Models and Methodologies for Simulating Mobile Ad- Hoc Networks

Sridhara, Vinay; Kim, Jong-Hyun; Bohacek, Stephan

doi:10.1109/wirles.2005.1549381

Cited by 2 publications

(5 citation statements)

References 5 publications

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“…The last attribute of the propagation model is diffraction. In our and other's work, it has been shown that diffraction plays an important role in wireless communication in urban areas [25]. Diffraction allows the transmission to curve over the top of buildings as well as around corners.…”

Section: Simulation Methodologymentioning

confidence: 77%

“…These simulations utilize a technique that uses ray-tracing and vertical plane rays [12] as well as several other techniques to increase the computational efficiency. Detailed discussion of the propagation model along with validation is provided in [25]. In brief, the simulation models the fact that wireless signals may reflect off of buildings and the ground.…”

Section: Simulation Methodologymentioning

confidence: 99%

“…Platoons are of interest to mesh networks since they lead to heterogeneous node distribution. The complete methodology for realistic simulation of the mobile Ad-Hoc networks could be found in [25].…”

Section: Simulation Methodologymentioning

confidence: 99%

“…To this end, realistic wireless propagation and realistic mobility models are utilized. The propagation is based on 3-D ray-tracing [25] that includes reflection, transmission, and diffraction. Propagation through buildings is modeled with the attenuation factor model [17], [24].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the propagation simulation is well suited for modeling urban propagation. Indeed, the tool has been validated with propagation measurements in urban areas [25].…”

Section: Introductionmentioning

confidence: 99%

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Performance of urban mesh networks

Sridhara

Kim

Bohacek

2005

Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Currently, large-scale deployments of mesh networks are being planned for Philadelphia as well as other cities. The performance of such networks has never been examined through simulation or through any other means. In this paper we perform detailed simulations of mesh networks in several urban environments and evaluate the performance of these networks. The simulations utilize realistic ray-tracing and other propagation models. The mobility of nodes is based on models derived from several movement and time use surveys including the US Department of Labor's recent time use study that includes travel diaries from over 20,000 people. Basic performance issues such as connectivity, capacity, and several application oriented performance metrics as a function of the density infrastructure (base stations and fix wireless relays) are examined. It is found that a high density infrastructure is required to achieve reasonable coverage, in particular, the density must be higher than is currently considered by most deployments. While allowing mobile nodes to act as relays improves coverage, it does not necessarily improve the performance received by the application. It is found that in general, there is a significant difference between the fraction of nodes that are able to communicate with a base station and the fraction of nodes that received acceptable application layer performance.

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Section: Simulation Methodologymentioning

confidence: 77%

Section: Simulation Methodologymentioning

confidence: 99%

Section: Simulation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…As a result, the propagation simulation is well suited for modeling urban propagation. Indeed, the tool has been validated with propagation measurements in urban areas [25].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance of urban mesh networks

Sridhara

Kim

Bohacek

2005

Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Selection Metrics for Cooperative Multihop Relaying

Kim

Bohacek

2006

Challenges in Ad Hoc Networking

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Cooperative relaying enables nodes to actively cooperate to deliver packets to their destination. The bestselect protocol (BSP) implements a type of cooperative relaying that generalizes single path routing with sets of nodes (relay-sets) replacing the concept of a single node relay. Thus, while in traditional single path routing, packets hop from node to node, in BSP, packets hop from relay-set to relay-set. Through the exchange of channel gain information between relay-sets, the best node within a relay-set is selected to transmit the data packet on behalf of the entire relay-set. The node selected depends on the metric used. Any metric that can be posed in a dynamic programming framework can be used. In this paper, performance gains from a number of selection metrics are investigated. Specific selection metrics include maximizing the minimum channel gain along the path, minimizing end-to-end delay, minimizing the total power, and minimizing the total energy. It will be shown that BSP can achieve significant gains in all of these metrics.

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Models and Methodologies for Simulating Mobile Ad- Hoc Networks

Cited by 2 publications

References 5 publications

Performance of urban mesh networks

Performance of urban mesh networks

Selection Metrics for Cooperative Multihop Relaying

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