PERFORMANCE ANALYSIS OF OLSR PROTOCOL IN MANET CONSIDERING DIFFERENT MOBILITY SPEED AND NETWORK DENSITY

International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.6, December 2021
DOI:10.5121/ijwmn.2021.13602 21
PERFORMANCE ANALYSIS OF OLSR PROTOCOL IN
MANET CONSIDERING DIFFERENT MOBILITY
SPEED AND NETWORK DENSITY
Koay Yong Cett, Nor Aida Mahiddin*, Fatin Fazain Mohd Affandi,
Raja Hasyifah Raja Bongsu, Aznida Hayati
Faculty of Informatics and Computing,
Universiti Sultan Zainal Abidin (UniSZA) Terengganu, Malaysia
ABSTRACT
A Mobile Ad Hoc Network (MANET) is created when an independent mobile node network is connected
dynamically via wireless links. MANET is a self-organizing network that does not rely on pre-existing
infrastructure such as wired or wireless network routers. Mobile nodes in this network move randomly,
thus, the topology is always changing. Routing protocols in MANET are critical in ensuring dependable
and consistent connectivity between the mobile nodes. They conclude logically based on the interaction
between mobile nodes in MANET routing and encourage them to choose the optimum path between source
and destination. Routing protocols are classified as proactive, reactive, or hybrid. The focus of this project
will be on Optimized Link State Routing (OLSR) protocol, a proactive routing technique. OLSR is known as
the optimized variant of link state routing in which packets are sent throughout the network using the
multipoint relay (MPR) mechanism. This article evaluates the performance of the OLSR routing protocol
under condition of changing mobility speed and network density. The study's performance indicators are
average packet throughput, packet delivery ratio (PDR), and average packet latency. Network Simulator 2
(NS-2) and an external patch UM-OLSR are used to simulate and evaluate the performance of such
protocol. As a result of research, the approach of implementing the MPR mechanism are able to minimise
redundant data transmission during the normal message broadcast. The MPRs enhance the link state
protocols’ traditional diffusion mechanism by selecting the right MPRs. Hence, the number of undesired
broadcasts can be reduced and limited. Further research will focus on different scenario and environment
using different mobility model.
KEYWORDS
MANET, OLSR, Node Mobility, Density, Routing Scheme
1. INTRODUCTION
MANET is a wireless network that gaining a great attention due to being self-organizes, self-
configures and self-repairing [1]. It is easily created by an autonomous system of wirelessly
connected mobile nodes and can be deployed even without assistance of external infrastructure,
such as a centralised base station (BS) or access point (AP). It is a fixed in the short that may be
set up at any moment and in any location as a reserve in the scenario where
physical infrastructure gets insufficient. For instance, MANET was chosen as the primary
network to be used during disaster area where existing and physical infrastructure may have been
destroyed, resulting in a massive breakdown in communication. Historical evidence demonstrates
that the communication demand increases dramatically, which later leads to a major occurrence.
The improvement in the wireless communication plays a crucial role especially in emergency and
search and rescue (SAR) operations due to improvement in end user hardware such as mobile

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devices and other electronic devices [1]. To accommodate a variety of SAR operations during the
disaster area scenario, the MANET may be efficiently altered, minimizing loss of life and
property [2]. Due to the fact that MANET is a self-configuring, self-repairing, and self-
recovering network, it is easy to cope even in the absence of the established infrastructure [3],
[4]. The communication network may be resolved quickly, minimizing delays and errors during
the SAR operations. Thus, the MANET can be regarded as a pragmatic solution for conducting
the SAR operations during disaster area because of its quick deployment, which enables rescue
teams to react quickly to victims' calls [5], [6].
MANETs consist of multiple nodes particularly electronic devices such as tablets, smartphones,
and video cameras. All of this equipment is connected via the wireless network and may
communicate with each other. All mobile nodes are free to join or leave the network at any
moment. The mobile nodes can move freely and be self-organized. As a result, the network's
topology might undergo rapid and unpredictable changes. Additionally, each of the network
nodes can act as a recipient, transmitter, or intermediate node that acts as a router,
which transmitting data to and from other mobile nodes. In the real-world scenarios, the nodes
are very mobile and relies on batteries to operate, considering the MANET applications [7].
Routing in MANET is based on a simple approach that allows each of the nodes to re-emit the
data message for the ease of propagation across the network from a source to its precise
destination. The critical issue in routing protocol design is determining their optimal path to the
destination. These protocols assist the nodes in determining the ideal path for packets to go
through the network. It is also used to develop and maintain an updated routing information that
enables the nodes to determine their ideal route for the communication between the source and
destination node. A few routing techniques have been proposed to address the issues with high
mobile nodes where topology changed frequently [8].
The paper is organized as follows: Section 2 explains the classification of routing protocols. In
Section 3, explains related works involving routing protocols in measuring the network
performance of MANET. Section 4 describes proposed approach in routing mechanism
considering OLSR routing protocol. Section 5 describes the network parameters used for the
simulation. Section 6 presents the results and analysis of the proposed approach. Finally,
conclusion is drawn in Section 7.
2. CLASSIFICATION OF ROUTING PROTOCOLS
Ad hoc routing protocol is a commonly used protocol referring to the protocol that aid in
determining the paths to be used for routing packets between the source and destination of
the nodes in the MANET environment. The role of routing protocols is to maintain routing tables
containing all available routing information and preserve a router’s table dynamically [1]. In
the ad hoc networks, the nodes had limited information regarding their networks' topology. Thus,
the routing can be quite challenging in MANETs due to the restricted resources and random
movement of nodes. As a result, a routing algorithm is presented to resolve the dilemma and
determine the ideal path between the origin and destination nodes.
2.1. Proactive Protocol
Furthermore, it is a table-driven protocol that makes use of mapping tables to preserve the route
and course of each node within the defined network. This provides the routing scheme by
continuously sending topological knowledge packets of data to the network's nodes. As a
consequence, each node's routing information is regularly updated leveraging the updated

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topological information. When nodes are added or removed from the network, control messages
are sent to neighbouring nodes and routing tables are modified. Typically, this network protocol
makes extensive use of link state techniques to disseminate information about its network
neighbours [9].
2.2. Reactive Protocol
On-demand protocols are so named because they initiate a path discovery procedure once the
source node has network packets to disseminate to the destination node. Therefore, determining
the shortest path between the communication nodes. Once the path is created, it will be retained
until the pathway is no longer permitted or until the packets of information reach the target. A
series of activities, including the usage of a sequence number, have been taken to retain the new
route and avoid repetition.
2.3. Hybrid Protocol
It is a hybrid of proactive and reactive protocol techniques in which the strengths of both prior
protocols are merged to create an immediate reactive neighbourhood linked to proactive
connections up to a particular distance. If an application wishes to transfer packets to a node apart
from this zone, a reactive evaluation is performed. This instantly makes the pathways within a
node's coverage area available. Initially, proactive routing protocols are implemented using
routing tables in association with root nodes. If the node learns that it lacks knowledge on the
path to the destination of the source nodes, it will perform route discovery using the reactive
routing scheme [10].
3. RELATED WORKS
Muthana Najim Abdulleh and Salman Yussof conduct a comparative evaluation of the routing
protocol, considering the specific number of nodes and size of simulation area [11]. Throughput,
average end-to-end (E2E) delay, packet delivery fraction (PDF), and normalized routing load
(NRL) were used to evaluate the performance of the routing protocols. The simulation is carried
out for two scenarios, with differences in simulation parameters studied between the two
scenarios. The range number of the nodes in the first scenario was limited between 30 until 150.
In second scenario, they focus to the network density and scale the map from 500 to 1750 metres.
The simulation results indicate that Greedy Parameter Stateless Routing (GPSR) outperforms
OLSR and Ad hoc On-Demand Distance Vector (AODV) routing protocol in the majority of
tests. Moreover, the simulation results indicate that increasing in terms of number of the nodes
has a substantial effect on the NRL, whereas changing the map area of the simulated region has a
considerable effect on throughput, average E2E delay, and PDF.
K.Natarajan and G.Mahadevan [12] perform an analysis on how the mobility speed can affect the
performance of network routing protocols. The selected routing protocols are AODV, Destination
Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), Location-Aided Routing
(LAR), OLSR, Fisheye State Routing (FSR) and Zone Routing Protocol (ZRP). The network
stimulation is divided into three distinct scenarios: low mobility, medium mobility, and high
mobility. The simulation results indicate that LAR and AODV perform better than other
protocols, reliably transmitting around 50% to 60% of data packets independent of speed.
Additionally, the DSR protocol exhibits a delay of 53% and 95% greater than the AODV and
DSDV procedures, respectively.

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Moreover, Lakshman Naik L, R. U. Khan, and R. B. Mishra [13] examined the effectiveness of
different ad hoc routing protocols by varying the speed of nodes. The aim of the paper is to
analyse the influence of the mobility speed of a node on various routing techniques. The
performance analysis will be conducted on AODV, DSDR, and OLSR routing protocols. They
simulate the network using three different node speeds with a fixed number of source/sink links
of ten. As a result, OLSR protocol has a higher throughput than AODV and DSDV when
the node speed varies. Although OLSR performance degrades slightly as the node speed
increases, it is still superior to AODV and DSDV. Besides, OLSR has a greater packet delivery
ratio than AODV and DSDV. OLSR, on the other hand, degrades slightly as the node speed
increases. In terms of E2E delay, OLSR outperforms both AODV and DSDV. However, when
the node speed increases, OLSR suffers a minor decrease. The packet loss findings indicate that
the OLSR outperforms AODV and DSDV, but the performance drops considerably as the node
speed increases. Finally, they concluded that OLSR outperforms AODV and DSDV across all
the parameters they considered.
Gouri M. Patil, Ajay Kumar, and A. D. Shaligram [14] conducted a performance comparison of
the different ad hoc routing protocols considering the network area size. The OLSR, AODV,
DSR, Gathering-based Routing Protocol (GRP) and Temporally Ordered Routing Algorithm
(TORA) have been examined. The simulation of several routing methods is carried out with three
different sizes of the simulation areas. The findings indicate that the TORA is the optimal choice
when the network load is taking into a concern. For a scalable simulation area up to 2000x2000
square metres, the DSR is the second-best option, followed by AODV, OLSR, and GRP.
However, if the E2E is taking into account as the factor, the GRP performs better when the
network area is up to 1000 x 1000 square metres. For 2000x2000 square metres, the OLSR is the
preferred option. For the throughput, AODV achieves the highest throughput in three scenarios
considering varying the network area sizes.
In [15], D. Kumar and S.C. Gupta investigate the influence of varying the transmission ranges,
the node density, and the nodes speed on OLSR, DSR and ZRP. These three routing protocols
correspond to three types of the routing protocols used in mobile ad hoc networks which
are proactive, reactive, and hybrid routing protocols. The simulation is conducted using three
different scenarios by varying node density, transmission range, and node speed. Based on the
simulation conducted by the authors, it showed that DSR outperforms OLSR and ZRP in terms of
E2E delay and PDR.
Ashutosh Sharma and Rajiv Kumar [16] conducted a performance assessment of a number of
different ad hoc routing methods used in MANET. Different performance indicators such as
average throughput, average PDR, and average delay were used to compare different routing
protocols. The AODV surpasses the other routing protocols in terms of average throughput.
Additionally, the OLSR works optimally in the scenario of average PDR due to OLSR's acyclic
path route selection. Besides, TORA is successful in dense networks since it broadcasts the
information to all the nodes. Finally, DSR delivers the least network delay. It is concluded that
the reactive protocols have a low average delay and a high throughput.
Authors in [17] proposed multi-metric performance of modified OLSR in which they
implemented an energy-aware and link stability approach to the MPR selection. The proposed
approach allowed a longer network lifetime by including the residual energy in the MPR
selection criteria via willingness variable. The simulation is simulation via Network Simulation 3
(NS-3) under different scenarios, varying the number of nodes and mobility pattern. The
simulation results showed a significant loss in terms of End-to-End (E2E) delay for the modified
OLSR.

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According to R. H. R. Bongsu, A. Mohammed, and M. A. Mohamed in [18], Wireless Mesh
Networks (WMNs) have garnered considerable attention and have been thoroughly researched in
a variety of domains such as architecture, implementation, and protocol development. Numerous
organisations have recognised the value of multi-radio multi-channel (MRMC), particularly in
rural networks, battlefields, and natural disasters, where a rapid communication network is
required for deployment. The WMN is made up of mesh nodes that are connected by gateways,
routers, and clients.
Finally, Ako Muhammad Abdullah, Emre Ozen, and Husnu Bayramoglu [19] examined a variety
of mobility models, including Fast Car Model (FCM), the Slow Car Model (SCM), the Race-
Walking Model (RWM), and the Human Walking Model (HWM). The authors developed these
mobility models by varying node speeds in order to evaluate the performance of the AODV,
OLSR, and GRP protocols. Different performance metrics are used to evaluate the network
performance. They concluded from the simulation results that OLSR protocols outperform two
other routing methods. It can be regarded that OLSR protocol is the most appropriate and
effective network routing protocol, allowing for minimal delay and retransmission attempts, as
well as increased data transfer performance from the source to the destination target. Meanwhile,
in all simulation scenarios, AODV protocol outperformed OLSR and GRP in respect of data drop
rate and network load. In comparison to the GRP protocol, the HWM model had a quite higher
AODV network load. Also, the GRP protocol has a substantially lower media access delay and a
data rate than AODV in all simulation scenarios. The findings show that this sort of operation is
critical in deciding the protocol to fully exploit in the network. For example, the OLSR protocol
is highly suited for giving real-time assistance.
4. OPTIMIZED LINK STATE ROUTING (OLSR) PROTOCOL
OLSR is the routing protocol's equivalent implementation for the classical link-state. In
comparison to the distance vector routing protocol, these routing methods avoid routing loops
and have no scalability issues [9]. Due to the flooding method used by conventional connection
state routing protocols, the communication of topological information between the nodes
generates a substantial quantity of traffic. It is an unfavourable characteristic of the MANET as a
result of insufficient resources. The OLSR implements a new method in the network in order to
minimize the traffic volume involved. All OLSR nodes are permitted to receive data packets
including topological information, but only a small number of nodes referred to as MPRs are
permitted to relay messages across the network. MPRs are the smallest number of neighbours that
a node needs to communicate with all of its other neighbours within two hops [13].
The OLSR protocol is built around two main topology management mechanisms: neighbourhood
detection and neighbourhood sensing. The OLSR protocol makes use of both these processes via
four distinct types of control messages: HELLO, TC, MID, and HNA. Also, OLSR protocol does
neighbourhood sensing via HELLO packets [2]. The topological data is transferred by using
optimal dissemination or MPRs to scatter the TC packets. The TC messages are a collection of
links in the neighbourhood of the network nodes that are used to handle the OLSR protocol's
packets [20].
Further, OLSR protocol takes into account all interfaces associated to the network nodes when
transmitting MID messages. As a result, the network's nodes can exploit all available routes
regardless of the type of interfaces utilised at each hop. OLSR node assigns a primary address to
one of its interfaces, which can later be used as a pointer in control messages. Likewise, the
OLSR protocol's HNA messages are used to construct sub networks and hosts that exist outside
of MANET but are discoverable by a node functioning as a gateway [7].

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START
END
X which is the node wants to
compute MPR
N1: X’s 1 Hop Neighborhood
N1: X’s 2 Hop Neighborhood
Delete the nodes of N2 which can be
reached by y (MPR)
Node i in N1,
Willingness i = will always
MPR set of X
Choose the Node N1 with the highest
connectivity as MPR (node y)
N1 Empty or not?
Node in N2 has
symmetric link with N1
Node connected in N2?
Yes
No
Yes
Yes
No
No
Figure 1. Routing selection technique
The fundamental goal of MPRs is to minimise redundant data transmission during the normal
distribution of the message. MPRs are very helpful when it comes to delivering control

27
information over a network. MPRs enhance the link state protocols' traditional diffusion
mechanism. A given node chooses a group of MPRs depending on its information of its two-hop
neighbourhood.
In a MANET, with a dynamically shifting topology, the MPRs had to be re-evaluated anytime the
two-hop neighbour set's experience changed. As a result, the MPRs' position in the
neighbourhood is restricted [18]. The improvement in packet diffusion methods employing the
MPR methodology is illustrated in Figure 1.
In the first part of the figure, the central node distributes control information to eight additional
nodes through the traditional flooding mechanism. The relay approach is illustrated in the
following figure, with the message being carried via four nodes. Thus, by choosing the right
MPRs, it is possible to limit the number of undesired broadcasts. By retaining a variety of data
tables, OLSR protocol functions as a modification of the traditional link-state protocol in
neighborhood sensing. Every time a control information is conveyed or broadcast, the tables are
reset. The nodes save a variety of unique tables in the cache.
Moreover, the HELLO messages have three distinct functions in the OLSR protocol. For link
sensing and neighbor sensing, the messages are conveyed to the neighbor within one hop, and for
two-hop sensing, they are sent to the neighbor within two hops. Finally, it acts as an MPR
selector sensor, indicating the presence of MPRs in the network [7].
Furthermore, the OLSR is referred to as a proactive and table-driven routing system. The routing
loops do not exist in the link state routing protocols. Also, the link state routing techniques are
scalable. However, the link state routing protocol creates a huge quantity of traffic when
the topological data is sent between mobile nodes. Due to the restricted resources available in
the MANET, excessive traffic is an unfavorable characteristic [20]. The OLSR protocol
introduced a novel strategy or technique for significantly reducing the amount of bandwidth
required to exchange the topological data between nodes. The OLSR protocol approves and
permits all nodes to receive the topological data messages.
Nevertheless, only a few MPRs are capable of delivering all of this information throughout the
network. To conclude, a node's MPRs are the fewest immediate neighbors needed to
communicate all its neighbors in two hops. Thus, the MPRs ensure the information messages
linked with the entire network reaches every node.
5. SIMULATION
The simulation of the project is performed by using NS-2. NS-2 is one of the commonly used
network simulator for MANET and Vehicle Ad hoc Network (VANET). NS-2 supports both
wired and wireless networks for routing and multicast protocols. In this study, NS-2 is chosen
instead of other network simulator is due to its being used widely by other researchers and its
simplicity in simulating the network. NS-2 is more practical to simulate the network than
Network Simulator 3 (NS-3) as there are many references that study such protocol. Although NS-
3 is newest than NS-2, however there are limited studies and guidance in simulating the network.
Hence, NS-2 is more practical to be used as the researchers can compare the results with other
studies due to wide range of resources that were using the same simulator but, under different
approaches and metrics. Using the same simulator as other studies can be one of our ways to
ensure that the project being conducted in the right path.
Based on the summarization of the previous work, the simulation parameters for the project are
proposed. The selected simulation area is 1000 m x 1000 m. In addition, the number of the nodes

28
selected in the following project are 20, 40, 60, 80 and 100 nodes respectively. The number of the
nodes above 100 are not selected as the simulation requires too much time to compute in the
current machine used. The mobility speeds are varied into 5 situations which are very slow
mobility (VSM), slow mobility (SM), moderate fast mobility (MFM), fast mobility (FM) and
very fast mobility (VFM). The VSM is stated as 10 m/s, SM is stated as 15 m/s, MFM is stated as
20 m/s and FM is stated as 20 m/s. The simulation time limit is set to 900 s which is the
maximum simulation time in the research papers. The parameters are summarized in Table 1
below.
Table 1. Simulation parameters.
Parameter Specification
Simulation Area (m x m) 1000 x 1000
Mobility Model Random Waypoint
Routing Protocol OLSR
Number of Nodes 20, 40, 60, 80, 100
Speed (m/s) 10, 15, 20, 25, 30
Simulation Time Limit (s) 900
Mac Type 802.11
Traffic CBR
Packet Size (bytes) 512
6. RESULTS
The considered performance metrics of this project are average delay, average throughput and
PDR to evaluate the network performance of the proposed routing technique.
Figure 2. Average delay
As shown in Figure 2, the increasing in delay is due to the increase in number of nodes. FM
illustrates that average delay increased in a gradual speed as the number of nodes increased. In
addition, FM has the highest average delay when it reaches 100 nodes while, VFM has the lowest
average delay when the number of nodes is set at 20 in the simulation. Besides, the average delay
for 100 nodes in the simulation is the highest in all various mobility speeds. Therefore, the
average delay is the lowest when the number of nodes is less.

29
Figure 3. Average throughput.
Figure 3 illustrated the results of throughput obtained in the network simulation. From the results,
we notice that FM that is associated with 40 nodes in the simulation has the highest average
throughput among all others. In addition, VFM has the second highest average throughput when
compared to other simulations. Whereas, SM associated with 80 nodes has the lowest average
throughput. Therefore, the simulation for various mobility speeds with the same number of nodes
do not possess any large differences of average throughput between them. Except for the FM and
VFM that are associated with 40 nodes have a big difference of average throughput when
compared to the VSM, SM and MFM.
Figure 4. Packet delivery ratio.

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In Figure 4, SM has the higher percentage of PDR in all different numbers of nodes except for the
number of nodes that are set to 20. This can be concluded that OLSR has a better packet delivery
ratio when the mobility speed is set to SM when the number of nodes is set above 20. In addition,
the SM with nodes being set to 100 has the highest packet delivery ratio. Whereas, the VFM with
nodes being set to 40 has the lowest packet delivery ratio. The packet delivery ratio for various
mobility speeds has slight differences when the number of nodes is set as 20.
7. CONCLUSIONS
This paper explicates the application and result of OLSR performance by altering the speed of
mobility and the network density. The network simulation for the OLSR protocol is executed and
all the results obtained are recorded. From the result, the average delay graph illustrates an
increasing trend as the number of nodes increases. While, both the packet delivery ratio graph
and the average throughput graph showed not much of a change when the number of nodes
increases. The mobility speed for different nodes does not show a huge difference with the same
number of nodes given in simulation. The performance analysis and evaluation were done based
on the result obtained. This is a crucial aspect of project development. Further study can be
explored by implementing the approach in different scenario such as disaster area scenario with
different type of mobility models and parameters for evaluating the network performance.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGMENTS
This work was supported by the UniSZA/MENTORMENTEE/2018/022[R0046-R004] under the research
of Performance Evaluation of Users Mobility Speed Over Mobile Cloud Computing.
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AUTHORS
Koay Yong Cett received his Bachelor’s degree in Computer Science of Network
Security fromUniversiti Sultan Zainal Abidin, Terengganu, Malaysia in 2021. He was
born in Kuala Lumpur on 5 February 1997. He is currently employed as a Cyber
Security Engineer at LGMS - LE Global Services Sdn Bhd. His research interests
include Network Security and Mobile Ad hoc Networks.
Nor Aida Mahiddin received the B.S. Degree in Information Technology from
National University of Malaysia, the Master's Degree in Computer Science Major in
Distributed Computing and Ph.D. degrees in Computer and Information Sciences from
Auckland University of Technology, New Zealand. Dr. Nor Aida is currently a lecturer
at Faculty of Informatics and Computing, University Sultan Zainal Abidin, Malaysia.
She is the author of several papers in peer-reviewed journals and conferences
proceeding. She is currently a member of the Institute of Electrical and Electronics
Engineers (IEEE), Internet Society and The Society of Digital Information and
Wireless Communications (SDIWC). Her research interests include network design,
modelling and performance evaluation, wireless communication networks, disaster
resilient network design, optimization of gateway congestion control, ad hoc and
sensor networks and wireless mesh and routing protocols.
Fatin Fazain Mohd Affandi is a Computer Science’s student, born and brought up in
Besut, Terengganu, Malaysia. She did her Bachelors of Computer Science, major in
Network Security in year 2019. Her area of interest is in Network and Security so,
currently she is a PG student in the course named "Masters in Computer Science" at
Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia.

32
Raja Hasyifah Raja Bongsu received BS degree in Computer Science from
University Kebangsaan Malaysia (UKM), in 2008 and MSc in Computer Science
(Distributed Computing) from University Putra Malaysia (UPM), Malaysia. Her
research interests are in the areas of distributed systems and computer networking,
including wireless mesh networks.
Aznida Hayati Zakaria @ Mohamad hold Ph.D in Computer Science from
University Malaysia Terengganu, M. Sc in Distributed Databases from the same
university and a Bachelor’s degree BIT majoring computer networking from
University Utara Malaysia. Aznida Hayati works in University Sultan Zainal Abidin
as faculty member and has several years work experience in the areas of teaching,
research, administrative, programming, computer networking and arranging/organized
research conferences, seminars, workshops, events. Dr. Aznida Hayati has several
research publications in well-known international Journals and conferences.

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