A Link’s Fineness Based on Spatial Relation for Multipoint Relays Selection Algorithm in Mobile Ad Hoc Networks Algorithm

International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.5, September 2024
DOI: 10.5121/ijcnc.2024.16508 133
A LINK’S FINENESS BASED ON SPATIAL RELATION
FOR MULTIPOINT RELAYS SELECTION ALGORITHM
IN MOBILE AD HOC NETWORKS
Ayoub Abdellaoui1
, Jamal Elmhamdi1
, Es-Sadek Mohamed Zeriab2
and
Mohammed Bekkali2
1
Research Laboratory in Electrical Engineering, Team ERIT, Mohammed V University,
ENSAM, Rabat, Morocco
2
STIS, M2CS, Department of Applied Mathematics and Informatics, Mohammed V
University, ENSAM, Rabat, Morocco
ABSTRACT
The propagation of wireless mobile equipment has given rise to mobile ad hoc networks (MANET), which
allow the establishment of networks deprived of infrastructure support. The improvements in these
technologies have also resulted in the variety of link quality, scalability, stability and reachability limits.
Principally, due to their incapability to guarantee node reliability, MANETs are weak in highly dynamic
environment. Since high-speed nodes, low density, and rapid changes in the topology are directly damage
networks performances and lifetime. In response, routing is an inspiring issue in these networks. This
paper proposed a new routing scheme in MANETS. This technique is integrated in the optimized link state
routing (OLSR). In the projected scheme, the author defines a link fineness based on spatial relation
between mobile nodes, which exploits different parameters, speed, direction and acceleration, including
the sent and received of hello packets. Correspondingly, the proposed selected multipoint relays (MPRs)
based the projected methods is included in the algorithm. The scheme elected more stable MPRs with high
link’s quality, more neighborhood degree and higher willingness as MPR that can increase the
reachability and stability of the network even in an extremely dynamic situation. This version of OLSR is
verified by the network simulator (NS3) under the Radom Waypoint mobility model. Results revealed an
important improvement for SLF_OLSR. In addition, this scheme can be integrated into other protocols in
MANETs.
KEYWORDS
Mobile ad hoc networks; Olsr; Mprs selection; Link’s Fineness; Spatial relation; Ns3 simulator
1. INTRODUCTION
Nowadays, location and time features are no longer issues for users to access information and
data due to the wireless technology that proposes an endless flexibility of usage and networking.
Different advantages are presented to people in several areas, provided by the growth of this
technology to eliminate different limits such as interval of the network, uninstallation of
equipment, hardware, making a prodigious flexibility in terms of communication between
different nodes and helping mechanism to be adapted to numerous variations due to the mobility
of mobile elements in the network.
Nowadays, the implementation of wireless technology and devices has hit the roof, resulting in
an amplified claim for innovative network technologies that allow communication without

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requirement for traditional infrastructure. A typical technology that has ascended is the mobile ad
hoc network (MANET) [1]. Furthermore, An ad Hoc network is defined as a group of mobile
nodes that have the capability of exchanging data and communicating by wireless technology,
establishing a provisional network without any help and use of any management or any fixed
support. Therefore, in an Ad Hoc network, the cooperation of all nodes is important to create a
momentary architecture for a successful communication.
The perception of VANET [2] acquired importance in the scientific community in computer
science and telecommunications. Therefore, Vehicular Ad hoc networks are a derivation of
MANETs. In VANET each mobile node (vehicle) is defined as part of the network. It offers an
enhanced improvements of the intelligent transportation system in driving security [3][4].
In another way, when numerous drones communicate together in an ad hoc method, they build a
novel and an innovative ad hoc network entitled flying ad-hoc network (FANET) [5][6]. In
FANETs, a collection of UAVs works with each other without any access point, except that at
least one of these nodes must communicate with satellite or ground station (GS) [7][8]. Certainly,
this network is identified as a subcategory of MANET and VANET.
The regular physical changes of the topological structure oblige mobile nodes to accomplish all
network’s actions on their own, counting discovery of a network topology, routing tables and
updates or transferring control messages between mobile nodes without forgetting the ability of
sending, receiving and forwarding data among mobile nodes. In this situation, a distributed
fashion is involved where mobile nodes inherit router’s procedures to distribute connection
requirements among mobile nodes [9][10]. Moreover, low processing power, restricted
bandwidth links, small power resource and small memory present different limited resources that
categorize mobile nodes.
Compared to supported networks, the wireless technology creates links wirelessly which makes
data broadcast over MANETs exposed to high packet losses and more recurrent path
disconnections. Also, it reduces the reliability of the network in sending data traffic. Achieve the
connection, direct communication and collaboration with intermediate nodes is created based on
mobile nodes that are in a radio range of other nodes. MANETs have various motivating
applications that satisfy human needs such as military, emergency circumstances and natural
disasters, etc. In such applications, the idea is to make communication possible where and when
it is impossible to rely, due to the absence of fixed substructures or their high employment
charges [11][12]. Therefore, self-configuration and self-routing are also features that made
MANETs unique. Therefore, many challenges as resource limitations, changing topology,
routing, security, energy and mobility are occurred.
As mentioned before, routing information in MANETs is a big issue, creating numerous
challenges to project appropriate routing protocols. Hence, various routing protocols have been
developed and well-matched with the features and the dynamic nature of these networks. Current
routing protocols have their own mechanisms to improve some of the performance’s metrics of
MANETs as well as the throughput, Packet Delivery Ratio (PDR), routing overhead and end-to-
end delay [12][13]. These metrics are employed to improve the quality of services in the network.
For that, Routing protocols in MANETs are generally classified into three classes: proactive,
reactive or hybrid [14][15].
In proactive, each node controls its own routing tables to save the routing information for all
connected mobile nodes. Hence, to preserve the routing information continually updated, the
mobile nodes share updated data frequently or periodically, helping to make routing information
accessible when required and at the same time, increasing routing overhead. Some of these

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routing protocols are Fisheye State Routing protocol (FSR) [16] and the Optimized Link State
Routing protocol (OLSR) [17][18]. Additionally, in reactive, mobile nodes do not save routing
information, but these nodes diffuse path requests to the destination node on demand. This phase
is only executed when information routing is required, which enhances the throughput and the
end-to-end delay. Dynamic Source Routing (DSR) [19] and Ad Hoc On-demand Distance Vector
(AODV) [20] are participating in this category. Therefore, to improve the performance of the
routing protocol, a mixture of positive features of both protocols (proactive and reactive) created
a Hybrid protocol as well as a Zone Routing Protocol (ZRP) [21].
Due to the frequent movement of nodes, communications or links are nonstop cracked. All
metrics cited above are affected by these issues. In wireless networks, OLSR is categorized as an
important routing protocol outstanding to Multipoint Relays (MPRs) technology [22][23] (Figure
1). Due to the mobility issue in MANETs, stable mobile nodes make dynamic topology looks less
and big networks seem smaller. Furthermore, OLSR protocol offers an improved performance in
the network by involving MPRs nodes that can illustrate the mobility pattern considering its
functionality [24],[25] by making this mobile node as a leader of numerous nodes or groups.
Therefore, in this paper, the author exposed an algorithm accomplished to captures the group
mobility pattern with link’s quality and integrates this information to select stable MPRs.
Resolving the inefficiency in existing MPRs Selection algorithm, a new mobility technique
adaptive MPRs Selection algorithm for OLSR in MANETs is presented. The technique provides
a finest way for mobile nodes to elect more stable MPRs. The enhanced MPRs selection
algorithm is defined by mobility pattern of neighbors to guarantee extreme network stability. The
technique is adapted to high environment. To conclude, the proposal is a linear distance based
spatial dependency and link’s quality as a method for MPRs Selection named Spatial Link’s
Fineness MPR.
The positions of mobile nodes in the network are periodically measured by consuming some kind
of system localization like GPS. Also, the information of positions will be considered as the base
of our algorithm to create optimal routes with a minimum delay, lost packets and to have more
stability and reachability in the network. The modification of MPRs Selection algorithm was
integrated and the position of mobile nodes was injected in Hello message with link’s fineness,
different mobility’s features as the speed, the acceleration, the direction and also, the value of
spatial link’s Fineness MPR. The motivation for the author, is to adapt and to improve MPRs
selection for more performance in the network [26][27][28]. Additionally, the proposed method
can be efficiently used even in dynamic network situations such as MANETs, VANETs and
FANETs.
Results shown that the projected algorithm of selection MPRs improved the quality of services in
the network. The impact of these adjustments on the network performance under Random
Waypoints Model has been evaluated by NS-3 simulator [29].
The rest of the paper is organized as follows. In Related works section, the author presents many
routing methods. In Proposed Mechanism section, the author describes the concepts related to the
algorithm used. In Results and Discussion section, the author presents graphs and reflections by
comparing results. Finally, a Conclusion section is presented.

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Figure 1. Multipoint Relays Illustration
2. RELATED WORKS
This section acts as a review of different studies. Therefore, the author summarizes diverse
proposed schemes involved to enhance routing protocols. A classification of these protocols is
presented according to the mobility, distance and link quality of mobile nodes. Then, different
categories of these schemes are described depending on speed, direction, quality, position of
nodes, probabilistic methods or degree of mobility etc.
Therefore, the Authors proposed in [30] a survey of approaches and procedures associated to
improve MPR selection. For this, the author presented a synthetic study of various procedures
and approaches for scheming and selecting MPR nodes based on a set of criteria as energy,
mobility, bandwidth, quality of links, etc. The result of this work illustrated that most procedures
consider a restricted number of metrics to select MPR nodes, so, they are inadequate to tolerate
OLSR protocol to be quite complete and efficient due to several metrics that occurred at the same
time in the real environment.
The author in this paper [31] offered a summary of the idea of vehicular ad hoc networks,
applications, characteristics. Therefore, the paper is providing some of problems and challenges
in VANET.
In the context of Proactive routing protocols, authors [32] have constructed a complete
mathematical-based model to evaluate the Destination-Sequenced Distance Vector protocol
(DSDV) and examined its performance on estimating the route length based on the concepts of
the probability density purpose and the predictable values to discovery greatest estimate values in
real scenarios. Authors have confirmed the validity of the projected model using simulation
scenarios applied by the Network Simulator (NS3). Results obtained from the mathematical
model and the simulation have revealed that the route length is inversely proportional to the
speed of mobile node and the hop count. Moreover, the route length estimated from the DSDV
protocol is fewer than the actual, because to the implementation of the settling time concept and
keeping periodic routes update parameter at a constant level, despite the fact that the speed of
mobile nodes can reduce the operative route employment.
Correspondingly, the MPR selection works very well to broadcast packets in the network. Truly,
various studies accessible in the literature were attracted by this subject. MPRs nodes can
significantly disturb the performances of the network, that means, choosing reliable MPRs
revealed hope to increasing these performances. Inside TC messages, MPRs nodes diffuse links
which might create routes from source to destination, formerly MPRs form a kind of support in

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mobile ad hoc network. Hence, choose MPRs that encounter a specified requirement to enhance
the networks performance. Hence, an examination of MPRs selection in OLSR Protocol [33]
determined that paths performance can be improved by using additional criteria in MPRs
mechanism. Additionally, routing metrics are taken into reflection to choose mobile nodes relays.
Greatest works in OLSR hope to find other efficient metrics rather than the default one presented
in the RFC3626 [18] where the path quality is defined by number of hops.
Grounded on OLSR, authors in the paper [34] have matched the improved Optimal Link State
Routing Protocol (MMPROLSR) with the GSA-PSO (Gravitational Search-Particle Swarm
Optimization) scheme in grouping with the cognitive radio technique. This method can be useful
to Vehicular Sensor Networks. MMPR-OLSR with GSA-PSO optimization simplifies the
MMPR-OLSR protocol to elect the appropriate mobile nodes using a finest searching technique.
GSA-PSO not only supports in selecting suitable MMPR nodes, but also helps in reducing the
needless overheads due to the broadcast of control packets and in reducing the number of relay
selector nodes used in communication. This scheme also focuses on assigning stations between
all users of the network, which is measured by the proposed method. A group of mobile nodes are
designated before the start of the actual transmission.
These vehicular nodes within the transmission range are selected as relays. Cognitive radio acts
by recognizing the idle channels, thus allowing the usage of the unexploited channels.
The projected method works efficiently in reaching the objective of operative channel utilization
united with efficient transmission. The proposed approach is simulated using the NS2 platform
and shown a sharp reduction in delay and a high packet delivery ratio in addition to a high
channel utilization. The effective channel utilization used in the GSA-PSO method helps to
decrease the delay in packet transmission and delivery by providing a good PDR.
Another paper [35], used OLSR to explore the performance of VoIP applications in the VANET
network. The network was verified before and after running the OLSR algorithm, the challenging
absorbed on Quality of Service (QoS) parameters and probability of packet loss among two
moving hops through multi-hop Ad-hoc networks in diverse situations based on the ITU G.711
VoIP codec. The simulation shown a decreased delay by 18.72%, while decreased jitter by
20.42% and decreased packet loss by 128.6%. Nevertheless, The OLSR has exposed primary
good performance for four hops, and then supplementary hops, the delay surpassed 400ms which
is not suitable according to ITU-T recommendations.
Therefore, this paper [36] objects to suggest an enhanced harmony search optimization (EHSO)
algorithm that reflects the configuration of the OLSR parameters by joining two steps, a method
for optimization approved out by the EHSO algorithm created on embedding two prevalent
selection approaches in its memory, namely, roulette wheel selection and tournament selection.
Simulation analysis demonstrated that the projected method has attained the QoS necessity,
compared to the existing algorithms.
In the same area, the paper [37] proposed to use ELMs, which are renowned for their aptitude to
estimated anything, to model and estimate the mobility of each mobile node in a MANET.
Mobility-aware topology control approaches and location-assisted routing both influence the
prediction of the mobility in MANETs. It is supposed that each mobile node taking part in these
protocols is attentive of its current mobility information, including location, velocity, and
movements direction angle. This method predicts locations of upcoming nodes and distances
between succeeding nodes. A fuzzy logic-based routing protocol is exposed in another paper [38]
to replicate battery level of nodes, hop count, and trust between nodes. The anticipated routing
protocol adaptively chooses paths that use lowest hop count with the highest level of trust and a

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satisfactory battery level to improve the reliability of path selection while preserving the
percentage of effectively delivered packets. The result of the simulation revealed that the
proposed protocol can attain a high ratio of successfully delivered packets, a lower average end-
to-end delay, and a normalized routing load.
In other subject of Ad hoc Networks, specifically Fanets, authors of this paper [39] projected an
innovative optimized link-state routing technique with a greedy and perimeter forwarding
Aptitude entitled OLSR+GPSR in flying ad hoc networks. The planned scheme uses a fuzzy
system to adjust the broadcast period of hello messages based on velocity of UAVs and position
prediction error, accordingly, that high-speed UAVs have a shorter hello broadcast period than
low-speed UAVs. In OLSR+GPSR, MPR nodes are selected based on some metrics, particularly
neighbor degree, node stability which is derived from velocity, direction and distance, the
occupied buffer capability, and residual energy. Finally, the proposed scheme removes two
phases in OLSR, i.e., the TC message broadcasting and the calculation of all routing routes to
decrease routing overhead. OLSR+GPSR is simulated on an NS3 simulator, and this evaluation
presented the superiority of OLSR+GPSR in terms of delay, packet delivery ratio, throughput,
and overhead compared to Gangopadhyay et al., P-OLSR, and OLSR-ETX.
Authors Of another paper [40], presented a smart filtering-based adaptive optimized link state
routing (SFA-OLSR) scheme in FANETs. To enhance flexibility in the FANET environment,
SFA-OLSR offers a novel solution to regulate the hello broadcast period so that each flying node
specifies its broadcast period based on an innovative scale titled cosine similarity among real and
predicted positions. Moreover, in SFA-OLSR, each flying node develops a filtering algorithm
based on link lifetime and remaining energy. The goal of this algorithm is to decrease the size of
the single-hop neighboring set of each flying node and diminish the search space when finding
multi-point relays (MPRs). Then, SFA-OLSR exploits the sparrow exploration algorithm (SSA)
to elect the best MPRs. This algorithm presented a multi-objective purpose by concentrating on
energy, link lifespan, and neighbor degree. Finally, the simulation of SFA-OLSR is accomplished
by the NS3 simulator. These evaluations illustrated that SFA-OLSR has a good performance in
terms of packet delivery ratio, delay, and throughput, but its overhead is increased.
Authors in [41] proposed a complete search of cluster-based routing protocols (CBRPs) in terms
of their strengths, weaknesses, specific applications, methods, number of nodes, and future
perfections for helping FANETs. Furthermore, 21 CBRPs based FANETs were studied in terms
of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features,
cluster head (CH) selection, routing metrics, and performance measures. Additionally, open
problems that need to be talked in future revisions in the field of routing protocols for UAV
networks.
In other way, this paper [42] objected to stream time sensitive applications using mobile ad hoc
network (MANET), the author has selected the Optimal Link State Routing (OLSR) protocol.
Still, the protocol has increased overhead because each node chooses a set of multipoint relays
(MPR) nodes. Hence, authors proposed quality of service (QoS) supporting the MPR selection
method and a novel lower maintenance clustering method for diminishing the overhead of the
network. As a result, the projected method exposed a great result in the average end-to-end delay,
packet delivery ratio, routing load, and throughput. Furthermore, authors in [43] developed a new
dynamic hello technique, where new neighbour nodes and lost neighbour nodes are used to
compute link change rate (LCR) and hello-interval/refresh rate (r). The Exchange of link
connectivity information at a fast rate consumes unnecessary bandwidth and energy. In MANET
resource wastage can be controlled by avoiding the re-route discovery, frequent error notification,
and local repair in the entire network. We are enhancing the existing hello process, which shows
significant improvement in performance.

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Additionally, several revisions proposed schemes that integrated the degree of mobility to
systematically study the impact of mobility on the performance of routing protocols for ad hoc
networks. These studies offered an opportunity to think about mobility metrics that measure this
relationship.
Correlated to this report, Bai et al. [44] advised the important framework to methodically
investigate the impact of mobility on the performance of routing protocols for ad hoc networks.
Authors suggested two mobility schemes for measuring temporal and spatial movement
dependence between mobile nodes. Both schemes are based on the cosine similarity between the
velocities of nodes.
The first is Degree of Spatial Dependence between nodes (i) and (j) at time t, named DSD (i,j,t).
DSD(i,j,t)=Cos(i,j,t)*SR(i,j,t) (1)
The second planned a Degree of Temporal Dependence (DTD) [45], which is like to DSD, but
the difference is that DTD considered the difference among velocities along two time slots. Thus,
depending on its past moving pattern, mobile nodes expected its speed. This scheme reproduces
the smoothness of node movement.
DTD(i,t,t’)=Cos(i,t,t’)*SR(i,t,t’) (2)
In order to provide a better understanding of spatial dependence, authors in [46] proposed a more
comprehensive mobility metric and schemes. The simulations were based on the NS3 simulator
and the proposed method presented great performances compared to OLSR standard.
Various mobility models can be integrated to evaluate the performance of routing protocols in
MANETs. Comprehensive examination of these models is presented in [47], [48]. This paper is
built on Random Waypoint model [49] and it suggests a new method for the mechanism of
multipoint relays based on mobility and quality of links called as Spatial Link’s Fineness MPR
(SLFMPR) integrated in the algorithm of the MPRs selection in OLSR Protocol.
3. PROPOSED MECHANISM
In Mobile Ad hoc Networks, at any time and randomly, mobile nodes change their positions that
disturb the link’s quality and stability.
3.1. Brief Description of OLSR
OLSR is an efficient table-driven protocol developed for mobile ad hoc networks, it’s not
complicated logic and easier to adjust. Topological information of the network is regularly
exchanged among nodes in OLSR. In the network, mobile nodes diffuse Hello messages
(Figure2) [18] to their 1-hop neighbors, defining several of them to run as MPRs (Figure 1) to
diffuse topological information and create link state information to be directed throughout the
network. MPRs propose a powerful mechanism for monitoring traffic by reducing the required
number of transmissions. Also, MPRs are used in path calculation to link all destinations in the
network. Due to the mechanism of MPRs, OLSR offers finest paths considering number of hops
and it is mainly appropriate for large and dense networks. In other way, OLSR routing protocol
presents restriction in calculation of minimum MPRs (NP complete problem). Hence, to elect
MPRs is very inspiring and diverse heuristics algorithms can be explored in order to find the
perfect result.

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Figure 2. Standard Hello Message in OLSR Protocol
3.2. Terminology and Introduction of the Improvement Mechanism
Related to studies cited before, the author developed and extended an innovative scheme named
Spatial Link’s Fineness MPR (SLFMPR) used in basic selection of MPRs in OLSR.
The scheme defines the quality of the connection and the similarity of mobility features between
two nodes that are within their transmission range. Mobile nodes with the high link quality and
the same mobility features are more likely to move together over a period of time with a great
linking to achieve their tasks until one of them leaves the transmission range and cannot be
selected as MPR.
Table 1. Terminology Of The Improvement.
Terminology Description Unit of measure
D Linear distance [m]
S Average speed [m/s]
θ Direction of a node [°]
V Velocity of a node [m/s]
A Average acceleration [m/s2]
∆T Time interval [s]
t Current time [s]
RS(i,j) Relative speed
RA(i,j) Relative acceleration
RD(i,j) Relative direction
SD Spatial dependency
LF Link’s Fineness
SLFMPR Spatial Link’s Fineness MPR
∆xT,∆yT The increment of the linear distance in x
and y coordinates
xi(t)), yi(t), Xi(T),Yi(T) The coordinates of the node i at different
time
3.3. Description of the Proposed Mechanism
Let MPR(S), N(S) and N2(S) as the MPR, N and N2 of the node S which are defined as the
original OLSR protocol. After studying all the steps in this algorithm, the technique was added
without changing the OLSR algorithm.
Let’s consider numerous mobile ad hoc nodes in a network, involving the dynamic establishment
of links such as 𝐺 (U, 𝐸) is a direct graph and (U) is the set nodes and 𝐸 is the set of links 𝑙 = (i,
j), where the node (j) is within the area of (i).
At (∆T), (∆xT) and (∆yT) are the increment of the linear distance in (x) and (y) coordinates as:

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(3)
Where (t) is the current time and (xi(t)), (yi(t)), (Xi(T)), (Yi(T)) represent the spatial coordinates
at time (t) and (T) respectively.
Hence, the linear distance (D) can be defined as:
D= = (4)
Therefore, the speed (S) over time (∆T) is defined as:
= (5)
The direction of node (θ) can be defined as:
(6)
Where and
The node computes its acceleration (A) over time ΔT based on the velocity (V) which is the
speed combined with the direction:
(7)
each node evaluates the connection quality according to the equation below:
= (8)
where, Rhello(i, j) and Shello(i, j) indicate the number of Hello messages received and the
number of Hello messages sent from node(j) to node(i).
LF(i,j) is the link’s fineness between two mobile nodes in the network.
Based on these values, a node calculates its Spatial Link’s Fineness MPRs (SLFMPR) with the
next phases:
First phase: Nodes exchange their mobility features and their link’s fineness with its directly
connected neighbors through Hello packets (Figure 3).

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Reserved Htime Willingness
Link
Code
Reserve
d
Link Message Size
Speed Accelerati
on
LF Direction
SLFMPR
Neighbor Interface Address
Neighbor Interface Address
Figure 3. Modified Hello Message in OLSR Protocol
Second phase: A node estimates its Relative Speed (RS), Relative acceleration (RA) and Relative
Direction (RD) with its directly connected neighbors.
For nodes (i) and (j), RS of these two nodes is defined as:
(8)
Where (Smax) is the node’s maximum speed and RD of these two nodes is the cosine of the
angle between (i) and (j) at time (t) and it can be calculated as:
(9)
And RA between two nodes (i) and (j) is given by:
(10)
Where (Amax) is the maximum acceleration of node.
Third phase: Spatial Dependency (SD) between node (i) and node (j) can be defined as:
(11)
Fourth phase: Spatial Link’s Fineness MPR (SLFMPR) of a node is defined as the average of the
summation of all (SD) it has of all its n neighbors with the incorporation of the link’s fineness
which is also calculated as the average of the summation of all (LF) it has of all its n neighbors.
Then, it can be calculated as:
(13)
A higher (SLFMPR) value implies that node (i) has a bigger neighbor set with a better connection
quality. The spatial relation and the link’s fineness may be powerfully associated together.
Accordingly, to reproduce linking quality and mobility features of the group (neighbors
connected), a node with a higher the SLFMPR value is elected as MPR. Also, SLFMPR value
defined above can extend stability, and improve connection’s quality and reachability by making
the routing applicable in extremely and various mobile environment.

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Figure 4. The modified multipoint relay selection
Figure 5. Modified MPRs Selection Flow Chart
4. RESULTS AND ANALYSIS
4.1. Simulation Mobility Model
The research is constituted in a C++ environment created by the author under the NS3 simulator
[29]. Assorted studies were based on Random Waypoints as the greatest mobility model for
MANETs to evaluate the performance of routing protocols. The paper compares the performance
of diverse scenarios of the modified OLSR and the original (i.e. SLF_OLSR with OLSR). The
simulation is executed for 100 s. 10,20,30,40,50,60,70,80,90,100 identical mobile nodes are
arranged in a terrain of 1000 m by 1000 m.
An investigation of diverse graphs was presented to discuss simulation results and Table II
summarizes all parameters used during simulation.

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Table 2. Simulation Parameters.
Simulation Parameters Value
Flat Size 1000 m × 1000 m
Max Number Of Nodes 10,20,30,40,50,60,70,80,90,100
Radio Scoop 250 m
MAC Layer IEEE.802.11.peer to peer mode
Transport Layer User Datagram Protocol (UDP)
Traffic Model Used CBR
Package Size 1024 bytes
Rate 0,4
Mobility Model Random Waypoint
Pause Time 1 seconds
Maximum Speed of Nodes 25 m/s
Simulation Time 100 Seconds
4.2. Comparison and Discussion
Both protocols are compared and exposed in the graphs below. It is observed that SLF_OLSR
exposed enhancement compared with the original OLSR, especially, when the network contains
the largest set of nodes. It approves the effectiveness of SLF_OLSR for dense networks.
Comparison results exposed the impact on the performances of the protocol.
In Figure 6, the author examined the time for data packets diffused from source to destination.
The comparison shown that SLF_OLSR presented a lower delay compared to the original OLSR.
The enhancement can be clarified by efficiency that relativity and link’s quality between nodes
involved in the selection of MPRs to get more proficiency direct neighbors. This method helps
paths to still valid with a perfect linking.
Figure 6. Mean Delay Comparison
The jitter is decreased due to mobility scheme and link’s fineness between mobile nodes. In the
context, SLF_OLSR has a minimum jitter compared to original OLSR as presented in Figure 7.
The enhancement proves that the method defined by authors offers a dissimilarity in broadcast
and communication, mainly in situations that contains more agitation nodes.

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Figure 7. Mean Jitter Comparison
compared to original OLSR, SLF_OLSR protocol reflected a lowest value of the packet loss ratio
as presented in Figure 8 and this is due to the quality of links and the relativity approach between
mobile nodes which help the transmission of packet to be successfully completed.
Figure 8. Packet Loss Ratio Comparison
Packets successfully broadcasted in both protocols are compared in Figure 9. Even in dense
network, SLF_OLSR certified a better delivery of packets compared to original OLSR. In
addition, the graph approves that SLF_OLSR protocol has good performance for packet delivery
ratio (PDR).
Figure 9. Packet Delivery Ratio Comparison
The Figure 10 exposes the comparison of lost packets in relation to node’s number. The value of
lost packets in SLF_OLSR is minor compared to original OLSR. The link quality, link duration,

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reachability and relativity are crucial to have an efficacy communication, which are attained by
relativity and link’s quality between nodes and their MPRs. SLF_OLSR avoids transmission’s
breaking frequently done between mobile nodes.
Figure 10. Lost Packets Comparison
Impact of node’s number on throughput is shown in Figure 11. Furthermore, SLF_OLSR
protocol based on relativity and link’s quality integrated in the selection of MPRs, presented an
enhanced throughput compared to original OLSR. Hence, the approach between mobile nodes
helps the transmission of packet to be successfully accomplished.
Figure 11. Throughput Comparison
5. CONCLUSIONS
In this paper, the author presented an efficient MPRs selection algorithm based on relativity and
link quality which are based on mobility scheme and ratio of sent and received of hello packets.
This approach helped to reduce the impact of mobility in mobile ad hoc networks by a purpose
that objects to increase network reachability and quality. Based on the novel method, the
maximum MPRs reachability with link’s fineness are assured. As projected, MPRs designated are
more powerful compared with original OLSR by involving new criteria in MPRs selection
algorithm through SLFMPR value. NS3 simulator is used for evaluating performances. The
Random Waypoint mobility model is considered for simulating the movement of mobile nodes in
the simulation. Simulation results show that SLF_OLSR has an effective performance compared
to the original OLSR in terms of lost packets, delay, jitter, PLR, PDR and throughput. In future
works, an evaluation of the novel scheme with other mobility models and more scenarios will be

147
expected to highlight the efficiency of the proposed method. Furthermore, a comparison of results
with the latest OLSR-based protocol is also projected.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
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AUTHORS
Pr. Ayoub Abdellaoui is a professor at the National Higher School of Arts and Jobs
of Rabat, Morocco. He specializes in information system, web development,
networking in his teaching. His main research activities focus on computer science,
computer networks, wireless network and communication.
Pr. Jamal El Mhamdi is a professor at the National Higher School of Arts and
Jobs of Rabat, Morocco. He specializes in signal processing, analysis and
interpretation techniques in his teaching. His main research activities focus on
signal processing.
Pr. Es-sadek Mohamed Zeriab is a professor at the National Higher School of Arts
and Jobs of Rabat, Morocco. He specializes in mathematics in his teaching. His main
research activities focus on Coupling, Procedure, Objective, Function Optimization,
Algorithm Optimization.
Pr. Mohammed Bekkali is a professor at the National Higher School of Arts and
Jobs of Rabat, Morocco. He specializes in Artificial Intelligence and Data Science in
his teaching. His main research activities focus on Electrical, Computer Engineering
and Artificial Intelligence.

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