Analysis of OLSR Routing Protocol Performance Based on Gauss-Markov Mobility and Random Walk in Mobile Ad-Hoc Network (MANET)

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Journal of Information Technology and Computer Science Volume 7, Number 3, December 2022, pp. 183-195

Journal Homepage: www.jitecs.ub.ac.id

Analysis of OLSR Routing Protocol Performance Based on Gauss-Markov Mobility and Random Walk in Mobile Ad-

Hoc Network (MANET)

Heru Nurwarsito¹, Ervani Sofyana Putra*²

1,2 Brawijaya University, Malang

1heru@ub.ac.id, ²ervanputra9@gmail.com

*Corresponding Author

Received 15 October 2020; accepted 09 Januari 2023

Abstract. MANET is included in the network without infrastructure from a collection of nodes that are interconnected to communicate. Routing protocol performance is a measure of the performance of how the ability of the routing protocol works. A routing protocol is needed where the routing table information must be kept up to date at regular intervals such as Optimized Link State Routing (OLSR). MANET is dynamic, so node mobility increases the risk of node failure.

Gauss-Markov and random walk are mobility that has a significant impact on the performance of the routing protocol. This study analyzes the effect of mobility on the OLSR protocol in MANET topology using Network Simulator 3.25 with packet delivery ratio (PDR) parameters, end-to-end delay, and routing overhead.

The test scenario is done by varying the number of nodes as many as 20, 40, 60, and 80 nodes as well as the minimum and maximum speed of nodes 0-5 m/s, 5- 10 m/s, 10-15 m/s, and 15-20 m/s. PDR results show the highest value on Gauss- Markov with 80 nodes and a minimum and maximum speed of 10-15 m/s of 69.36%. The best end-to-end delay results are seen in Gauss-Markov with 20 nodes and a minimum and maximum speed of 15-20 m/s of 10.22 ms. The routing overhead results display the best value on a random walk with 20 nodes and a minimum and maximum speed of 0-5 m/s of 8100 packets.

1 Introduction

Mobile Ad hoc Network (MANET) is a wireless network without infrastructure consisting of many nodes that are interconnected to communicate [1]. In this network, each node has a role as a router that handles and finds a route to each node in the network. The router will work by sending a data packet through a network to its destination with a process called routing. Routing is used to determine the process of sending messages from the source node to the destination node [2]. In implementing MANET, it is necessary to have a rule or protocol that is useful for managing a route to be used.

MANET has 3 types of protocols which are divided into three parts, namely reactive, proactive, and hybrid protocols [3]. The reactive protocol is a protocol that will work when there is a request for a route change or creating a new route. AODV (Ad Hoc on-Demand Distance Vector), AOMDV (Ad-hoc On-demand Multipath Distance Vector), DSR (Dynamic Source Routing), and TORA (Temporally Ordered

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184 JITeCS Volume 7, Number 3, December 2022, pp 183-195

Routing Algorithm) are included in the types of reactive protocols. The proactive protocol is a routing protocol where the data information in the routing table must be updated regularly. DSDV (Destination-Sequenced Distance Vector), OLSR (Optimized Link State Routing), and BATMAN (Better Approach to Mobile Ad-hoc Networking) are proactive protocol types. The use of routing protocols is to arrange nodes to connect with other nodes in conveying information by connecting nodes and then determining routes on the computer network. In this study, the proactive OLSR routing protocol was used, which is to exchange topological information with other nodes in the network continuously [4]. Each node selects neighboring nodes as a multipoint relay (MPR). In OLSR, the node selected as the MPR must be in charge of continuing the transmission throughout the network [5]. With this mechanism, the number of packets that must be transmitted on the network will decrease [6].

The mobility of nodes in MANET can increase the risk of failure between nodes in connecting which causes disconnected connections and failure in routing which results in the performance of the MANET network [7]. Several types of mobility contained in MANET are Gauss–Markov and random walks. The presence of mobility will add to the challenges of a network, so the use of mobility is an important factor in testing how a network is performing.

The Random Waypoint and Random Direction mobility models with several test parameters such as packet delivery ratio, average end-to-end delay, and routing convergence time [7]. In this study, it can be seen the effect of node mobility on the test parameters.

Based on the explanations that have been presented, studies are using conventional mobility models in MANET. Where such a mobility model provides an unrealistic movement scenario because the movement of nodes is limited to random movements.

The author intends to develop existing research using the OLSR protocol with the Gauss–Markov mobility model and the random walk. The selection of Gauss–Markov node mobility is based on a study in that Gauss–Markov mobility approaches the movement of mobile nodes in the real world compared to other mobility [8]. The selection of random walk mobility is based on Performance Analysis of MANET Routing Protocols that Gauss–Markov has almost the same characteristics as a random walk, namely when the initial velocity is used as a parameter to determine the speed and direction of the movement of the next node [9].

2 Literatur Review

2.1 Mobile Ad Hoc Network (MANET)

MANET as shown in Figure 1, consists of a collection of each node that can communicate with each other with capabilities without using a predetermined infrastructure or using centralized administration [10]. Activities that occur in the MANET network are carried out by each node itself, including sending messages and making topologies. In a MANET network, it has another character, namely a router that is useful so that nodes can search for routes and send data packets quickly and efficiently.

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…185 2.2 Optimized Link State Routing (OLSR)

This protocol operates using a table-driven so this protocol is entered into a proactive type, where the exchange of topology information with other nodes on the network is carried out regularly. OLSR is designed to work in conditions that are always in motion, and do not require a centralized setting [11]. The OLSR protocol is particularly suitable for large and congested cellular networks. OLSR can reduce the overhead on network traffic by using a multipoint relay (MPR) which is the main key in the OLSR protocol.

Figure 1. MANET Network Topology

Nodes that have been selected as MPR by several neighboring nodes will periodically announce this information in their control messages [12]. That way a node announces to the network that it has reached other nodes that have selected it as MPR.

MPR is used to form a route from the sending node to the destination node in the network [13].

2.3 Mobility of Gauss–Markov

Gauss–Markov mobility (GMM) is used to simulate a Personal Communication System network that is used to simulate an ad-hoc network. The way the Gauss–Markov mobility works is by using the time division for message delivery, where the movement of the nodes will vary based on the speed of message delivery or the movement of the node[14]. In determining the movement of the nodes, equation 1 is used.

𝑆𝑛 = 𝛼𝑆𝑛 − 1 + (1 − 𝛼)𝑆 + √(1 − α2)Sxn − 1 (1) Information:

Sn: initial speed

α: the parameter used to determine the random value of the movement that is 0 ≤ α

≤ 1.

𝑠: a variable value that is constant, which is the average speed and direction of motion for a node.

If the value in the α variable is smaller, the node movement will be more random, whereas when the value is greater in the α variable, the node movement will approach the movement pattern in the random waypoint model [15]. It is shown in Figure 2.

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Figure 2. The Gauss–Markov Movement

2.4 Mobility of Random Walk

Random Walk is a node mobility process that does not require storing information about the status of previous nodes to influence the calculation of the number of nodes in the message-sending process. There are characteristics of the random walk mobility model, a node randomly and evenly chooses a direction ϑ (t) in a predetermined range from 0 to 2π and the velocity Vt between 0 and Vmax to move to a new location. After the time interval t, the new direction and velocity are computed and assigned to the node. If the node reaches the network coverage limit (simulation area limit), then the node will reverse direction with an angle ϑ (t) or π-ϑ (t) [8]. In a random walk, the speed of the current node does not depend on the speed of the previous node so this mobility is memoryless, as in Figure 3. This difference can be overcome in Gauss–Markov mobility [9].

Figure 3. The Random Walk Movement

3 Methodology

3.1 Topology Design

There is a topology that is expected to have a good structure, then it is run based on the number of nodes that have been determined with minimum and maximum speed variations, in Figure 4 as MANET Topology Design. Each node is executed and then communicates using the Optimized Link State Routing (OLSR) protocol and moves in sending packets. Where the movement of the nodes is given a mobility model such as Gauss–Markov and random walks, then all variations in the number of nodes will be carried out with a limited area. Then using 802.11b is a standard using wifi in

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…187 transmitting packets when nodes meet.

Figure 4. MANET Topology Design

3.2 Simulation Parameter Design

The simulation will be carried out in the network simulator 3 application and the simulation parameters used are shown in table 1 below.

Table 1. Simulation Parameters

Simulation Parameters Value

Simulation Time (seconds) 600 seconds

Routing Protocol Optimized Link State Routing (OLSR)

Mobility Model Gaussmarkov, Random Walk

Minimum and maximum node speed (m/s) 0 - 5 m/s, 5 - 10 m/s, 10 - 15 m/s, 15 - 20 m/s

Number of Nodes 20, 40, 60, and 80 Nodes

Data Package Size 1024 bytes

Simulation Area 1000x1000 meters

3.3 Testing Scenarios

The test scenario in this study is carried out based on two mobility with the parameters used in the form of packet delivery ratio (PDR), end-to-end delay, and routing overhead such as:

1. Performed on the Gauss–Markov mobility run with variations in the number of nodes 20, 40, 60, and 80 nodes then each node is given a variation of the minimum and a maximum speed of the node 0-5m/s, 5-10m/s, 10-15m/s and 15- 20m/s using the OLSR protocol, which is run in a simulation area with an area of 1000 × 1000 for 600 seconds.

2. Performed on random walk mobility run with variations in the number of nodes 20, 40, 60, and 80 nodes then each node is given a variation of the minimum and a maximum speed of nodes 0-5m/s, 5-10m/s, 10-15m/s and 15-20m/s using the OLSR protocol, which is run in a simulation area with an area of 1000 × 1000

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for 600 seconds.

There are several steps in carrying out system testing. This step can be seen in Figure 5:

Figure 5. Testing Diagram

4 Result and Discussion

4.1 Based on Parameter Packet Delivery Ratio (PDR)

Figure 6, testing 20 nodes with a speed of 0-5 m/s, the PDR value of Gauss–Markov results in a value of 56.24%, and random walk results in a value of 44.63%. With a speed of 5-10 m/s, the Gauss–Markov PDR value increased to 58.33%, and random walks decreased to 32.93%. With a speed of 10-15 m/s, the Gauss–Markov PDR value decreased to 56.87%, and the random walk again decreased to 28.44%. With a speed of 15-20 m/s, the PDR value of Gauss–Markov decreased to 53.85%, random walks decreased to 37.77%.

Figure 7, testing 40 nodes with a speed of 0-5 m/s, the PDR value of Gauss–Markov produces a value of 54.49%, and random walk results in a value of 37.91%. With a speed of 5-10 m/s, the PDR value at Gauss–Markov increased to 61.32%, random walks increased to 39.37%. With a speed of 10-15 m/s, the Gauss–Markov PDR value again increased to 64.63%, random walks again decreased to 38.96%. With a speed of 15-20 m/s, the Gauss-Markov PDR value decreased to 57.38%, and the random walk decreased slightly to 38.71%.

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…189

Figure 6. PDR Performance Graph on 20 Node Testing

Figure 8, testing 60 nodes with a speed of 0-5 m/s, the PDR value of Gauss-Markov produces a value of 58.53% and random walk results in a value of 39.10%. With a speed of 5-10 m/s, the Gauss-Markov mobility PDR value increased to 64.37%, while random walks decreased by 38.71%. With a speed of 10-15 m/s, the Gauss-Markov PDR value has increased to 64.71%, and random walks have increased to 40.45%. With a speed of 15-20 m/s, the Gauss-Markov PDR value decreased to 61.77%, random walks increased to 47.16%.

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Figure 9, testing 80 nodes with a speed of 0-5 m/s, the Gauss-Markov PDR value produces a value of 62.84% and random walk results in a value of 42.54%. With a speed of 5-10 m/s, the Gauss-Markov PDR value increased to 67.76%, random walks decreased by 41.34%. With a speed of 10-15 m/s, the Gauss-Markov PDR value increased to 69.36%, random walks increased to 42.39%. With a speed of 15-20 m/s, the Gauss-Markov PDR value decreased to 63.73%, random walks increased to 48.28%.

From the test of the Gauss-Markov and random walk mobility models, the highest value is obtained at the Gauss-Markov with a variation of the number of 80 nodes and the minimum and maximum speed of 10-15 m/s the PDR value obtained is 69.36%.

4.2 Based on End To End Delay Parameters

Figure 10, testing 20 nodes with a speed of 0-5 m/s, the end-to-end delay value of Gauss-Markov produces 20.77 ms, and random walk results in 26.68 ms. With a speed of 5-10 m/s, the end-to-end delay value of Gauss-Markov has increased to 20.97 ms, and random walks have decreased by 25.73 ms. With a speed of 10-15 m/s, the Gauss- Markov value of the end-to-end delay has increased to 35.04 ms, the random walk has decreased to 19.21 ms. With a speed of 15-20 m/s the end-to-end delay value of Gauss- Markov decreased to 10.22 ms, random walk increased to 27.53 ms.

Figure 10. Graph of End To End Delay Performance on 20 Node Testing

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…191 Figure 11, testing 40 nodes with a speed of 0-5 m/s, the end-to-end delay value of Gauss-Markov produces 30.38 ms, and random walk results in 25.01 ms. With a speed of 5-10 m/s, the end-to-end delay value of Gauss-Markov decreased to 28.82 ms, random walk increased by 50.47 ms. With a speed of 10-15 m/s, the Gauss-Markov value of the end-to-end delay has increased to 35.21 ms, the random walk has decreased to 35.54 ms. With a speed of 15-20 m/s the end-to-end delay value of Gauss-Markov decreased to 31.82 ms, random walk decreased to 19.74 ms.

Figure 12, testing of 60 nodes with a speed of 0-5 m/s, the end-to-end delay value of Gauss-Markov produces 32.20 ms, random walk results in 48.52 ms. With a speed of 5-10 m/s, the end-to-end delay value of Gauss-Markov has increased to 49.57 ms, random walk occurs at 63.52 ms. With a speed of 10-15 m/s, the Gauss-Markov value of the end-to-end delay has decreased to 38.25 ms, the random walk has decreased to 41.67 ms. With a speed of 15-20 m/s the end-to-end delay value of Gauss-Markov increases to 42.80 ms, and the random walk has an increase to 47.84 ms.

Figure 13, testing 80 nodes with a speed of 0-5 m/s, the end-to-end delay value of Gauss-Markov produces 53.79 ms, and random walk produces 63.85 ms. With a speed of 5-10 m/s, the value of the end-to-end delay of the Gauss-Markov mobility decreased

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to 39.07 ms, while the random walk decreased to 39.86 ms. With a speed of 10-15 m/s, the mobility of the Gauss-Markov value of the end-to-end delay decreased to 34.61 ms, random walk increased to 63.70 ms. With a speed of 15-20 m/s, the Gauss-Markov value of the end-to-end delay increased to 44.82 ms, random walk decreased to 60.89 ms.

From the test of the Gauss-Markov and random walk mobility models, the best value is obtained at the Gauss-Markov with a variation of the number of 20 nodes and the minimum and maximum speed of 15-20 m/s, the value of the end to end delay is 10.22 ms.

4.3 Based on Routing Overhead Parameters

Figure 14, testing 20 nodes with a speed of 0-5 m/s, the Gauss-Markov routing overhead value results in 8150 packets, and random walks produce 8100 packets. With a speed of 5-10 m/s, the Gauss-Markov routing overhead value increased by 8441 packets, and random walks also increased by 8134 packets. With a speed of 10-15 m/s, the Gauss-Markov routing overhead value increased by 8625 packets, and random walks decreased by 8123 packets. With a speed of 15-20 m/s, the Gauss-Markov routing overhead value decreased by 8476 packets, and random walks increased by 8392 packets.

Figure 14. Graph of Routing Overhead Performance on 20 Node Testing

Figure 15, testing 40 nodes with a speed of 0-5 m/s, the value of the Gauss-Markov

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…193 routing overhead results in 17,630 packets, and random walk results in 17946 packets.

With a speed of 5-10 m/s, the Gauss-Markov routing overhead value increased by 17927 packets, and random walks decreased by 17622 packets. With a speed of 10-15 m/s, the Gauss-Markov routing overhead value increased by 18106 packets, and random walks decreased by 17,619 packets. With a speed of 15-20 m/s, the Gauss- Markov routing overhead value has increased by 18294 packets, and random walks have increased by 18050 packets.

Figure 15. Graph of Routing Overhead Performance on 40 Node Testing

Figure 16, testing 60 nodes with a speed of 0-5 m/s, the Gauss-Markov routing overhead value results in 27123 packets and random walks generate 26995 packets.

With a speed of 5-10 m/s, the Gauss-Markov routing overhead value increased by 27136 packets, and random walks decreased by 26695 packets. With a speed of 10-15 m/s, the Gauss-Markov routing overhead value increased by 28,195 packets, and random walks increased by 26934 packets. With a speed of 15-20 m/s, the Gauss- Markov routing overhead value has increased by 28304 packets, and random walks have increased by 27278 packets.

Figure 17, testing 80 nodes with a speed of 0-5 m/s, the value of the Gauss-Markov routing overhead produces 36713 packets, and random walks generate 36557 packets.

With a speed of 5-10 m/s, the Gauss-Markov routing overhead value increased by 36950 packets, and random walks increased by 36627 packets. With a speed of 10-15

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m/s, the Gauss-Markov routing overhead value has increased by 38020 packets, and random walks have increased by 36732 packets. With a speed of 15-20 m/s, the Gauss- Markov routing overhead value increased by 38382 packets, and random walks decreased by 36601 packets.

From the test of the Gauss-Markov and random walk mobility model, the best value is obtained on the random walk with a variation of the number of 20 nodes and the minimum and maximum speed of 0-5 m/s, the routing overhead value obtained is 8100 packets.

5 Conclusion

Based on the test and then continued with the analysis that has been done, it can be concluded the implementation of Gauss-Markov mobility and random walk mobility with the OLSR protocol in the MANET network successfully runs according to the scenario seen in the performance of the two mobility based on the parameters used such as packet delivery ratio (PDR), end-to-end delay, and routing overhead with the scenario consists of variations in the number of nodes with variations in the speed of the minimum and maximum nodes.

OLSR performance based on packet delivery ratio (PDR) parameters obtained the highest value in Gauss-Markov with a variation of the number of 80 nodes and minimum speed and a maximum speed of 10-15 m/s PDR value obtained is 69.36%.

Based on the end-to-end delay parameter, the best value for Gauss-Markov is obtained with a variation of the number of 20 nodes and the minimum and maximum speed of 15-20 m/s, the end-to-end delay value obtained is 10.22 ms. Based on the routing overhead parameter, the best value is obtained on a random walk with a variation of the number of 20 nodes and a minimum and maximum speed of 0-5 m/s, the routing overhead value obtained is 8100 packets.

Recommendations

Suggestions that can be recommended for further research with a comparative discussion using Gauss-Markov mobility and random walk in MANET are further

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Heru & Ervani, Analysis of OLSR Routing Protocol Performance…195 research can be developed using several scenarios or more diverse parameters such as the use of different types of traffic, the use of different mobility, or the use of different protocols. It is necessary to research different simulators to test the performance of the protocol. Further research can be developed using a different network topology such as the VANET topology.

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