Simulation and Evaluation of Different Mobility Models in Disaster Scenarios

Walunjkar, Gajanan; Rao, Anne Koteswara

doi:10.1109/rteict46194.2019.9016893

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Walunjkar and Rao [40] presented a study on the simulation and evaluation of different mobility models in disaster scenarios. The authors considered three mobility models, which were random waypoint (RWP), random walk (RW), and Gauss-Markov, and evaluated their performance in terms of network throughput, E2E delay, and PDR.…”

Section: Conceição and Curadomentioning

confidence: 99%

“…However, previous works in this field have largely ignored or simplified the representation of these areas, limiting the realism of the mobility models [43,44]. Additionally, the evaluation of network performance has often been conducted with small numbers of nodes and limited tactical network scales, further hindering the applicability of existing models [40].…”

Section: Conceição and Curadomentioning

confidence: 99%

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DRT mobility model for search and rescue operations based on catastrophic intensity to improve the quality of services

2024

IJATEE

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Section: Conceição and Curadomentioning

confidence: 99%

Section: Conceição and Curadomentioning

confidence: 99%

DRT mobility model for search and rescue operations based on catastrophic intensity to improve the quality of services

2024

IJATEE

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“…In Manhattan Grid Model nodes move only on predefined paths. In disaster mobility model, disaster area is divided into different zones such as incident zone, casualties treatment zone, transport area and hospital zone (Walunjkar & Rao, 2019a) etc. Figure 1 shows the division of zones in disaster area scenario.…”

Section: Introductionmentioning

confidence: 99%

Disaster Relief Management Using Reinforcement Learning-Based Routing

Walunjkar

Rao

2021

International Journal of Business Data Communications and Networking

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Effective disaster management is required for the peoples who are trapped in the disaster scenario but unfortunately when disaster situation occurs the infrastructure support is no longer available to the rescue team. Ad hoc networks which are infrastructure-less networks can easily deploy in such situation. In disaster area mobility model, disaster area is divided into different zones such as incident zone, casualty treatment zones, transport areas, hospital zones, etc. Also, in order to tackle high mobility of nodes and frequent failure of links in a network, there is a need of adaptive routing protocol. Reinforcement learning is used to design such adaptive routing protocol which shows good improvement in packet delivery ratio, delay and average energy consumed.

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“…In the event of a disaster, the temporary connectivity should be provided to victims through the aerial backbone network (ABN) organized by many aerial nodes [7] [31]. As first and foremost step, the aerial node should rapidly cover whole disaster area to search and rescue (SAR) each victim [32][33][34]. In fact, the actual coverage efficiency may be influenced by some inevitable factors (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Authentic 3D Mobility Model Based on Spiral Line for Aerial Backbone Network

Sun

Shi

2020

IEEE Access

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For search and rescue (SAR), the dynamic connectivity issue caused by mobility of aerial node will severely influence the actual performance in aerial backbone network (ABN). To capture true aerial mobility feature, some application-oriented aerial mobility model (AMM) has been developed. However, existing 3D AMM cannot satisfy some specific demands for in SAR, especially smooth 3D trajectory and rapid connectivity coverage. In this paper, we propose authentic 3D mobility model based on spiral line (3D SLMM), such as Z-Or-SLMM and Z-Non-SLMM. Firstly, we review the basic concept of 2D SLMM. Then, we develop authentic 3D SLMM based on 2D SLMM. Meanwhile, we validate synthetic trajectory of 3D SLMM through the real traces. In the further, we investigate some mathematical features of 3D SLMM, such as mathematical expression and the distribution of spatial nodes. Finally, we evaluate some key mobility property of 3D SLMM. The result shows that Z-Or-SLMM and Z-Non-SLMM all have specific mathematical expression and uniform distribution of spatial nodes at steady status. Besides, the Z-Or-SLMM and Z-Non-SLMM all possess authentic and smooth 3D trajectory. Specially, the aerial node in Z-Or-SLMM merely takes 1.5 minutes to cover 100% target region of 200m*200m. We believe that the 3D SLMM can be directly deployed in the actual scene, and provide many helpful and credible guidelines in the design and analysis of realistic ABN.

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Simulation and Evaluation of Different Mobility Models in Disaster Scenarios

Cited by 9 publications

References 9 publications

DRT mobility model for search and rescue operations based on catastrophic intensity to improve the quality of services

DRT mobility model for search and rescue operations based on catastrophic intensity to improve the quality of services

Disaster Relief Management Using Reinforcement Learning-Based Routing

The Authentic 3D Mobility Model Based on Spiral Line for Aerial Backbone Network

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