Improved MPR Selection Algorithm-Based WS-OLSR Routing Protocol

International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.3, May 2024
DOI: 10.5121/ijcnc.2024.16301 1
IMPROVED MPR SELECTION ALGORITHM-BASED
WS-OLSR ROUTING PROTOCOL
Waleed Khalid Ahmed1,2
, Mohd Nazri bin Mohd Warip1,2
, Mohamed Elshaikh
Elobaid Said Ahmed1,2
and Phaklen Ehkan1,2
1
Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Arau,
02600, Perlis, Malaysia.
2
Centre of Excellence for Advanced Computing (AdvComp), University Malaysia Perlis,
Arau, 02600, Perlis, Malaysia.
ABSTRACT
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on
the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing
Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of
MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an
improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing
efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as
redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-
OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as
node mobility and link changes are comprehensively considered to reflect network topology changes, and
the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new
MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its
effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation
results demonstrate the superior performance of our improved MR selection algorithm when compared to
traditional approaches.
KEYWORDS
Ad hoc Network, VANETs, OLSR Routing, MPR, Node Residual Energy.
1. INTRODUCTION
Vehicle Ad Hoc Networks (VANETs) have become a game-changing technology with the
potential to greatly improve traffic management, road safety, and vehicular communication in
general [1], [2], [3]. To transmit vital information including traffic conditions, safety alerts, and
real-time navigation data, vehicles in VANETs communicate with one another and with roadside
infrastructure. Numerous routing methods have been suggested to enable effective data
dissemination in VANETs, with the Wingsuit Search-based Optimised Link State Routing
Protocol (WS-OLSR) emerging as a popular option due to its scalability and adaptability to
highly dynamic vehicular environments [4].
Despite the promising attributes of WS-OLSR, the selection of MultiPoint Relays (MPRs)
remains a persistent challenge in the protocol, as underscored by previous studies [5] [6]. While
these research endeavours have made notable contributions to the field, they often fall short of
comprehensively addressing the distinct hurdles posed by weighted links, dynamic network
topologies, and scalability issues inherent to WS-OLSR [7].

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MPRs hold a pivotal responsibility in efficiently routing control messages and data packets
throughout the network. Conventionally, MPRs have been designated based on fixed criteria,
frequently reliant on a vehicle's position within the network graph [8] [9]. However, these
conventional MPR selection algorithms frequently lack the agility required to cope with the
dynamic nature of Vehicular hoc Networks (VANETs). In VANETs, vehicles are in perpetual
motion, operating at diverse speeds and densities, introducing a layer of complexity that static
MPR selection algorithms are ill-suited to manage.
The repercussions of suboptimal MPR selections in VANETs are substantial, leading to elevated
control message overhead, prolonged end-to-end data transmission delays, and reduced packet
delivery rates which ramifications significantly impede network performance and efficiency.
Given these distinct challenges and constraints found in existing research, a thorough review and
analysis are warranted to delineate this paper from prior work. This paper seeks to bridge this gap
by presenting an innovative approach or algorithm tailored to address the intricacies of weighted
links, dynamic topologies, and scalability concerns that are inherent in WS-OLSR. In so doing, it
provides a fresh and novel perspective, offering inventive solutions to MPR selection in
VANETs, thereby distinguishing itself from prior research and necessitating a comprehensive
review.
In reference [10], various efforts have been dedicated to improving the OLSR routing protocol by
optimizing the crucial HELLO and TC messages. These messages are fundamental for neighbour
discovery and topology dissemination within OLSR networks. The research conducted in this
reference delves into strategies for reducing the overhead associated with these messages while
ensuring their continued effectiveness [11]. The primary aim here is to minimize unnecessary
message transmission and enhance the efficiency of OLSR through message format optimization.
Additionally, reference [12] introduces a noteworthy approach aimed at sustaining the stability of
multi-hop links. This approach involves actively adding and managing routes to achieve and
maintain route stability. By doing so, the protocol seeks to offer consistent and reliable
communication paths, ultimately leading to improved packet delivery and reduced disruptions in
wireless networks. References [13] and [14] also contribute to the discussion by proposing new
ideas regarding the selection algorithm for the MultiPoint Relays (MPR) set in the OLSR routing
protocol. These algorithms incorporate a diverse array of factors into their selection processes,
with overarching objectives of elevating data transmission success rates, enhancing network
stability, and mitigating issues such as packet loss, network overhead, and latency.
In the selection of the MPR set, various factors such as node movement state, connection time,
node-link rate change rate, link congestion degree and node remaining energy are considered to
improve the success rate of data transmission and network stability and reduce the packet loss
rate, network overhead and latency purposes. In Reference [15], [16] refers to the intelligent
cluster algorithm to optimize the application of VANET self-organizing network, but its biggest
defect is that this kind of algorithm has high requirements on the computing power of VANET,
and the start-up time is long, which can the practical scope of the application is very limited.
To sum up, most of the current optimization schemes for OLSR at home and abroad focus on the
optimization of the MPR selection algorithm and the optimization of the optimal solution through
the cluster solution. The overhead is greater and the response sensitivity is reduced. Therefore,
considering the above factors and the characteristics of the OLSR protocol itself, to achieve the
purpose of reducing network overhead, improving network stability and increasing network
survival time. In this study, we have presented an enhanced MPR selection algorithm tailored to
the WS-OLSR routing protocol in VANETs. The improved MPR selection algorithm, which
integrates the link stability problem and takes the remaining energy (survival time) factor into

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consideration, the flooding cycle of TC messages is controlled according to the update frequency
of the MPR set. By accounting for dynamic network conditions, our algorithm substantially
improves the protocol's performance, leading to better packet delivery ratios, reduced delays, and
decreased control message overhead.
These enhancements make our algorithm a valuable contribution to the field of VANET research,
promoting safer and more efficient vehicular communication. WS-OLSR extends OLSR by
introducing link weights that reflect various network metrics such as link quality, bandwidth, or
other relevant factors.
This innovation enhances the protocol's capability to make informed routing decisions in
dynamic wireless environments. Future work may explore the integration of machine learning
techniques to further optimize MPR selection in highly dynamic VANET environments.
2. OPTIMIZATION OF OLSR ROUTING PROTOCOL
The primary objective of routing protocols like OLSR is to establish and maintain efficient data
paths between nodes in a network. OLSR achieves this by proactively exchanging topology
information among neighbouring nodes, which allows each node to build a routing table based on
the most up-to-date network state [17] [18], [19]. However, while OLSR exhibits many desirable
characteristics, it is not without its challenges and limitations.
Optimized Link State Routing (OLSR) is a routing protocol mainly used in VANET networks
[20], [21]. In the traditional link-state routing algorithm, each node in the network broadcasts its
link-state packets to other nodes, and this process is called flooding. Each link state packet
contains the link identification and cost that the node is connected to, and finally, after flooding,
each node in the network can get the same network topology map. OLSR optimizes the
traditional algorithm, and the core mechanism here is the selection of the MPR set and the
working mechanism of MPR [22], [23], [24].
A small number of nodes are selected as MPR nodes, and only MPR nodes are allowed to
broadcast and flood control messages, the to reduce the number of flooding times and the number
of flooding nodes, thereby reducing the amount of information transmission and reducing
network overhead [25]. OLSR is suitable for large-scale, high-density scenarios.
For the optimization of OLSR, two optimization schemes are given in this paper. One is to
control the broadcast period of messages through topology changes, and the other is to propose a
new MPR selection algorithm for the defects of traditional MPR selection algorithms. Through
the above two optimization schemes, based on the original OLSR protocol, the WS-OLSR
protocol has better performance, such as lower routing overhead and energy consumption, and
higher message delivery rate.
2.1. Broadcast Mechanism of HELLO Message
The broadcast mechanism of HELLO messages plays a critical role in various networking
protocols, including routing protocols like OLSR (Optimized Link State Routing), where HELLO
messages are employed to establish and maintain neighbour relationships among network nodes.
OLSR maintains routing information by regularly broadcasting HELLO messages and MPR sets
forwarding TC messages by nodes in the network. HELLO messages are used to establish local
link information databases and adjacent node information databases [26], [27], [28].

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However, when the network topology does not change much and the node status is stable, if the
message is sent according to the original broadcast cycle, unnecessary operations will occur,
resulting in a large amount of redundant network overhead and energy consumption [29]. If the
network topology changes frequently, the network fluctuates greatly, and the node status is
unstable, the originally set message-sending interval will make OLSR unable to update the
network status in time, resulting in network performance degradation.
Therefore, this paper considers defining the topology state of the network through node
information and controlling the flooding cycle of messages through the topology state of the
network.
This research examines the relative mobility and link status of each node in the network and all of
its one-hop neighbour nodes to assess the network topology state. Because there are few
significant three-dimensional dynamic changes in the working scene and most of the changes are
small, the mathematical modelling of the VANET assumes that it is moving on the same
horizontal plane. A three-dimensional coordinate system is therefore not established.
a) Relative mobility of nodes:
Define node i as any node in the network, and j as any one-hop neighbour of node i, then the
moving speed of node j relative to node i at time t1 is:
𝑉𝑖𝑗 = √(𝑉𝑖𝑥 − 𝑉
𝑗𝑥)2 + (𝑉𝑖𝑦 − 𝑉
𝑗𝑦)2 (1)
Where, Vix is the velocity of node i in the horizontal direction in the coordinate system, Viy is the
velocity of node i in the vertical axis direction in the coordinate system; Vjx is the velocity of
node j in the horizontal direction, and Vjy is the velocity of node j in the vertical direction.
The relative distance of node j relative to i at time t1 is:
𝑆𝑖𝑗 = √(𝑋𝑖 − 𝑋𝑗)2 + (𝑌𝑖 − 𝑌𝑗) 2 (2)
Where 𝑋𝑖 and 𝑋𝑗 are the position changes of node i and node j in the horizontal direction in the
coordinate system, and 𝑌𝑖 and 𝑌𝑗 are the position changes of node i and node j in the vertical
direction in the coordinate system; T is the flooding period of node i, t1 is defined as the time
before T, and t2 is the time after T, then the moving speed of node j at time t2 relative to node i is
𝑉𝑖𝑗′, then node j at time t2 moves relative to node i. The distance is 𝑆𝑖𝑗.
The speed change and distance change in the T time range are expressed as:
𝛥𝑉 = |𝑉𝑖𝑗 − 𝑉𝑖𝑗′| (3)
𝛥𝑆 = |𝑆𝑖𝑗 − 𝑆′𝑖𝑗| (4)
Define M as the relative mobility of nodes, then M is expressed as:
𝑀 = 𝑎𝛥𝑉 + 𝑏𝛥𝑆 (5)
Where a and b are weights, and a+b=1. Define a count variable Na of node i and the mobility
threshold m between nodes. The initial value of Na is 0. When the relative mobility M>m
between node i and node j, the value of Na is increased by 1.

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b) Node link state, the node link state is defined by:
𝑁𝐿𝑖𝑛𝑘𝑠 = 𝑁𝑛𝑒𝑖 + 𝑁𝑠𝑦𝑠 + 𝑁𝐴𝑠𝑦𝑚 (6)
Where 𝑁𝐿𝑖𝑛𝑘𝑠 is the current link status of the node, and the change of the network topology of the
surrounding nodes is inferred by monitoring the changes in the node's information table. B is the
change number (increase and decrease) of neighbour nodes around the node within a HELLO
message sending time interval; 𝑁𝐴𝑠𝑦𝑚 is the number of symmetrical one-hop nodes is newly
added by a node within a HELLO message sending time interval, and 𝑁𝐴𝑠𝑦𝑚 is the number of
symmetrical one-hop nodes within a HELLO message sending time interval. The number of
symmetric one-hop nodes reduced by nodes. Because neighbours change and links become
asymmetrical, the network needs to be detected again, and generally three HELLO messages are
sent. Therefore, 𝑁𝑛𝑒𝑖 and 𝑁𝐴𝑠𝑦𝑚 here are the average values of the interval of 3 HELLO
messages, and only one notification is required for the link state to become symmetrical, so
𝑁𝐴𝑠𝑦𝑚 is the current value.
Based on the relative mobility and link status of the above nodes, the network topology changes
around the nodes are obtained, so the calculation formula for defining the network stability is:
𝑁𝑆 = 0.3 × 𝑁𝛼 + 0.7 × 𝑁𝐿𝑖𝑛𝑘𝑠 (7)
The difference in the coefficients in the formula is because the link status reflects the network
status more clearly, while the mobility status reflects more the physical level node movement
status, and more is predictive function. There may be no change in the network topology level
when the node mobility fluctuates, but if this situation continues, the change in the network
topology level will appear predictably, and it will play a role of early warning and monitoring at
this time. However, in more cases, it is caused by link state changes or both occur at the same
time.
Define the sending interval increment ΔH=1s, Htwhich is the default HELLO message sending
interval of OLSR routing protocol, which is 2s. His is defined as the adaptive HELLO message
sending interval. This paper comprehensively considers the impact of links and mobility on Ns
and divides His into three intervals. When Ns=0, the network state is considered to be in a
relatively stable state, and His = Ht + ΔH; when Ns=1, the network is considered to be in a
normal state, His = Ht ; when Ns≥2, the network is considered to be in a state of violent
fluctuations, in order to update the network status in time, set His = Ht − ΔH [23]; So His is
expressed as:
ΔH [23]; So the expression of His is:
𝐻𝑖𝑠 = {
𝐻𝑡 + Δ𝐻 0 ≤ 𝑁s < 1
𝐻𝑡 1 ≤ 𝑁s < 2
𝐻𝑡 − Δ𝐻 2 ≤ 𝑁s
(8)
In order to take into account, the hysteresis of His changes caused by the existence of
intermediate states in the process of network state changes, the expression is optimized:
𝐻𝑖𝑠 = {
𝐻𝑡 + 𝑁s × Δ𝐻 0 ≤ 𝑁s < 1
𝐻𝑡 − (𝑁s − 1)Δ𝐻 1 ≤ 𝑁s < 2
𝐻𝑡 − Δ𝐻 2 ≤ 𝑁s
(9)

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2.2. TC Message Flooding Mechanism
The TC (Topology Control) message flooding mechanism is a crucial component in proactive
routing protocols like OLSR that rely on the exchange of network topology information to
establish and maintain efficient routing paths. TC messages are used to disseminate information
about a node's network neighbourhood, allowing other nodes to construct and update their routing
tables. The TC message is different from the broadcast mechanism of the HELLO message and
the communication range of one hop.
The existence of the TC message is to maintain the topology of the entire network, so its message
forwarding range is the entire network, and the MPR node set is responsible for forwarding. The
HELLO is longer. To ensure the timeliness of the TC message, the valid time of the TC message
is longer than that of the HELLO message.
Therefore, to maintain the sending interval of the TC message, it is only necessary to monitor the
change of the MPR set to know the change of the network topology. TCt is the default flooding
period, which is 5s. Define the TC message flooding period after maintenance as TCis, define M
as the counting unit, and set the initial state flooding period as TCt. When the MPR set does not
change, set M=0, Define the current interval as TCtcur, let the next message flooding period
TCis=TCtmin + 1, until the TCis reaches the maximum threshold value, set the maximum sending
interval as 8s; when the MPR set changes (node-set, link increase or decrease), set M=1, let
TCis=TCtmin, TCtmin be set to 4s. Therefore, the expression of TCis is:
TCis = {
TCtcur M = 0
TCtmin M = 1
(10)
3. PROPOSED METHOD
In OLSR routing, the MPR mechanism is its core idea. A node selects an MPR node set through
its one-hop neighbour nodes and two-hop neighbour nodes. All nodes can receive the message,
but only the nodes selected as the MPR set can forward the message to this node. The
information required for the calculation of the MPR set is obtained through the periodically
broadcast HELLO message. Note that there is a willingness option in the HELLO packet data. A
node carrying willing_never will never be elected as an MPR by any node.
A node with willing_always is preferred to be elected as MPR. The default is willing_default.
The current traditional MPR selection algorithm is proposed in the standard OLSR protocol.

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Figure 1. Traditional MPR selection algorithm process
This is a wingsuit flying search algorithm [4], which designs an MPR set that can ensure that a
node can reach all strictly symmetrical two-hop neighbour nodes through the MPR node relay
whose willingness is not willing_ never. The algorithm flow is shown in Figure 1.
At the same time, there are still some problems in the traditional MPR selection algorithm, such
as redundancy, the selected MPR set is not optimal, unnecessary network overhead is generated,
the energy consumption of MPR is not considered, and the stability of nodes selected as MPR is
not considered. This paper proposes the improve OLSR protocol to based on the selection factors
such as node energy, link and mobility are considered. The selection method of MPR optimized
based of the original algorithm is as follows.
3.1. Model Formulation
In the OLSR routing protocol, the MPR mechanism is the core mechanism, and choosing a
suitable MPR node will directly affect the routing overhead, energy consumption and network
reliability. Therefore, to establish reliable routing and ensure good network performance,
appropriate MPR nodes must be selected. The high mobility of nodes will make link on-off and
information exchange more frequent compared with the general mesh network, resulting in
higher energy consumption. Therefore, when considering the selection of MPR nodes, factors
such as node residual energy and node-link conditions should be considered. To comprehensively
consider the above factors, make the selected MPR as stable as possible and reduce the
probability of MPR switching.
a) For the node survival problem, the survival time of the node can be predicted by the
remaining energy 𝐸𝑟 of the node. The relevant meanings are as follows:
𝜂 =
𝐸𝑟
𝐸0
(11)
Where, η is the percentage of the current remaining energy to the total energy.

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b) For the link problem of the node, the change of the link of the node is introduced.
It is necessary to ensure that the MPR set forwards TC messages, only symmetric nodes can be
selected as the MPR set. It is defined as follows:
𝑁𝑐 = 𝑁𝑎𝑑𝑑 + 𝑁𝑑𝑒𝑙 (12)
𝛿 =
𝑁𝑐
𝑁𝑛
(13)
Where 𝑁𝑐 is the number of strictly one-hop symmetric nodes added or decreased within the
interval of the current HELLO message of the node, and 𝑁𝑐 is the current total number of
symmetric nodes of the node. The number of hop symmetric nodes, δ is the change rate of
symmetric nodes, which reflects the stability of node links.
c) The problem of node-link transmission quality [22], the link transmission quality (LTQ)
between nodes is calculated by the message ratio of the HELLO message sent by the neighbour
node within a certain period. To evaluate the ForwardLink (FL) and the value of the quality of
the reverse link, that is, the neighbour link (NL) as follows:
𝐹L =
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑗 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑓𝑟𝑜𝑚 𝑖
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑖 𝑠𝑒𝑛𝑡 𝑡𝑜 𝑗
(14)
𝑁L =
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑖 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑓𝑟𝑜𝑚 𝑗
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑗 𝑠𝑒𝑛𝑡 𝑡𝑜 𝑖
(15)
Where the quasi-MPR node i, the number of HELLO messages that i can obtain is only "the
number of HELLO messages sent by i to j" and "the number of HELLO messages sent by i
received by j".
Then 𝐹𝐿 and 𝑁𝐿 are determined, and LTQ cannot be calculated.
Therefore, the optimized method can be converted into the following method to obtain LTQ.
𝐹L =
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑖 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑓𝑟𝑜𝑚 𝑗
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑖 𝑠𝑒𝑛𝑡 𝑡𝑜 𝑗
(16)
𝑁L =
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑗 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑓𝑟𝑜𝑚 𝑖
𝑁𝑜.𝑜𝑓 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 𝑗 𝑠𝑒𝑛𝑡 𝑡𝑜 𝑖
(17)
LTQ=FL×NL (18)
According to the three variables defined above, it is further defined as the overall impact factor of
MPR determination, which is characterized by weighted calculation. Since the change rate of δ
symmetrical nodes is negatively correlated with the value, the specific expression is:
𝑃L = 𝑎1𝜂 − 𝑎2𝛿 + 𝑎3(average(𝐿TQ2) + 𝐿TQ1) (19)
where a, b, and c are the weight coefficients corresponding to the node attributes, and
𝑎1+𝑎2+𝑎3=1, and the values are adjusted for different directions of the network. 𝐿TQ1 is the link
transmission quality value between the current node and the node performing the MPR set
calculation, and average(𝐿TQ2) is the link transmission quality between the current node and its
strictly one-hop symmetric node (that is, the strict two-hop symmetric node of the node
performing the MPR set calculation) the average of the values.

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According to the 𝑃L value, the candidate MPR nodes are sorted from high to small. The node
with a higher 𝑃L value has higher residual energy, which reflects that its survival time will be
longer, and the link stability and link transmission quality are relatively high. It can be seen that
the candidate nodes with higher 𝑃L values are easier and more suitable to be selected as MPR
nodes. In this paper, the network focuses on the node survival time, so the proportions of a, b, and
c are determined to be 0.4, 0.3, and 0.3, respectively.
3.2. WS-MPR Selection Algorithm
The core idea of the improved algorithm proposed in this paper is: that in in the topology
structure, when node i selects an MPR node, a priority decision relationship is set, and the
priority of node survival status and link stability is greater than the priority of node depth. When
selecting, first determine the size of the 𝑆L value, followed by the node depth, and then select the
MPR in turn. The algorithm flow is shown in Figure 2.
Topology-Based Selection: The WS-MPR Selection Algorithm focuses on choosing MultiPoint
Relays (MPR) within the network topology. When a specific node (denoted as "node i") needs to
select an MPR node, it does so by establishing a priority order based on certain factors.
Priority Criteria: The core idea behind this algorithm is to establish a set of priority criteria for
MPR selection. Two primary factors are considered to determine the priority of MPR nodes:
Node Survival Status: The algorithm prioritizes MPR nodes based on their ability to maintain
network connectivity. This means that nodes with a higher likelihood of staying active and
reliably forwarding messages take precedence.
Link Stability: The stability of communication links is another critical factor. The algorithm
emphasizes selecting MPR nodes that offer stable and dependable connections.
Node Depth: In addition to the aforementioned priority criteria, the algorithm considers the depth
of nodes within the network topology. Node depth represents the number of hops it takes to reach
a specific node. However, in this algorithm, node depth is a secondary consideration, meaning
that it holds less priority than node survival status and link stability.
Selection Process: The actual MPR selection process follows a sequential order. The main steps
are as follows:
Step 1: Initialization: Begin with an empty set to store the selected MPR nodes (Mi).
Step 2: Path Value and Node Depth Calculation: Calculate both the "path value" and the node
depth for all nodes in the one-hop neighbour set (denoted as Q1).
Step 3: Selecting MPRs: Select MPR nodes from Q1 following a specific protocol. Firstly, nodes
with a unique path to a two-hop neighbour are chosen and added to the MPR set (Mi). These
selected nodes are also responsible for covering the nodes in the two-hop neighbour set (Q2).
After this step, the algorithm checks if Q2 is empty; if it is, the selection process ends. Otherwise,
the process continues to the next step.
Step 4: Adding Remaining MPRs: In this step, the algorithm adds nodes from the remaining set
in Q1. It prioritizes nodes with the largest path value. In cases where multiple nodes have the
same path value, the algorithm considers their node depth (D(y)) and selects the one with the
greatest depth. If there are still multiple nodes with equal values, the algorithm proceeds to select
one and removes the nodes it covers from Q2. The process iterates until Q2 becomes empty.

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Figure 2. WS-MPR selection algorithm flow
This algorithm aims to optimize the selection of MPR nodes in the network by prioritizing nodes
that are likely to maintain connectivity and have stable communication links. It also takes into
account node depth as a secondary factor. This approach helps reduce redundancy and
unnecessary network overhead while improving overall network performance and efficiency.
4. RESULT AND DISCUSSION
4.1. Simulation Parameters
This article uses the NS2 simulation software on the Linux platform to set up 5 simulation
scenarios, each node has the same computing power, and the communication range and other
information parameters are also the same. A detailed explanation of the network simulation
parameters as described in Table 1:
Network Simulator: The researchers employed NS2 simulation software with a specific version,
NS 3.29. NS2 (Network Simulator 2) is a widely used open-source network simulation tool for
modelling and analysing the behaviour of computer networks. In this case, NS3.29 was utilized to
set up and run the network simulations.
Operating System: The simulations were conducted on the Ubuntu 18.04 operating system.
Ubuntu is a popular Linux distribution, known for its stability and suitability for various
computational tasks, including network simulation.
Transport Protocol: The transport protocol used for the simulations was UDP (User Datagram
Protocol). UDP is a connectionless and lightweight transport protocol that is often used for
applications where low latency and minimal overhead are required, making it suitable for real-
time and multimedia applications.
Number of Nodes: The simulations involved a variable number of nodes, ranging from 20 to 200.
This parameter explores how the proposed approach performs in networks of different scales,
from relatively small to significantly larger ones.

11
Radio Propagation Mode: The radio propagation mode was set to "Ground, two dimensions."
This mode likely simulates a two-dimensional ground-based radio propagation environment,
which is relevant for terrestrial wireless communication scenarios.
Fixed Speed: The nodes in the simulation had a fixed speed of 25 meters per second (m/sec). This
fixed speed could mimic the movement of nodes in scenarios where vehicles or mobile devices
maintain a consistent speed.
Packet Size: The size of the packets used in the simulations was 512 bytes. Packet size is a
critical parameter, as it affects the efficiency of data transmission and can impact network
performance.
Mobility Model: The mobility model chosen for the simulations was the "Random Waypoint"
model. In this model, nodes move randomly within the simulation area, pausing at waypoints,
which is commonly used to represent the unpredictable movement of mobile devices or vehicles.
Simulation Time: The simulations were run for 200 seconds. This timeframe represents the
period over which the researchers observed and analysed network behaviour and performance.
Simulation Area: The simulation area was defined as 950 meters by 950 meters (950 m x 950 m).
This parameter specifies the spatial extent of the simulated network environment and is important
for understanding network coverage and behaviour in a specific area.
MAC Protocol: The Medium Access Control (MAC) protocol used in the simulations was IEEE
802.11. IEEE 802.11 is a widely adopted standard for wireless local area networks (WLANs) and
is commonly used for wireless communication in various scenarios.
Table 1. Simulation parameters
Parameters Description
Network Simulator NS 3.29
Operation System Ubuntu 18.04
Transport Protocol UDP
Number of Nodes 20-200
Radio Propagation Mode Ground, two dimensions,
Fixed Speed 25 m/sec
Packet Size 512 bytes
Mobility Model Random Waypoint
Simulation Time 200 seconds
Simulation Area 950 m x 950 m
MAC Protocol IEEE 802.11
The operating mechanism of the OLSR protocol determines that its routing overhead is destined
to be relatively large compared with other routing protocols. The routing overhead refers to the
routing cost on the path where the data packet is sent from the source node to the destination
node. The influencing factors are as follows: Protocol-related factors such as line occupancy rate,
data transmission and reception volume, hop count, etc. Different dynamic routing protocols will
choose one or more of the above factors to calculate the routing overhead. The choice here is to
calculate the total sent and received effective data packets as a measure of overhead. Compared
with the traditional OLSR protocol, the WS-OLSR protocol has a small number of nodes (before
80 nodes), that is, when the topology structure and changes are relatively simple, the overhead of
the two routing protocols is very close, and the difference is not large, as shown in Figure 3.

12
When the number of nodes is large and the topology structure and changes are relatively
complex, the MOLSR protocol has obvious advantages. The modified WS-OLSR is aimed at
controlling the sending time of HELLO messages and TC messages through topology changes.
Compared with the original OLSR, the routing overhead of the improved WS-OLSR is reduced
by at least 10%.
Figure 3. Comparison of routing overhead between OLSR protocol and WS-OLSR protocol
4.2. Packet Delivery Rate (PDR)
The Packet Delivery Rate (PDR) is a fundamental metric used to evaluate the performance of
routing algorithms in a network [30] [31]. It quantifies the efficiency of data packet transmission
from a source node to a destination node. This metric is expressed as a ratio, specifically, the
number of data packets successfully received by the destination node divided by the total number
of data packets sent by the source node. The resulting value is typically a fraction between 0 and
1, and it is a crucial indicator of how well a routing algorithm performs in terms of delivering
data reliably.
A Packet Delivery Rate of 1 (or 100%) indicates that every data packet sent from the source node
has successfully reached the destination node. This represents an ideal scenario where no data is
lost in transit, and network performance is at its best.
A Packet Delivery Rate of less than 1 indicates that some data packets were lost or not
successfully delivered. The closer the rate is to 1, the better the network's performance, as it
signifies a higher proportion of successful deliveries.
Packet Delivery Rate is an essential metric for assessing the effectiveness of routing algorithms.
In the context of the research paper, it's used to evaluate the performance of two routing
protocols: OLSR (Optimized Link State Routing) and WS-OLSR (Weighted Sum Optimized
Link State Routing). By comparing the Packet Delivery Rates of these two protocols, the
researchers can determine which one is more efficient in terms of delivering data packets.
Figure 4 in the paper likely presents a graphical comparison of the Packet Delivery Rates of
OLSR and WS-OLSR. Such a comparison allows the researchers to visually assess how these
protocols perform concerning successful data packet delivery. An improvement in the Packet
Delivery Rate, moving it closer to 1, indicates better network performance and more reliable data
transmission, which is a key goal in designing and evaluating routing algorithms for network
communication.

13
(a) (b)
Figure 4. Comparison of packet delivery rates between a) OLSR protocol and b) WS-OLSR protocol
The success rate of the WS-OLSR protocol is much higher than that of the OLSR protocol,
especially when the number of nodes is large and the topology changes are complex. In the
MOLSR protocol, the redundancy of message flooding is reduced on the based on the original
protocol, and message congestion is reduced to a certain extent. In the SL-MPR selection
algorithm, link changes and node energy issues are taken into consideration to ensure improved
link utilization and stability. Simulations show that the packet delivery rate of the protocol is
significantly improved.
4.3. Efficiency of Routing
The efficiency of routing of the protocol here is to count the remaining energy of the fixed node
in multiple experiments and obtain the difference from the initial energy value of the node. It can
be seen from the energy consumption comparison diagram of the protocols in Figure 5 that the
efficiency of routing of the two protocols is almost the same when the number of nodes is small,
but the energy consumption of the WS-OLSR protocol is better than that of the traditional OLSR
protocol when the number of nodes is large. This is because when the topology is complex, the
appropriate MPR node selection reduces the network overhead and prolongs the node survival
time and the adaptive HELLO broadcast message and TC control message flooding can more
effectively reduce the loss of node redundancy. The remaining amount of energy reflects the
survival time of the node.
(a) (b)
Figure 5. Comparison of efficiency of routing between a) OLSR protocol and b) WS-OLSR protocol
While the performance of any protocol or optimization can vary depending on specific scenarios,
there are some considerations on how the enhancements may fare in real-world VANET
applications:
Topology Dynamics: In real-world VANETs, the road network is dynamic, with vehicles
constantly moving, entering, and leaving the network. The proposed optimization scheme,

14
designed to handle frequent topology changes, is expected to perform well in such scenarios by
efficiently adapting to network fluctuations.
Traffic Conditions: The performance of the optimization scheme may vary based on traffic
density, which can significantly impact communication reliability. During congested traffic, the
scheme's ability to optimize message routing is crucial for maintaining connectivity.
Interoperability: Real-world VANETs often involve vehicles from various manufacturers, each
potentially using different communication equipment. The ability of the proposed upgrades to
interoperate seamlessly with a variety of VANET devices is critical for practical success.
Communication Range: VANETs encompass both vehicle-to-vehicle (V2V) and vehicle-to-
infrastructure (V2I) communication. The scheme's ability to adapt to varying communication
ranges and handle communication with roadside infrastructure is a key consideration.
5. CONCLUSION
This paper has effectively tackled the limitations inherent in the original OLSR protocol,
particularly addressing the inflexibility of its broadcast message mechanism and the limited
considerations in its MultiPoint Relays (MPR) selection algorithm. The introduction of an
optimization scheme for OLSR has been pivotal in enhancing the protocol's adaptability during
network communication, with a specific focus on minimizing the adverse effects of frequent
topology changes on its performance. In the realm of optimizing the MPR selection algorithm,
this research has diligently accounted for various influencing factors in the selection of MPR
nodes. The extensive array of simulation experiments conducted has provided substantial
evidence that the refined WS-OLSR protocol outperforms the traditional OLSR protocol,
significantly elevating overall network performance. It is crucial to recognize that communication
and routing in Vehicular Ad-Hoc Network (VANET) environments are intrinsically intricate and
multifaceted. While this paper has concentrated on addressing pivotal facets of these challenges,
we acknowledge that there exists a plethora of other factors that necessitate comprehensive
exploration in future networking research endeavours. As such, future research in the domain of
VANET networking may consider the following directions:
Dynamic Traffic Management: Investigating adaptive mechanisms for managing traffic within
VANETs to optimize routing and reduce congestion.
Security and Privacy Enhancements: Developing robust security measures and privacy
preservation techniques for VANETs, particularly in the context of vehicular communication.
Energy-Efficient Protocols: Exploring energy-efficient routing and communication protocols to
prolong the lifespan of battery-powered vehicles and infrastructure.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
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