Prediction-based advanced resource reservation in mobile environments

Samaan, Nancy; Benmammar, Badr; Krief, Francine; Karmouch, Ahmed

doi:10.1109/ccece.2005.1557244

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…Their mobility prediction algorithm is probabilistic and relies on the Dempster–Shafer Theory [ 20 ]). In [ 21 , 22 ], they illustrated the same algorithm, only that now the locations are represented by Cell-IDs assigned by the corresponding cell towers. Ridhawi et al applied a similar algorithm for tracking and predicting users indoors to support location-aware services in [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Karatzoglou

Kohler

Beigl

2018

Sensors

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In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

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Section: Related Workmentioning

confidence: 99%

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Karatzoglou

Kohler

Beigl

2018

Sensors

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“…The usefulness of cognitive radio is a hypothesis that has been proved based on the required time for a connection to a new frequency band, and this, whatever of the number of frequency bands that a terminal used to remedy a failed connection. In our future work, we will seek to reduce the impact of mobility on cognitive radio communications by building predictive models of mobility by referring to previous work such as [9] [10] [11] [12] [13].…”

Section: Discussionmentioning

confidence: 99%

Improving video conferencing application quality for a mobile terminal through cognitive radio

Amraoui

Baghli

Benmammar

2012

2012 IEEE 14th International Conference on Communication Technology

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Abstract-Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This optimizes the use of available radio-frequency (RF) spectrum while minimizing interference to other users. In this paper, we propose a new approach which uses the CR for improving video conferencing application quality for a cognitive radio mobile terminal (CRMT). We also show through experimentation the interest of our approach.

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“…We will seek also to reduce the impact of mobility on cognitive radio communications by building predictive models of mobility by referring to previous work such as [29] and [30].…”

Section: Discussionmentioning

confidence: 99%