Efficient way of implementing the random and GM  (Gauss-Markov) mobility model in MANET

Nagaraju, M; Rao, K Raghava; Vijayalakshmi, K

doi:10.14419/ijet.v7i2.7.10595

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The planning of the movements is defined by mathematical equations, in which the nodes are forced to follow these movement patterns [ 8 ]. Similarly, time-dependent mobility models are based on mathematical equations in which the nodes depend on an initial time with respect to a previous time, thus avoiding sudden changes in speed and direction [ 9 ]. Different prediction schemes can be used to control GM.…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

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The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability.

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Section: Introductionmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The planning of the movements is defined by mathematical equations, where the nodes are forced to follow these movement patterns [8]. Similarly, time-dependent mobility models are based on mathematical equations where the nodes depend on an initial time with respect to a previous time, thus avoiding sudden changes in speed and direction [9].…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2023

Preprint

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According to the cognitive radio (CR) paradigm, the efficient use of the radio spectrum is achieved by performing four main functions: spectral perception, spectral mobility, decision-making, and cooperation. In this work, a multivariable algorithm that considers geographic mobility (GM) is presented. The decision-making process includes a feedback mechanism for the dynamic selection of reservation channels in a wireless network with cognitive capabilities. Geographic mobility plays a crucial role in how radio takes advantage of its environment; however, it has been scarcely explored in the literature. In the present work, geographic mobility tests were carried out involving the interaction of CR and primary users (PU), where a measurement of the latency time of the proposed algorithm called: Algorithm for Decision-Making with Geographic Mobility (ATDeMoGeo) was evaluated and compared with other algorithms such as: Dijkstra's Algorithm, Analytic Hierarchical Process (AHP), Fuzzy Analytic Hierarchical Process (FAHP) and Decision-Making Algorithm with Modified Dijkstra (ATDDiM). It was found that the proposed algorithm is robust and that it considerably reduces the latency time to accurately determine the best communication channels, thanks to the decision-making function implemented to establish which channels the CRs can occupy. For the selection of the best reserve channels, a series of criteria were considered, such as: signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), estimated channel time of availability (ETA) using the random waypoint mobility model (RWPM). These criteria are evaluated for two types of service, i.e., real time (RT) and best effort (BE). For the evaluation and validation of the ATDeMoGeo algorithm, a network with cognitive characteristics in NS-3 was simulated. The simulation scenarios consisted of a base station (BS) with mobile CRs and PUs. Based on the previous criteria, i.e., BW, SINR, AP and ETA, the BS assesses the spectrum occupancy information obtained by the CRs. The results show that the proposed algorithm can be used without problem for the proposed scenarios with higher speed and accuracy for the selection of better communication channels.

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A Reliable Routing Decision Algorithm with Minimal Multicast Power Consumption in Mobile Wireless Network

Zeng

Wang

2019

2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)

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Efficient way of implementing the random and GM (Gauss-Markov) mobility model in MANET

Cited by 3 publications

References 4 publications

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

A Reliable Routing Decision Algorithm with Minimal Multicast Power Consumption in Mobile Wireless Network

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