The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform
Mohamedslim Alouini - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
IEEE Transactions on Communications, 2017Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
arXiv: Information Theory, 2016Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, are natural candidates to provide the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
Mohammad Gharbieh - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
IEEE Transactions on Communications, 2017Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
arXiv: Information Theory, 2016Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, are natural candidates to provide the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
Ahmed Bader - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
IEEE Transactions on Communications, 2017Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
arXiv: Information Theory, 2016Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, are natural candidates to provide the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
Hesham Elsawy - One of the best experts on this subject based on the ideXlab platform.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
IEEE Transactions on Communications, 2017Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
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spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis
arXiv: Information Theory, 2016Co-Authors: Mohammad Gharbieh, Hesham Elsawy, Ahmed Bader, Mohamedslim AlouiniAbstract:The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, are natural candidates to provide the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a Traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the Traffic Requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT Traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
Georg Post - One of the best experts on this subject based on the ideXlab platform.
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Passive Online RTT Estimation for Flow-Aware Routers Using One-Way Traffic
2010Co-Authors: Damiano Carra, Konstantin Avrachenkov, Sara Alouf, Alberto Blanc, Philippe Nain, Georg PostAbstract:With the introduction of new generation high speed routers, and with the help of "flow-aware" Traffic management, it becomes possible to improve the Quality of Service for users as well as the network efficiency for ISPs. An example of the "flow-aware" Traffic management is the Alcatel-Lucent "Semantic Networking" framework where short-lived flows are processed with high priority and long-lived flows are controlled on a per flow basis. In order to control efficiently the flows, it is useful to know an estimate of the Round Trip Time (RTT). In the present work, we provide an online RTT estimation algorithm which is passive and needs one-way Traffic only. The one-way Traffic Requirement is essential for the application of the algorithm for "flow-aware" Traffic management inside the network. To the best of our knowledge, there was no online one-way Traffic RTT estimators. Tests on a controlled testbed and on the Internet demonstrate high accuracy of the proposed estimator.
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Passive Online RTT Estimation for Flow-Aware Routers using One-Way Traffic
2009Co-Authors: Damiano Carra, Konstantin Avrachenkov, Sara Alouf, Alberto Blanc, Philippe Nain, Georg PostAbstract:With the introduction of the new generation high speed routers, it becomes possible to improve the Quality of Service, the Quality of Experience for users and the network efficiency for ISPs with the help of "flow-aware" Traffic management. An example of the "flow-aware" Traffic management is the Alcatel-Lucent framework "Semantic Networking," where short-lived and long-lived TCP flows are treated differently. Short-lived flows are processed with high priority and long-lived flows are controlled in a "flow-aware" fashion. To control efficiently the long-lived flows, one needs to know an estimation of the Round Trip Time (RTT). In the present work, we provide an online RTT estimation algorithm which is passive and can deal with a one-way Traffic. The one-way Traffic Requirement is essential for the application of the algorithm for "flow-aware" Traffic management inside the network. To the best of our knowledge, there was no online one-way Traffic RTT estimators. Tests on the Internet demonstrate high accuracy of the proposed estimator. The results show that, 75% (resp. 99%) of the time, the RTT estimation is within 10% (resp. 20%) of the RTT at the source.
