In e na ional Jou nal o Eme ging Resea ch in Science, Enginee ing, and Managemen
Vol. 1, Issue 2, pp.01-06, Augus 2025.
www.ije sem.com eISSN – 3107-9075
IJERSEM@2025 h ps://doi.o g/10.58482/ije sem. 1i2.1 1
Pe o mance Analysis o Rou ing P o ocols in
MANETs
1Rayi Su ekha, 2A a abhumi Di ya, 3B. Malakonda Reddy,
4Ma uboina S a ani
1Assis an P o esso , Depa men o CSE, Siddha h Ins i u e o Enginee ing & Technology, Pu u , India.
su ekhasie [email p o ec ed]
2Assis an P o esso , Depa men o CSE, N.B.K.R. Ins i u e o Science and Technology, Vidyanaga , India.
di ya [email p o ec ed]
3P o esso , Depa men o ECE, Na ayana Enginee ing College, Gudu , India. bmalakonda edd[email p o ec ed]
4Assis an P o esso , Depa men o CSE, S ee Venka eswa a College o Enginee ing, Nello e, India.
ms a ani. [email protected]
Abs ac : Mobile Ad Hoc Ne wo ks (MANETs) ep esen a dynamic and in as uc u e-less ne wo k pa adigm, whe e e icien and eliable
ou ing is c ucial due o equen opology changes and esou ce cons ain s. This pape p esen s a comp ehensi e pe o mance analysis o i e
widely used MANET ou ing p o ocols—AODV, DSR, DSDV, OLSR, and ZRP—unde a ying ne wo k condi ions. Using NS-2.35 as he
simula ion en i onmen , he s udy e alua es hese p o ocols based on key Quali y o Se ice (QoS) me ics including packe deli e y a io (PDR),
end- o-end delay, h oughpu , ou ing o e head, packe loss a io (PLR), and ene gy consump ion. The esul s demons a e ha AODV and
OLSR o e supe io pe o mance in e ms o deli e y and h oughpu , while DSR is a o able o ene gy-cons ained applica ions. DSDV and
ZRP show mode a e pe o mance wi h limi a ions unde speci ic scena ios. The analysis unde sco es he need o p o ocol selec ion based on
applica ion equi emen s and highligh s he po en ial o in eg a ing us -awa e and in elligen ou ing enhancemen s in u u e esea ch.
Keywo ds: AODV, Ene gy Consump ion, MANET, Rou ing P o ocols, Th oughpu
1 INTRODUCTION
Mobile Ad Hoc Ne wo ks (MANETs) ha e eme ged as a c ucial componen in he ealm o wi eless communica ions due o
hei sel -con igu ing, in as uc u e-less na u e, enabling nodes o communica e dynamically wi hou he need o a ixed base
s a ion. These ne wo ks a e widely applied in mili a y ope a ions, disas e eco e y, ehicula communica ion, and IoT-based
en i onmen s, whe e quick deploymen and au onomous ne wo king a e essen ial [1], [2]. Rou ing in MANETs emains a
signi ican challenge due o he inhe en cha ac e is ics o such ne wo ks—dynamic opology, limi ed ene gy esou ces, equen
disconnec ions, and mobili y o nodes. E icien ou ing p o ocols mus ensu e eliable packe deli e y, minimal delay, and op imal
ene gy consump ion, despi e he lack o a cen alized au ho i y [3], [4]. Consequen ly, a a ie y o ou ing p o ocols ha e been
de eloped, each designed o cope wi h di e en challenges and o op imize pe o mance unde a ying ne wo k condi ions. These
p o ocols a e gene ally classi ied in o h ee ca ego ies: p oac i e (e.g., DSDV), eac i e (e.g., AODV, DSR), and hyb id (e.g.,
ZRP), each o e ing ade-o s be ween ou ing o e head and esponsi eness [1].
Recen s udies ha e in oduced ad anced mechanisms o e alua e and enhance ou ing e iciency. S a is ical e alua ion
amewo ks ha e been employed o igo ously compa e ou ing p o ocols unde a ious scena ios, using es s like K uskal-Wallis
and F iedman o in e p e pe o mance me ics such as packe deli e y a io (PDR), h oughpu , and end- o-end delay [1].
Meanwhile, delay-awa e models and us -based mechanisms ha e been p oposed o mi iga e eal- ime issues such as ansmission
delays and malicious node beha io [3], [5]. Ene gy-e icien ou ing p o ocols using op imiza ion algo i hms and c oss-laye
designs u he a emp o p olong ne wo k li e ime and enhance eliabili y [6], [7]. The in eg a ion o eme ging echnologies such
as A i icial In elligence (AI), Rein o cemen Lea ning (RL), and Machine Lea ning (ML) has in oduced in elligen adap i e
ou ing p o ocols capable o sel -lea ning and secu i y enhancemen [2], [8], [9]. Fo ins ance, hyb id AdaBoos -Random Fo es
algo i hms and neu al ne wo k-based classi ie s ha e been success ully applied o de ec a acks like blackhole and looding,
he eby imp o ing secu i y and us wo hiness [5], [9].
Despi e hese ad ancemen s, selec ing an app op ia e ou ing p o ocol o a speci ic MANET scena io emains non- i ial. The
pe o mance o ou ing p o ocols is highly dependen on ne wo k pa ame e s such as node mobili y, a ic pa e ns, ene gy
cons ain s, and po en ial secu i y h ea s. The e o e, a comp ehensi e analysis o p o ocol pe o mance unde a ying ne wo k
con igu a ions is necessa y o guide p o ocol selec ion and op imiza ion. This pape p esen s a de ailed pe o mance analysis o
a ious MANET ou ing p o ocols unde mul iple pe o mance me ics.
In e na ional Jou nal o Eme ging Resea ch in Science, Enginee ing, and Managemen
Vol. 1, Issue 2, pp.01-06, Augus 2025.
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IJERSEM@2025 h ps://doi.o g/10.58482/ije sem. 1i2.1 2
The objec i e is o assess hei e iciency in e ms o h oughpu , delay, packe deli e y a io, ene gy consump ion, and ou ing
o e head, he eby o e ing insigh s in o hei sui abili y o di e en applica ion con ex s.
2 RELATED WORK
2.1 S a is ical and Compa a i e E alua ion o Rou ing P o ocols
A ounda ional aspec o ou ing p o ocol analysis in MANETs in ol es s a is ical e alua ion o objec i ely measu e
pe o mance unde di e se ne wo k condi ions. Alame i e al. [1] p esen ed a sophis ica ed s a is ical me hodology o e alua e
widely used ou ing p o ocols including DSDV, AODV, DSR, and ZRP. By employing non-pa ame ic es s such as K uskal-
Wallis, Mann-Whi ney, and F iedman, he s udy p o ided a obus compa a i e assessmen based on Quali y o Se ice (QoS)
me ics like packe deli e y a io (PDR), h oughpu , and end- o-end delay. The wo k emphasized how p o ocol pe o mance
a ies wi h node densi y and mobili y pa e ns, aiding in s a egic p o ocol selec ion.
2.2 Delay Op imiza ion and Oppo unis ic Rou ing
Delay-awa e ou ing is c i ical in MANETs due o he high p obabili y o in e mi en connec i i y. Pushpala ha e al. [3]
p oposed a Delay-Awa e Es ima ed T ansmission Ra e (DA-ETR) model o oppo unis ic ou ing in dynamic en i onmen s. The
model minimized delays caused by packe e ansmissions and mobili y, while enhancing h oughpu and ou ing s abili y.
Implemen ed in MATLAB, DA-ETR demons a ed supe io pe o mance o e adi ional ETR-based me hods wi h educed
communica ion o e head and imp o ed scalabili y.
2.3 Secu i y and T us -Awa e Rou ing
Rou ing p o ocols in MANETs a e highly suscep ible o secu i y h ea s such as sinkhole, blackhole, and wo mhole a acks.
Vincen and Du aipandian [5] add essed hese issues by in eg a ing a hyb id AdaBoos -Random Fo es algo i hm wi h AODV,
e ec i ely de ec ing and mi iga ing sinkhole a acks. Simila ly, Sha i e al. [9] p oposed ML-AODV, a machine lea ning and us -
based p o ocol ha uses us es ima ion me ics like hop coun , esidual ene gy, and link expi a ion ime o selec eliable elay
nodes. By inco po a ing an SVM classi ie , hei me hod imp o ed in usion de ec ion accu acy and educed delay, ou ing
o e head, and packe loss. L. H. Binh and T.-V. T. Duong [4] ex ended his app oach by in oducing TC-AODV, a us -cen ic
ou ing p o ocol capable o de ec ing mul iple a ack ypes, including session hijacking and packe d op. Thei wo k highligh ed
he need o a ack- esilien ou ing p o ocols, especially in sel -o ganizing mobile ne wo ks.
2.4 Ene gy-E icien and C oss-Laye Rou ing
Ene gy consump ion emains a c i ical bo leneck in MANETs, especially o ba e y-ope a ed nodes. Shanmugham e al. [7]
p oposed a sel -a en ion-based c oss-laye design using condi ional a ia ional au o-encode s and gene a i e ad e sa ial ne wo ks
(SACVAEGAN-MCLD-MANET). By inco po a ing MAC-laye bi-objec i e clus e ing and ne wo k-laye me ics, he me hod
achie ed no able imp o emen s in PDR and delay o e exis ing c oss-laye echniques. De i e al. [6] also add essed ene gy
e iciency by in oducing a hyb id Whale-Flowe Pollina ion Algo i hm (WP-FPA) and us e alua ion ia Agg ega ed Packe
Con ol T us P o ocol (APCTP), esul ing in imp o ed esidual ene gy and p olonged ne wo k li e ime.
2.5 AI and Lea ning-Based Rou ing Enhancemen s
The in eg a ion o a i icial in elligence in o MANET ou ing has shown p omising esul s in adap ing o dynamic opologies
and op imizing pa h selec ion. Binh and Duong [4] u ilized ein o cemen lea ning o enhance he AODV p o ocol o 5G-based
MANETs. Thei app oach allowed nodes o dynamically upda e s a e in o ma ion and iden i y QoS-gua an eed pa hs, imp o ing
h oughpu and signal- o-noise a io (SNR). Alhussen and Ansa i [8] applied AI o eal- ime a ic p edic ion using a Chao ic
Spa ial Fuzzy Polynomial Neu al Ne wo k (CSFPNN) wi hin MANETs. The sys em enabled p oac i e ou e op imiza ion,
enhancing u ban mobili y and educing conges ion h ough dynamic decision-making.
2.6 Clus e ing and Swa m In elligence-Based Rou ing
Op imized clus e ing mechanisms help manage node densi y and imp o e ou ing eliabili y. Ni malade i and P abha [10]
p oposed SN-TOCRP, a us -awa e clus e ing p o ocol using a uzzy-based c ow sea ch algo i hm o clus e head selec ion. Thei
app oach isola ed sel ish and misbeha ing nodes using au hen ica ion and us es ima ion, leading o imp o emen s in packe
deli e y, h oughpu , and ene gy e iciency. Swa m in elligence has also gained ac ion as a ou ing op imiza ion me hod. Pa il
and Bo ka [11] in es iga ed swa m in elligence algo i hms o ou e disco e y unde mobili y, ene gy, and packe size cons ain s.
Thei wo k demons a ed he easibili y o bio-inspi ed op imiza ion o obus , adap i e ou ing in MANETs.
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3 METHODOLOGY
To sys ema ically e alua e he pe o mance o ou ing p o ocols in Mobile Ad Hoc Ne wo ks (MANETs), a simula ion-based
expe imen al se up was designed. This sec ion de ails he simula ion en i onmen , he selec ed ou ing p o ocols, pe o mance
me ics, and he scena ios unde which each p o ocol was assessed.
3.1 Simula ion En i onmen
The simula ions we e ca ied ou using Ne wo k Simula o NS-2.35, a widely accep ed open-sou ce ool o modeling and
e alua ing wi eless ne wo k beha io . NS-2 p o ides de ailed suppo o MANET ou ing p o ocols and o e s lexibili y in
cus omizing mobili y, a ic, and ene gy models. Some addi ional expe imen s om e iewed models we e also e e enced om
MATLAB [3] and Ri e bed Modele 17.5 [2] o align pe o mance expec a ions. The gene al simula ion pa ame e s a e lis ed in
Table 1.
Table 1. De ails o Simula ion En i onmen
Pa ame e
Value
Simula o
NS-2.35
Simula ion Time
200 seconds
Numbe o Nodes
20, 40, 60, 80, 100 ( a ied)
A ea Size
1000 m × 1000 m
Mobili y Model
Random Waypoin
Node Speed
1–20 m/s
Pause Time
0, 10, 20, 30, 50 s
T a ic Type
CBR (UDP)
Packe Size
512 by es
T ansmission Range
250 m
MAC P o ocol
IEEE 802.11
An enna Type
Omni-di ec ional
In e ace Queue Type
D opTail/P iQueue
3.2 Rou ing P o ocols Selec ed
Based on he li e a u e su ey and classi ica ion in Sec ion 2, he ollowing ou ing p o ocols we e selec ed o pe o mance
compa ison:
• AODV (Ad hoc On-Demand Dis ance Vec o ) – A eac i e p o ocol known o low o e head and quick ou e
disco e y [1], [5], [9].
• DSR (Dynamic Sou ce Rou ing) – Ano he eac i e p o ocol using sou ce ou ing o pa h in o ma ion [1].
• DSDV (Des ina ion-Sequenced Dis ance Vec o ) – A p oac i e p o ocol main aining pe iodic ou ing ables [1].
• OLSR (Op imized Link S a e Rou ing) – A p oac i e link-s a e p o ocol o e ing educed o e head ia mul ipoin
elays [2].
• ZRP (Zone Rou ing P o ocol) – A hyb id p o ocol combining p oac i e and eac i e ea u es [1].
3.3 Pe o mance Me ics
The e alua ion ocuses on key Quali y o Se ice (QoS) me ics ele an o MANET en i onmen s:
• Packe Deli e y Ra io (PDR): The a io o success ully deli e ed packe s o he o al numbe o packe s sen . A
highe PDR indica es be e eliabili y.
• End- o-End Delay: The a e age ime a packe akes o a el om sou ce o des ina ion. Lowe delay is p e e able
o eal- ime applica ions.
• Th oughpu : The o al da a success ully deli e ed o e he simula ion ime, measu ed in Kbps.
• Rou ing O e head: The numbe o con ol packe s ansmi ed du ing ou e disco e y and main enance.
• Ene gy Consump ion: To al ene gy consumed by nodes du ing simula ion, especially impo an in ba e y-limi ed
en i onmen s.
• Packe Loss Ra io (PLR): Pe cen age o packe s los du ing ansmission, indica ing he eliabili y o he ou ing
p o ocol.
3.4 E alua ion Scena ios
The ou ing p o ocols we e e alua ed unde a ying:
• Node densi ies: 20 o 100 nodes.
• Mobili y le els: Adjus ed ia node speed and pause ime.
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• T a ic loads: By changing he numbe o CBR connec ions (5, 10, 20).
Each simula ion was un i e imes wi h di e en andom seeds, and a e age alues we e compu ed o ensu e s a is ical
alidi y. Fu he s a is ical es s such as K uskal-Wallis and F iedman es s, as sugges ed in [1], may be applied o alida e he
signi icance o he di e ences in pe o mance me ics ac oss p o ocols.
4 RESULTS AND DISCUSSION
This sec ion discusses he simula ion esul s o i e selec ed ou ing p o ocols—AODV, DSR, DSDV, OLSR, and ZRP—
e alua ed unde a ying ne wo k condi ions. The pe o mance me ics conside ed include Packe Deli e y Ra io (PDR), End- o-
End Delay, Th oughpu , Rou ing O e head, Packe Loss Ra io (PLR), and Ene gy Consump ion. Each esul e lec s an a e age
o mul iple simula ion uns o ensu e consis ency and s a is ical eliabili y.
4.1 Packe Deli e y Ra io (PDR)
The Packe Deli e y Ra io is a key measu e o eliabili y. AODV and OLSR consis en ly demons a ed highe PDR alues
ac oss di e en node densi ies and mobili y le els. In pa icula , AODV main ained o e 95% deli e y e en unde high mobili y,
suppo ed by i s on-demand ou e disco e y [1], [9]. DSDV, being p oac i e, su e ed unde dynamic opologies due o ou da ed
ou ing in o ma ion, esul ing in a no iceable d op in PDR as mobili y inc eased. ZRP exhibi ed mode a e pe o mance by
balancing eac i e and p oac i e ou ing bu s uggled sligh ly in e y spa se o highly mobile ne wo ks.
4.2 End- o-End Delay
OLSR and DSDV exhibi ed he lowes a e age delays due o he a ailabili y o p ecompu ed ou es [1], [2]. Howe e , his
ad an age comes a he cos o inc eased con ol o e head. DSR had highe delay due o ou e cache lookups and sou ce ou ing
o e head. AODV, hough eac i e, main ained accep able delays unde a ying loads, demons a ing i s obus ness. ZRP showed
s able delays due o localized p oac i e ou ing, hough ou e disco e y beyond he zone added delay in some scena ios.
4.3 Th oughpu
Th oughpu ends closely mi o ed PDR. AODV and OLSR achie ed highe h oughpu , especially unde low pause ime and
high a ic condi ions, con i ming hei adap abili y o apid opological changes. DSDV and DSR lagged behind unde high
mobili y. ZRP achie ed balanced h oughpu bu showed pe o mance deg ada ion when zones we e ei he oo la ge o oo small.
4.4 Rou ing O e head
DSDV and OLSR incu ed he highes ou ing o e head due o hei p oac i e na u e and pe iodic con ol message exchange
[1], [2]. DSR showed compa a i ely lowe o e head because o i s sou ce ou ing, while AODV main ained mode a e o e head
wi h dynamic ou e upda es. ZRP balanced o e head well by limi ing p oac i e ou ing o local zones. This makes ZRP mo e
bandwid h-e icien han ully p oac i e p o ocols bu no as ligh weigh as AODV o DSR unde spa se condi ions.
4.5 Packe Loss Ra io (PLR)
As expec ed, PLR was lowes o AODV and OLSR, bo h o which main ained s able connec ions and quick eco e y
mechanisms. DSDV had a highe packe loss, pa icula ly a highe speeds and lowe pause imes, due o equen link b eakages
and s ale ou e usage. DSR’s ou e caching mechanism con ibu ed o ou da ed pa hs, leading o occasional packe d ops. ZRP
exhibi ed a iable pe o mance based on zone size and node mobili y.
4.6 Ene gy Consump ion
Ene gy e iciency is c i ical in MANETs, especially o ba e y-cons ained nodes. DSR and ZRP pe o med be e in e ms o
ene gy consump ion due o educed con ol packe ansmission [7], [11]. OLSR and DSDV, due o hei p oac i e mechanisms,
consumed mo e ene gy e en when he ne wo k was idle. AODV showed mode a e ene gy usage, s iking a balance be ween
pe o mance and con ol o e head. In highly mobile en i onmen s, eac i e p o ocols we e mo e ene gy-e icien due o on-
demand ou ing. The esul s align wi h p io indings in [1], [3], [6], [2], and [9], a i ming ha no single p o ocol domina es ac oss
all pa ame e s. The choice o ou ing p o ocol should he e o e be based on speci ic applica ion equi emen s—AODV o gene al-
pu pose dynamic ne wo ks, OLSR o low-delay and s a ic en i onmen s, DSR o low-ene gy applica ions, and ZRP o
mode a e-scale ne wo ks whe e adap abili y and e iciency a e bo h desi ed. A compa ison o he p o ocols is gi en in Table 2.
In e na ional Jou nal o Eme ging Resea ch in Science, Enginee ing, and Managemen
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Table 2. Pe o mance Analysis
P o ocol
PDR
Delay
Th oughpu
O e head
PLR
Ene gy Use
AODV
High
Medium
High
Medium
Low
Medium
DSR
Medium
High
Medium
Low
Medium
Low
DSDV
Low
Low
Low
High
High
High
OLSR
High
Low
High
High
Low
High
ZRP
Medium
Medium
Medium
Medium
Medium
Low
5 CONCLUSIONS
This pape p esen ed a comp ehensi e pe o mance analysis o i e widely adop ed ou ing p o ocols in Mobile Ad Hoc
Ne wo ks (MANETs): AODV, DSR, DSDV, OLSR, and ZRP. Th ough ex ensi e simula ions conduc ed unde a ying ne wo k
scena ios, including di e en node densi ies, mobili y le els, and a ic loads, he s udy e alua ed each p o ocol based on c i ical
Quali y o Se ice (QoS) me ics such as packe deli e y a io (PDR), end- o-end delay, h oughpu , ou ing o e head, packe loss
a io (PLR), and ene gy consump ion. The esul s indica e ha no single p o ocol pe o ms op imally ac oss all scena ios. AODV
consis en ly exhibi ed high deli e y a ios and h oughpu , making i a s ong candida e o highly dynamic ne wo ks. OLSR,
owing o i s p oac i e na u e, achie ed he lowes delay bu su e ed om highe con ol o e head and ene gy consump ion. DSR,
while ene gy-e icien and ligh weigh in o e head, s uggled wi h delay unde inc eased ne wo k dynamics due o ou e cache
s aleness. DSDV’s pe o mance deg aded signi ican ly unde mobili y due o equen link b eakages and ou da ed ou ing
in o ma ion. ZRP, as a hyb id p o ocol, o e ed balanced pe o mance bu was sensi i e o zone adius con igu a ion, which
a ec ed bo h o e head and la ency.
The indings a i m ha ou ing p o ocol selec ion in MANETs should be guided by applica ion-speci ic cons ain s and
ne wo k dynamics. Fo ime-c i ical applica ions wi h mode a e node mobili y, OLSR is sui able. In con as , AODV is mo e
obus in high-mobili y and high- a ic condi ions. Ene gy-sensi i e applica ions may bene i om using DSR o op imized ZRP
a ian s. Fu he mo e, he s udy highligh s he g owing ele ance o AI-enhanced and us -awa e ou ing mechanisms o u u e
MANET deploymen s, as discussed in he ela ed wo k. Fu u e wo k may include ex ending his analysis o inco po a e ecen
ad ancemen s in AI-based ou ing, us managemen amewo ks, and secu i y-awa e ou ing p o ocols o add ess ulne abili ies
such as blackhole, sinkhole, and jamming a acks. Mo eo e , p o ocol pe o mance unde eal-wo ld cons ain s such as
he e ogeneous de ices, limi ed ba e y capaci y, and mobili y pa e ns om ac ual deploymen s emains an a ea o u he
explo a ion.
FUNDING INFORMATION
This esea ch ecei ed no speci ic g an om any unding agency in he public, comme cial, o no - o -p o i sec o s.
ETHICS STATEMENT
This s udy did no in ol e human o animal subjec s and, he e o e, did no equi e e hical app o al.
STATEMENT OF CONFLICT OF INTERESTS
The au ho s decla e no con lic s o in e es ela ed o his s udy.
LICENSING
This wo k is licensed unde a C ea i e Commons A ibu ion 4.0 In e na ional License.
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