Ad hoc networks connection availability modeling

Trajanov, Dimitar; Filiposka, Sonja; Efnuseva, Marija; Grnarov, A.

doi:10.1145/1023756.1023767

Cited by 15 publications

(18 citation statements)

References 4 publications

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“…It uses numerical validation for the availability of steady state, failure frequency and probability of disconnected nodes to analyze the survivability of a system. [18] proposes a generalized connection availability model based on real measurable parameters that concern the performances of mobile ad-hoc networks on the basis of [16]. The proposed connection availability model can be used for the analysis of the connection availability, or the design of an ad-hoc network that needs to satisfy a given connection availability level.…”

Section: Evaluation Models Of Survivabilitymentioning

confidence: 99%

“…Chen et al [16] propose the continuous time Markov chain (CTMC) to represent a connection availability model for a two-hop ad-hoc network that incorporates the physical faults such as node, power and link faults without considering a variety of faults and connection states for mobile terminals. Koroma et al [17] consider a more complex system with several faults to study the network survivability using CTMC based on the model proposed by Chen et al Trajanov et al [18] propose a connection availability model to analyze the connection availability of MANETs based on the model presented by Chen et al [9] gives a good starting point for the survivability evaluation, but it is difficult to obtain more precise evaluation result. The methods proposed in [10][11][12][13][14][15] are very complicated and they are difficult to be used in practical network environments.…”

Section: Introductionmentioning

confidence: 99%

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Survivability Evaluation in Large-Scale Mobile Ad-Hoc Networks

Peng

Jia

Wang

2009

J. Comput. Sci. Technol.

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Survivability refers to the ability of a network system to fulfill critical services in a timely manner to end users in the presence of failures and/or attacks. In order to establish a highly survivable system, it is necessary to measure its survivability to evaluate the performance of the system's services under adverse conditions. According to survivability requirements of large-scale mobile ad-hoc networks (MANETs), we propose a novel model for quantitative evaluation on survivability. The proposed model considers various types of faults and connection states of mobile hosts, and uses the continuous time Markov chain (CTMC) to describe the survivability of MANETs in a precise manner. We introduce the reliability theory to perform quantitative analysis and survivability evaluation of segment-by-segment routing (SSR), multipath-based segment-by-segment routing (MP-SSR), and segment-by-segment-based multipath routing (SS-MPR) in large-scale MANETs. The proposed model can be used to analyze the network performance much more easily than a simulation-based approach. Numerical validation shows that the proposed model can be used to obtain a better evaluation result on the survivability of large-scale MANETs.

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Section: Evaluation Models Of Survivabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survivability Evaluation in Large-Scale Mobile Ad-Hoc Networks

Peng

Jia

Wang

2009

J. Comput. Sci. Technol.

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“…The model we propose combines and extends theoretical results from [5,14]. The analytical approach consists of a steady-state probability analysis of the availability of a two-hop link connectivity model, combined with a system transient analysis of dropped packets.…”

Section: Introductionmentioning

confidence: 98%

Connection availability and transient survivability analysis in wireless ad-hoc networks

Kalogridis

Lin

2009

Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Network survivability performance provides an assessment of how well a network supports its functionalities in adverse conditions. This paper studies a wireless network survivability scenario, in which ad hoc routing failures occur due to node mobility, and it enhances and validates previously published results in the following manner: a) it analyses ad hoc connection availability with the use of a Markovian model, b) it amends a previously published evaluation of mobile ad hoc router "birth" and "death" rates, c) it validates theoretical results with simulations, and d) it uses a transient model to evaluate the impact of connection availability to higher application layers. The overall survivability model combines a steady-state analysis of the Markovian model and a transient analysis of packets queuing for access to the wireless medium. The main assumption for the Markovian model is that changes in the ad hoc routing status follow an exponential distribution with state transitory rates analogous to the number of available routers. These rates also depend on the network geometry and the node mobility model. Given a "random walk" mobility model, it is shown that the connection availability is independent of the node velocities. This is statistically verified by simulations reinforcing the validity of the theoretical Markovian assumption. The impact of link failures to the survivability of the system is studied by emulating the layers between the data traffic source and the wireless MAC protocol with a buffer and, thereafter, by evaluating the amount and duration of transitory buffer overflows. The presented survivability model could serve as groundwork for further research in ad hoc wireless networks, including ad hoc network performance analysis and QoS optimisation.

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“…When the routing nodes move out of the range, then the packets in transit will either be dropped or delayed and this affects the performance of the network. Another analysis that is given in [9] studies the effect of mobility on the availability of the paths. Both [1] and [9] use a the Continuous Time Markov Model (CTMC) to model the connection availability for a two hop ad hoc network with respect to node mobility and failures.…”

Section: Introductionmentioning

confidence: 99%