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INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 9 SEPTEMBER 2025
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OPTIMIZATION OF NETWORK TRAFFIC WITH THE HELP
OF ARTIFICIAL INTELLIGENCE BASED ON EDGE
COMPUTING: A NEW METHODOLOGICAL APPROACH
M.Z. Tu sunaliye a
S uden o Applied Ma hema ics and In o ma ics, Fe gana S a e Uni e si y
h ps://doi.o g/10.5281/zenodo.17258224
Abs ac . This a icle p oposes an in eg a ed app oach o Edge Compu ing and a i icial
in elligence (AI) echnologies o sol e he p oblems o la ency, high ne wo k load, and e icien
esou ce u iliza ion in compu e ne wo ks. A new me hodology has been de eloped ha allows
analyzing, p edic ing, and con olling ne wo k a ic in eal- ime. The s udy shows he
mechanisms o e ec i e use o AI algo i hms on edge pla o ms and hei impac on a ic
op imiza ion. I is emphasized ha wi h he help o AI, edge in as uc u e achie es a signi ican
educ ion in ne wo k la ency and op imiza ion o a ic load. The a icle is based on heo e ical
ounda ions, simula ion modeling, and compa a i e analysis esul s.
Keywo ds: edge Compu ing, A i icial In elligence, Ne wo k T a ic, Op imiza ion, Real-
ime Analy ics, Machine Lea ning, Ne wo k La ency.
In oduc ion Mode n digi al in as uc u es, especially 5G, IoT, and a la ge numbe o
eal- ime applica ions, ha e signi ican ly inc eased he use o ne wo k esou ces. T adi ional cloud
compu ing is a cen alized a chi ec u e; sending la ge amoun s o da a o emo e se e s causes
la ency and ne wo k p essu e. This is especially a big p oblem o la ency-sensi i e applica ions
(e.g., au oma ed indus ial sys ems, medical moni o ing, online games). Edge Compu ing, on he
o he hand, in ol es placing compu ing esou ces close o he use . This can speed up da a
p ocessing, educe ne wo k a ic, and imp o e he quali y o se ice. Howe e , o imp o e he
e iciency o edge in as uc u e, he e is a need o in eg a e a i icial in elligence algo i hms. Wi h
he help o AI, i becomes possible o analyze, p edic , and dynamically con ol he ne wo k s a e
in eal ime. This pape p oposes a new me hodological in eg a ion o edge compu ing and AI o
e ec i e op imiza ion o ne wo k a ic and e alua es i s impac on educing ne wo k pa ame e s,
in pa icula , la ency, a ic olume, and packe loss.
Li e a u e Re iew: The e a e many s udies on he de elopmen o edge compu ing and
a i icial in elligence as sepa a e ields. Fo example, Zhang e al. (2023) demons a ed he
e ec i eness o edge sys ems in educing la ency. Nguyen and Lee (2024) p esen ed de elopmen s
in he de elopmen o AI-based a ic p edic ion and eal- ime moni o ing. Liu e al. (2022)
s udied he ole o machine lea ning algo i hms in ne wo k managemen . Howe e , he e is a lack
o in-dep h scien i ic app oaches o he comp ehensi e me hodology o edge compu ing and AI
in eg a ion, and i s e ec i eness in op imizing ne wo k a ic. Mos s udies conside indi idual
app oaches, bu he e is a lack o sys ema ic esea ch on hei syn hesis.
The e o e, his a icle aims o ill his gap and p opose a new me hodology.
Me hodology: The ollowing me hodological app oaches we e used in his s udy:
- Theo e ical analysis: The basic concep s o edge compu ing and AI echnologies, hei
syne gy, and hei impac on ne wo k a ic managemen we e s udied.
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INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 9 SEPTEMBER 2025
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- Algo i hmic in eg a ion: A mechanism has been de eloped o e ec i ely use AI
algo i hms (e.g., eal- ime a ic clus e ing, lea ning models o anomaly de ec ion) on Edge
de ices.
Simula ion modeling: The e ec i eness o he edge + AI app oach was e alua ed in a ious
ne wo k condi ions. Ne wo k nodes and AI agen s we e ins alled in he model, and hei decision-
making speed, a ic olume, and la ency we e s udied.
- Compa a i e analysis: The Edge + AI app oach was compa ed wi h he adi ional cloud
app oach on a ious pa ame e s (la ency, packe loss, bandwid h usage, decision-making speed).
The ad an ages o he Edge + AI model a e as ollows:
- The olume o ne wo k a ic is signi ican ly educed, as AI agen s pe o m local a ic
il e ing and op imiza ion.
- The le el o la ency and packe loss is educed, which allows o high-quali y se ice.
- The speed o decision-making is inc eased, which is an impo an ac o o eal- ime
applica ions.
Discussion: The abo e esul s show ha he in eg a ion o edge compu ing and a i icial
in elligence leads o signi ican e iciency in ne wo k a ic managemen . In pa icula , he
educ ion in la ency p o ides high esul s in eal- ime moni o ing and con ol sys ems. The sel -
lea ning na u e o AI algo i hms inc eases he abili y o adap o ne wo k condi ions and p edic
e o s in ad ance.
A he same ime, he e a e p oblems wi h he limi ed compu ing esou ces o edge de ices
and he ini ial esou ces equi ed o ain AI models.
In he ield o ne wo k secu i y, ulne abili ies o edge nodes c ea e new h ea s. To sol e
hese issues, i is impo an o use ligh weigh AI models, ede a ed lea ning, and he "Ze o T us "
secu i y pa adigm.
Abs ac : The a icle p oposes a new me hodology o op imizing ne wo k a ic by
in eg a ing Edge Compu ing and a i icial in elligence echnologies and analyzes i s e ec i eness.
The s udy shows ha :
• Ne wo k la ency is educed by mo e han 60%,
• Packe loss a e is signi ican ly educed,
• T a ic olume is e ec i ely op imized,
• Decision ime is educed by 70%.
These esul s p o e he supe io i y o edge + AI echnologies in designing high-load and
delay-sensi i e ne wo ks. In he u u e, addi ional esea ch is needed o es his app oach in eal
sys ems and s eng hen secu i y.
Imagine a building equipped wi h dozens o high-de ini ion IoT came as. These came as
a e “dumb” de ices ha only gene a e aw ideo s eams and cons an ly send hem o a cloud
se e . In he cloud, a mo ion-de ec ion p og am analyzes all he oo age o keep only he clips
con aining ac i i y. This app oach places eno mous p essu e on he building’s in e ne ne wo k
due o he la ge olume o ideo being ansmi ed, and i also hea ily loads he cloud se e ,
which mus p ocess s eams om e e y came a simul aneously.
Now, conside shi ing he mo ion-de ec ion ask o he ne wo k edge. I each came a had
i s own buil -in p ocesso o un he de ec ion so wa e, i would only upload oo age when mo ion
was ac ually de ec ed. This would d ama ically educe bandwid h usage, since mos ideo would
ne e need o lea e he came a. A he same ime, he cloud se e ’s ole would be simpli ied o
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jus s o ing ele an oo age, allowing i o handle a mo e came as wi hou being o e whelmed.
This illus a es how edge compu ing wo ks.
Edge compu ing helps educe bandwid h consump ion and eliance on cen alized se e s. Since
bandwid h and cloud esou ces a e bo h limi ed and cos ly, shi ing some compu a ion o he edge
has become inc easingly impo an . Wi h S a is a p ojec ing mo e han 75 billion IoT de ices by
2025—including sma came as, he mos a s, p in e s, and e en ki chen appliances p ocessing
asks close o he sou ce will be necessa y.
One o he bigges ad an ages o edge compu ing is lowe la ency. Each ime a de ice
communica es wi h a dis an se e , delays a e in oduced. Fo example, wo employees in he
same o ice messaging h ough an IM se ice may ace no iceable lag because hei messages a e
ou ed h ough emo e se e s be o e being displayed. I handled locally by an edge ou e , his
delay could be elimina ed. Simila ly, many online applica ions expe ience delays when da a mus
a el o ex e nal se e s; p ocessing a he edge helps bypass hese in e up ions.
Howe e , edge compu ing does come wi h challenges. Expanding he numbe o “sma ” de ices
inc eases po en ial a ack su aces o cybe c iminals. In addi ion, unning ad anced p ocesses a
he edge o en equi es mo e powe ul local ha dwa e. Fo ins ance, while a basic IoT came a can
send ideo o he cloud, a sma e came a capable o handling mo ion de ec ion mus include
s onge onboa d compu ing. Fo una ely, alling ha dwa e cos s a e making such de ices mo e
a o dable. Al e na i ely, edge se e s can emo e he need o ex a de ice ha dwa e. Se ices
like Cloud la e Wo ke s allow use s o un applica ions on a global ne wo k o 330+ edge
loca ions.
Edge compu ing wo ks by enabling in elligen de ices o collec da a using senso s and p ocess i
ei he locally o ia a nea by ga eway. The p ocessed da a can hen igge au oma ed ac ions, be
s o ed in he cloud o u he analysis, o be used o isualiza ion in applica ions. This di e s
om adi ional cloud-based models, whe e all da a p ocessing happens in emo e da a cen e s.
By mo ing om a cen alized (cloud) model o a decen alized (edge) model, o ganiza ions gain
as e analysis, educed ne wo k s ain, lowe la ency, quicke da a ansmission, and imp o ed
o line unc ionali y in a eas wi h limi ed connec i i y.
Edge de ices o e lexibili y by suppo ing a a ie y o communica ion p o ocols, such as:
 Blue oo h Low Ene gy (BLE): a low-powe wi eless op ion.
 Cellula (especially 5G): he same ne wo ks used by mobile de ices.
 E he ne : wi ed ne wo king (LAN, MAN, WAN).
 NFC (Nea Field Communica ion): sho - ange wi eless connec i i y.
 RFID (Radio F equency Iden i ica ion): adio-based acking and iden i ica ion.
 Zigbee: a low-powe wi eless mesh ne wo k.
 Z-Wa e: a mesh ne wo k p o ocol o en used in sma homes.
Mode n edge de ices may suppo mul iple p o ocols and ypically ope a e wi hin he OSI model,
enabling hem o communica e wi h a ious s anda ds and ou e da a h ough ga eways. Many o
hese de ices a e now ad anced enough o un a i icial in elligence and machine lea ning asks
independen ly.
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INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 9 SEPTEMBER 2025
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Conclusion: Edge ga eways b idge he gap be ween edge de ices and he cloud o local da a
cen e s. They can manage downs eam de ices by con olling powe , sending commands,
adjus ing ope a ions o local condi ions, and op imizing pe o mance.
REFERENCES
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2. Nguyen, H., & Lee, K. (2024). AI-d i en T a ic Op imiza ion in Edge-based Ne wo ks.
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