identifying Design Requirements for
Wireless Routing Link Metrics”, 54th IEEE Globecom
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[9] Qayyum, A. et al., Multipoint relaying: An efficient
technique for flooding in mobile wireless networks,
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Sequenced Distance- Vector routing (DSDV) for mobile
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Supplementary resource (1)

September 2013
N. Javaid · A. Bibi · Zahoor Ali Khan · Karim D. Djouani
... In [4] [5], communication time between nodes is found when the nodes are moving in same and opposite direction with same or different speeds. In this study, we improve the work of678910 and calculate the probability of link establishment between nodes when they are moving in same and opposite direction with same or different speeds. The study of DYMO is done in comparison with other routing protocols [6]. ...
... In this study, we improve the work of678910 and calculate the probability of link establishment between nodes when they are moving in same and opposite direction with same or different speeds. The study of DYMO is done in comparison with other routing protocols [6]. The performance metrics such as jitter, throughput and delay is taken under observation in their work. ...
... The performance metrics such as jitter, throughput and delay is taken under observation in their work. The study [6] involved the consistently varying network topology and comparison of DSR with AODV in MANETs for different scenarios and performance metrics to propose the best scenario for each routing protocol to maximize its efficiency. In [7] the authors modified the OLSR protocols in their paper. ...
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Infrastructure-less wireless multi-hop networks have long been proposed for natural disaster and warfare scenarios. However, the current demand of such networks has been towards social networking, gaming and ultimately, ubiquitous computing. In fact, the increasing number of users that own wireless capable devices is taking these networks to an entirely different scale. Existing routing protocols do not scale and do not consider the context wherein services operate. By presenting an alternative routing scheme that appropriately handles mobility of users among different contexts, large-scale clustered wireless networks are designed, using an efficient gateway selection with load-balancing capabilities. This approach uses a virtual hierarchy of clusters to explore the contextual-proximity of nodes, while reducing the total overhead of routing traffic even when compared with other cluster-based approaches. Moreover, it is capable of predicting gateway link disconnections, increasing the total amount of delivered data. The obtained results reveal that this routing scheme outperforms existing routing protocols regardless of the mobility pattern being used, being consistently lighter in overhead and delivering up to 50% more data traffic. These results motivate a new era of large-scale wireless multi-hop networks suitable for hand-held devices exchanging data amongst themselves.
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This work is based on measurements of the ReMesh wireless mesh network deployed over the city of Niterói, Brazil. It uses a modified version of the OLSR ad hoc routing protocol. OLSR has the goal of maximizing throughput by minimizing the number of transmissions over the wireless shared medium selecting routes based on the sum of the expected transmission count (ETX) of each link from a source node towards a destination node. Because of routing instabilities and the high packet loss rates (PLR) observed with the original OLSR algorithm, this work uses an algorithm for selecting multi-hop paths based on minimum loss probability along the entire path. Test results show that the mesh network performance has been improved, leading to more stable routes, lower packet loss rates, shorter delays and in many cases a small increase in network throughput.
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Wireless mesh networks (WMNs) can be used in many different ap-plications. However, they lack standards and, as a consequence, a number of issues must still be addressed to ensure the proper functioning of these networks. Amongst these issues, routing is this paper's main concern. Thus, we propose the use of multiple metrics with the proactive Optimized Link State Routing (OLSR) protocol, in order to provide quality of service routing. Even though it has already been proved that routing with multiple metrics is an NP-complete problem, we show how the techniques of Analytic Hierarchy Process (AHP) and Pruning may be combined to perform multiple-metric routing, offering the best available routes based on the considered metrics. A study on the performance of the metrics considered for the proposal is also carried out in the NS simulator.
Conference Paper
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One of the main problems faced by ad hoc networks is providing specific quality of service guarantees for multimedia applications, mainly due to factors such as radio signal fading and node mobility. Since mesh networks are a special type of ad hoc network, they inherit these networks' problems. This paper's main goal is to present OLSR-MD, an extension to OLSR (optimized link state routing), to provide quality of service based on link delay measurements. An evaluation of OLSR-MD in a mesh network to be deployed at the Federal University of Para, by means of ns2 (version 2.30) simulations, showed that this protocol performed better than other OLSR based alternatives studied in the simulations.
Conference Paper
This paper presents the expected transmission count metric (ETX), which finds high-throughput paths on multi-hop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. The ETX metric incorporates the effects of link loss ratios, asymmetry in the loss ratios between the two directions of each link, and interference among the successive links of a path. In contrast, the minimum hop-count metric chooses arbitrarily among the different paths of the same minimum length, regardless of the often large differences in throughput among those paths, and ignoring the possibility that a longer path might offer higher throughput.
Conference Paper
Being most popular and IETF standard metric, minimum hop count is appropriately used by ad hoc networks, as new paths must rapidly be found in the situations where quality paths could not be found in due time due to high node mobility. There always has been a tradeoff between throughput and energy consumption, but stationary topology of WMNs and high node density of WSN's benefit the algorithms to consider quality-aware routing to choose the best routes. In this paper, we analytically review ongoing research on wireless routing metrics which are based on ETX (expected transmission count) as it performs better than minimum hop count under link availability. Performances over ETX, target platforms and design requirements of these ETX based metrics are high-lighted. Consequences of the criteria being adopted (in addition to expected link layer transmissions & retransmissions) in the form of incremental: (1) performance overheads and computational complexity causing inefficient use of network resources and instability of the routing algorithm, (2) throughput gains achieved with better utilization of wireless medium resources have been elaborated.
Conference Paper
This paper presents the expected link performance (ELP) metric for finding high throughput, low delay paths in 802.11 mesh networks. ELP combines three different mechanisms to accurately determine the expected link performance. Link quality information is combined with cross-layered link interference estimation to select optimal paths. Simulation results show that ELP significantly outperforms both hop count as well as the popular ETX metric. ELP has up to 12% higher throughput and about 40% smaller delay than ETX.
In this paper, we present a detailed framework consisting of modeling of routing overhead generated by three widely used proactive routing protocols; Destination-Sequenced Distance Vector (DSDV), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). The questions like, how these protocols differ from each other on the basis of implementing different routing strategies, how neighbor estimation errors affect broadcast of route requests, how reduction of broadcast overhead achieves bandwidth, how to cope with the problem of mobility and density, etc, are attempted to respond. In all of the above mentioned situations, routing overhead and delay generated by the chosen protocols can exactly be calculated from our modeled equations. Finally, we analyze the performance of selected routing protocols using our proposed framework in NS-2 by considering different performance parameters; Route REQuest (RREQ) packet generation, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) with respect to varying rates of mobility and density of nodes in the underlying wireless network.