ad hoc mobility models indoor and outdoor models pdf

Ad Hoc Mobility Models Indoor And Outdoor Models Pdf

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Mobility models characterize the movements of mobile users with respect to their location, velocity and direction over a period of time. Most of the times simulators play a significant role in testing the features of mobile ad hoc networks. Simulators like NS , QualNet , etc.

Ad Hoc Networks

In Ad-hoc wireless networks, mobility management faces many challenges. Mobility of the nodes causes the network topology to change. The routing protocols must dynamically re-adjust to these changes in order to keep the accurate routes. Therefore, the routing updates traffic overhead is very much high.

Generally, different types of mobility patterns have different impact on the network protocols or applications. Thus, the network performance is strongly affected by the nature of mobility pattern. In this paper, we present a survey of various mobility models in ad-hoc networks. One of the main purpose of this paper is to investigate the impact of the mobility model on the performance of a specific network protocol or application. The results indicate that different mobility patterns affect the various protocols in different ways.

Specifically, the ranking of routing algorithms is influenced by the choice of mobility pattern. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal. Related article at Pubmed , Scholar Google. Research has gained a significant advance in the development of routing protocols for wireless ad hoc networks[1], [2].

A manet consists of a number of mobile devices that come together to form a network as needed, without any support from any existing Internet infrastructure or any other kind of fixed stations. Formally, a manet can be defined as an autonomous system of nodes or MSs also serving as routers connected by wireless links, the union of which forms a communication network modeled in the form of an arbitrary communication graph. In such environment, Neighbor nodes communicate directly with each others while communication between non-neighbor nodes performed via the intermediate nodes which act as routers.

As the network topology changes frequently because of node mobility and power limitations, efficient routing protocols are necessary to organize and maintain communication between the nodes.

In order to thoroughly simulate a new protocol for an ad hoc network, it is imperative to use a mobility model that accurately represents the mobile nodes MNs that will eventually utilize the given protocol. Only in this type of scenario is it possible to determine whether or not the proposed protocol will be useful when implemented.

Currently there are two types of mobility models used in the simulation of networks: traces and synthetic models. Traces are those mobility patterns that are observed in real life systems. Traces provide accurate information, especially when they involve a large number of participants and an appropriately long observation period [3].

However, new network environments e. In this type of situation it is necessary to use synthetic models. Synthetic models attempt to realistically represent the behaviors of MNs without the use of traces.

In this paper, we present several synthetic mobility models that have been proposed for or used in the performance evaluation of ad hoc network protocols.

A mobility model should attempt to mimic the movements of real MNs. Changes in speed and direction must occur and they must occur in reasonable time slots. For example, we would not want MNs to travel in straight lines at constant speeds throughout the course of the entire simulation because real MNs would not travel in such a restricted manner. The mobility model is designed to describe the movement pattern of mobile users, and how their location, velocity and acceleration change over time [4].

Since mobility patterns may play a significant role in determining the protocol performance, it is desirable for mobility models to emulate the movement pattern of targeted real life applications in a reasonable way. Otherwise, the observations made and the conclusions drawn from the simulation studies may be misleading. Thus, when evaluating MANET protocols, it is necessary to choose the proper underlying mobility model. For example, the nodes in Random Waypoint model behave quite differently as compared to nodes moving in groups.

It is not appropriate to evaluate the applications where nodes tend to move together using Random Waypoint model. Therefore, there is a real need for developing a deeper understanding of mobility models and their impact on protocol performance. One intuitive method to create realistic mobility patterns would be to construct trace-based mobility models, in which accurate information about the mobility traces of users could be provided.

However, since MANETs have not been implemented and deployed on a wide scale, obtaining real mobility traces becomes a major challenge. Therefore, various researchers proposed different kinds of mobility models, attempting to capture various characteristics of mobility and represent mobility in a somewhat 'realistic' fashion. Much of the current research has focused on the so-called synthetic mobility models that are not trace-driven.

In the previous studies on mobility patterns in wireless cellular networks, researchers mainly focus on the movement of users relative to a particular area i. However, to model and analyze the mobility models in MANET, we are more interested in the movement of individual nodes at the microscopic-level, including node location and velocity relative to other nodes, because these factors directly determine when the links are formed and broken since communication is peer-to-peer.

The Mobility Model s are mainly categorized into four parts as per shown in the diagram drawn below. A categorization for various mobility models into several classes based on their specific mobility characteristics is provided. For some mobility models, the movement of a mobile node is likely to be affected by its movement history. This type of mobility model is referred as mobility model with temporal dependency. In some mobility scenarios, the mobile nodes tend to travel in a correlated manner.

Such type of mobility models are known as mobility models with spatial dependency. Another class is the mobility model with geographic restriction, where the movement of nodes is bounded by streets, freeways or obstacles. The various categories [4] of mobility models are shown below:.

One frequently used mobility model in MANET simulations is the Random Waypoint model, in which nodes move independently to a randomly chosen destination with a randomly selected velocity. The simplicity of Random Waypoint model may have been one reason for its widespread use in simulations.

Hence, recent research has started to focus on the alternative mobility models with different mobility characteristics. In these models, the movement of a node is more or less restricted by its history, or other nodes in the neighborhood or the environment. In random-based mobility models, the mobile nodes move randomly and freely without restrictions [5].

To be more specific, the destination, speed and direction are all chosen randomly and independently of other nodes. This kind of model has been used in many simulation studies. The Random Waypoint Model :. Soon, it became a 'benchmark' mobility model to evaluate the MANET routing protocols, because of its simplicity and wide availability. This tool is included in the widely used network simulator ns As the simulation starts, each mobile node randomly selects one location in the simulation field as the destination.

It then travels towards this destination with constant velocity chosen uniformly and randomly from [0,Vmax], where the parameter Vmax is the maximum allowable velocity for every mobile node. The velocity and direction of a node are chosen independently of other nodes.

After this duration, it again chooses another random destination in the simulation field and moves towards it. The whole process is repeated again and again until the simulation ends. In the Random Waypoint model, Vmax and Tpause are the two key parameters that determine the mobility behavior of nodes.

If the Vmax is small and the pause time Tpause is long, the topology of Ad Hoc network becomes relatively stable. On the other hand, if the node moves fast i. Random Walk Model :. The Random Walk model was originally proposed to emulate the unpredictable movement of particles in physics. It is also referred to as the Brownian Motion[6]. Because some mobile nodes are believed to move in an unexpected way, Random Walk mobility model is proposed to mimic their movement behavior. The Random Walk model has similarities with the Random Waypoint model because the node movement has strong randomness in both models.

We can think the Random Walk model as the specific Random Waypoint model with zero pause time. The Random Walk model is a memory-less mobility process where the information about the previous status is not used for the future decision. That is to say, the current velocity is independent with its previous velocity and the future velocity is also independent with its current velocity.

Random Direction Model :. In line with the observation that distribution of movement angle is not uniform in Random Waypoint model, the Random Direction model based on similar intuition is proposed by Royer, Melliar-Smith and Mose[7]r. This model is able to overcome the non-uniform spatial distribution and density wave problems. Instead of selecting a random destination within the simulation field, in the Random Direction model the node randomly and uniformly chooses a direction by which to move along until it reaches the boundary.

After the node reaches the boundary of the simulation field and stops with a pause time Tpause, it then randomly and uniformly chooses another direction to travel. This way, the nodes are uniformly distributed within the simulation field. Another variant of the Random Direction model is the Modified Random Direction model that allows a node to stop and choose another new direction before it reaches the boundary of the simulation field.

For both versions of. Mobility of a node may be constrained and limited by the physical laws of acceleration, velocity and rate of change of direction. Hence, the current velocity of a mobile node may depend on its previous velocity. We call this mobility characteristic the Temporal Dependency of velocity.

However, the memoryless nature of Random Walk model, Random Waypoint model and other variants render them inadequate to capture this temporal dependency behavior. Gauss-Markov Mobility Model :. In this model, the velocity of mobile node is assumed to be correlated over time and modeled as a Gauss-Markov stochastic process. The idea is to eliminate the sharp and sudden turns present in the Random Walk and Random Waypoint even by keeping a certain degree of randomness. At fixed intervals of time n new direction dn and speed sn are chosen as:.

In the Gauss-Markov model, the temporal dependency plays a key role in determining the mobility behavior. In the Random Waypoint model and other random models, a mobile node moves independently of other nodes, i. Therefore, the mobility of mobile node could be influenced by other neighboring nodes. Since the velocities of different nodes are 'correlated' in space, thus we call this characteristic as the Spatial Dependency of velocity.

Reference Point Group Mobility Model :. In the RPGM model, each group has a center, which is either a logical center or a group leader node. For the sake of simplicity, we assume that the center is the group leader.

Ad Hoc Networks

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In Ad-hoc wireless networks, mobility management faces many challenges. Mobility of the nodes causes the network topology to change. The routing protocols must dynamically re-adjust to these changes in order to keep the accurate routes. Therefore, the routing updates traffic overhead is very much high. Generally, different types of mobility patterns have different impact on the network protocols or applications. Thus, the network performance is strongly affected by the nature of mobility pattern. In this paper, we present a survey of various mobility models in ad-hoc networks.

Wireless communication technologies have now greatly impact our daily lives. From indoor wireless LANs to outdoor cellular mobile networks, wireless technologies have benefited billions of users around the globe. Hannes Hartenstein and Kenneth P. Chen and S. Frenkiel, B.

mobility models, e.g. random waypoint model, models insufficient to reflect the environmental constraints. In this paper, we evaluate an ad-hoc wireless network​.


Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. In this paper, we evaluate an ad-hoc wireless network and investigate on performance issues in urban area.

The 34 revised full papers presented were carefully reviewed and selected from 46 submissions. The papers provide visions, trends, challenges and opportunities in the area of ad hoc networking and emerging applications. The conference also features two workshops on ad hoc network security and vulnerability, and convergence of wireless directional network systems and software defined networking, respectively. Skip to main content Skip to table of contents.

Show all documents Gerla [] present a large-scale military operation using a combination of unmanned vehicles. Sadasivam and colleagues specify that RWP is not appropriate for the battlefield, and they conduct a comparison of battlefield and rescue operations using RPGM.

Changing Trends in Modeling Mobility

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on-demand routing protocols for mobile ad hoc networks: dynamic source routing DSR , ad hoc on-demand distance vector routing AODV.


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Study on Indoor and Outdoor Environment for Mobile Ad Hoc Network Supported with Request Full-text Paper PDF Manhattan Mobility Model is used for outdoor users and Random Waypoint Model for indoor users [11].



The routing protocols (AODV, DSR, DSDV), mobility models (Random Way Point (RWP), Tactical Indoor Mobility Model (TIMM)) and other aspects are explained.


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