Mobility Models for Delay Tolerant Network: A Survey

Shahzamal, M; Parvez, M F; Zaman, Majid; Hossain, M D

doi:10.5121/ijwmn.2014.6410

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…One of the features that makes the simulation more realistic is the model that governs the movement of the nodes in the vehicular network, providing coordinates, speeds, and pause times for the nodes. Popular models include random waypoint, map-based movement, and shortest path map-based movement [34]. We used the latter for the simulation, which constrains the node movement to predefined paths, using Dijkstra's shortest path algorithm to find its way through the map area.…”

Section: Mobility Modelmentioning

confidence: 99%

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

2019

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The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.

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Section: Mobility Modelmentioning

confidence: 99%

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

2019

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“…Nodes shall have to buffer messages for a long time and in case of network congestion they have to decide which messages to drop from its queue. In this section we describe the different queuing policies [18] used in this paper for the evaluation in Section 5.…”

Section: B Uffer Management and Queuing Policiesmentioning

confidence: 99%

Evaluating Joint Routing and Scheduling in Opportunistic Sensor Network Under Levy Walk Nature of Human Mobility Model

2015

IJAERD

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“…In [11] the authors have proposed the human mobility model called truncated levy walk [12]. The different traces have been generated by changing the value of parameters called the levy exponent α and β so that proposed model fit to the actual movement patterns of human beings.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Joint Routing and Scheduling in Opportunistic Sensor Networks

Roohinaz¹,

Shah²

2015

IJCA

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In present scenario the innovation of mobile phones that are embedded with different types of onboard sensors, has brought on a new interest of using them as the main part of the sensor network, for a wide area of applications (for e.g. monitoring). In such ad-hoc network end to end connectivity cannot be established and the connection comes out to be mainly opportunistic. The connection in Opportunistic Sensor Network (OSN) is many to one i.e. all the nodes have a common destination. The prominent challenge faced in the opportunistic sensor networks includes the Routing /Scheduling and managing the constrained memory. In this paper scheduling is defined by routing combined with proper buffer management policy and since the scheduling in OSN is node based so selection of proper Buffer management is also significant. This paper considers the monitoring application with the help of human carried mobile sensors and hence a performance evaluation of joint Routing/Scheduling and Buffer management using simulator called ONE (Opportunistic Network Environment) Simulator is done.Simulation results indicate superiority of Shortest remaining Life Time (SHLI) scheme for node movement as per human mobility characteristics. This is contrary to community mobility model as shown for PROPHET algorithm, in which Most Forwarded First (MOFO) scheme performs better.

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Mobility Models for Delay Tolerant Network: A Survey

Abstract: Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in

Cited by 19 publications

References 28 publications

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

Evaluating Joint Routing and Scheduling in Opportunistic Sensor Network Under Levy Walk Nature of Human Mobility Model

Performance Evaluation of Joint Routing and Scheduling in Opportunistic Sensor Networks

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