Computer Network Simulation Using NS2

Nayak, Ajit Kumar; Champati, Satyananda; Mall, Rajib

doi:10.1201/9781315367286

Cited by 14 publications

(5 citation statements)

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“…Throughput didefinisikan sebagai jumlah total byte yang diterima oleh tujuan per detik. Untuk menghitung ini, digunakan rumus sebagai berikut [13]: Analisa Throughput Berdasarkan grafik pada gambar 5, nilai throughput yang didapatkan memiliki perbedaan yang kecil pada jumlah node yang sama dengan ketinggian 0, 5 dan 10 meter sedangkan pada ketinggian 15 meter nilai throughput yang didapatkan dengan jumlah node 121 memiliki perbedaan cukup besar dikarenakan besarnya routing overhead pada formasi random dengan ketinggan PAN Coordinator 15 meter. Nilai throughput semakin menurun seiring dengan bertambahnya jumlah node.…”

Section: Throughputunclassified

Pemodelan Dan Simulasi Pengaruh Posisi Dan Ketinggian Nodal Sensor Terhadap Quality of Services (QoS) Jaringan Sensor Nirkabel Menggunakan Protokol Zigbee

Bhoka Tola,

Doo,

Pollo

2023

JTEIN

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Jaringan Sensor Nirkabel diimplementasikan pada area yang cukup luas dengan jumlah node sensor yang banyak. Sehingga lebih baik dilakukan pemodelan dan simulasi sebelum diimplementasikan pada kondisi sebenarnya. Salah satu protokol yang sesuai pada jaringan sensor nirkabel yaitu Protokol Zigbee. Penelitian ini bertujuan untuk mengetahui pengaruh posisi dan ketinggian nodal sensor terhadap Quality of Services. Pengujian dilakukan dengan mengubah posisi node sensor secara grid dan random dengan ketingian PAN Coordinator yang divariasikan menggunakan software Network-Simulator-2 kemudian melakukan perhitungan paremeter routing overhead, throughput, delay dan packet loss dari tracefile hasil simulasi menggunakan program AWK. Hasil pengujian menunjukan pada pengaruh posisi terhadap QoS dengan jumlah node 81 untuk semua ketinggian memiliki performa QoS lebih baik pada posisi grid. Pada posisi grid dengan ketinggian 0 meter diperoleh nilai pada routing overhead 45,84 byte, throughput 67,53 kbps, delay 35,85 ms dan packet loss 33,38 %. Sedangkan posisi random diperoleh routing overhead 53,77 byte, throughput 63,83 kbps, delay 37,36 ms dan packet loss 38,14 %. Pada pengaruh ketinggian, diperoleh hasil QoS yang bervariasi yaitu pada ketinggian 5 meter, diperoleh nilai pada routing overhead 45,82 byte, throughput 69,53 kbps, packet loss 38,53%, dan delay 42,49 ms. Pada ketinggian 10 meter, diperoleh nilai pada routing overhead 38,11 byte, throughput 68,67 kbps, packet loss 27,03%, dan delay 39,49 ms. pada ketinggian 15 meter, diperoleh nilai pada routing overhead 59,07 byte, throughput 59,78 kbps, packet loss 40,56% dan delay 36,82 ms. Dengan demikian dapat disimpulkan bahwa posisi node dan ketinggian PAN Coordinator dapat mempengaruhi kinerja QoS pada jaringan sensor nirkabel.

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Section: Throughputunclassified

Pemodelan Dan Simulasi Pengaruh Posisi Dan Ketinggian Nodal Sensor Terhadap Quality of Services (QoS) Jaringan Sensor Nirkabel Menggunakan Protokol Zigbee

Bhoka Tola,

Doo,

Pollo

2023

JTEIN

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“…In practice, it is difficult to construct a real network environment, so NS2 is an effective experimental tool for network researchers and has been applied to many network studies. Using NS2 for computer network simulation provides a solid foundation for computer network knowledge and skills, covering everything from simple operating system commands to complex network performance indicator analysis [34]. We performed a lot of simulations under different traffic scenarios in NS2 to fully evaluate the performance of our method.…”

Section: A Simulation Scenarios Setupmentioning

confidence: 99%

Network Topology Inference Based on Subset Structure Fusion

Fei

Zhai

et al. 2020

IEEE Access

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Network topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topology in the non-stationary network due to the variability of traffic distribution. In this paper, we present a novel network topology inference method based on subset structure fusion for accurate topology inference in the non-stationary network. First, we propose an end-to-end measurement method named threepacket to accurately probe the three-leaf-nodes subset structures of the network without the assumption that the packet delay or loss follows a stable distribution. Second, we propose a metric for the shared path length based on the structural characteristics of the subset structures to fuse these subset structures into a correct complete topology. The analytical and simulation results show that our method is more applicable for topology inference in the non-stationary network compared with the existing methods. INDEX TERMS End-to-end measurement,network tomography,non-stationary network,subset structure fusion,topology inference.

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“…To evaluate the performance of our method in practical network environments, we first use NS2 to construct a network experimental environment. In practice, since constructing a real network experiment environment is very difficult, NS2 is regarded as an effective experimental tool for network researchers and has been applied to many network studies [46]. Especially for network topology inferences, it is a difficult task to find a real network whose topology is known and for which the corresponding traffic scenarios can be easily set up.…”

Section: A Simulation Scenario Setupmentioning

confidence: 99%

Network Topology Inference Using Higher-Order Statistical Characteristics of End-to-End Measured Delays

Fei

Wen

et al. 2020

IEEE Access

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Network topology is important information for many network control and management applications. Network tomography infers network topology from end-to-end measured packet delays or losses, which is more feasible than internal cooperation-based methods and attracts many studies. Most of the existing methods for network topology inference usually function under the assumption that the distribution of packet delay or loss follows a given distribution (e.g., Gaussian or Gaussian mixture), and they estimate network topology from the parameters of the given distribution. However, these methods may fail to obtain an accurate estimation because the real distribution of packet delay or loss usually cannot be described by a certain distribution. In this paper, we present a novel network topology inference method based on the unicast end-to-end measured delays. The method abandons the assumption of packet delay distribution and constructs network topology by inferring the higher-order cumulants of internal links from the end-to-end measured delays. The analytical and simulation results show that the proposed method offers over 10% improvement in accuracy compared with that of the state-of-the-art works. INDEX TERMS Higher-order cumulant, topology inference, end-to-end measurement, network tomography.

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Computer Network Simulation Using NS2

Cited by 14 publications

References 0 publications

Pemodelan Dan Simulasi Pengaruh Posisi Dan Ketinggian Nodal Sensor Terhadap Quality of Services (QoS) Jaringan Sensor Nirkabel Menggunakan Protokol Zigbee

Pemodelan Dan Simulasi Pengaruh Posisi Dan Ketinggian Nodal Sensor Terhadap Quality of Services (QoS) Jaringan Sensor Nirkabel Menggunakan Protokol Zigbee

Network Topology Inference Based on Subset Structure Fusion

Network Topology Inference Using Higher-Order Statistical Characteristics of End-to-End Measured Delays

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