EXTREME: combining the ease of management of multi-user experimental facilities and the flexibility of proof of concept testbeds

Portolés-Comeras, Marc; Requena‐Esteso, Manuel; Mangues‐Bafalluy, Josep; Cardenete-Suriol, M.

doi:10.1109/tridnt.2006.1649156

Cited by 23 publications

(12 citation statements)

References 6 publications

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“…All experiments are conducted utilizing Experimental Testbed for Research Enabling Mobility Enhancements (EXTREME) [9], which is developed for testing network algorithms and technologies of the Centre Tecnològic de Telecommunications de Catalunya (CTTC) in Barcelona, where the Wireless Local Area Network (WLAN) infrastructure is supported and its configuration is made with Asymmetric Digital Subscriber Line (ADSL) of rate 1 Mbps and 300 Kbps for uplink and downlink, respectively. Moreover, the production network operator of Orange mobile is directly interconnected with it via loosely coupled mechanism.…”

Section: Scenario Setupmentioning

confidence: 99%

Current Challenges and Opportunities in VoIP over Wireless Networks

Khalifeh¹,

Darabkh²

2012

Mobile Multimedia - User and Technology Perspectives

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Section: Scenario Setupmentioning

confidence: 99%

Current Challenges and Opportunities in VoIP over Wireless Networks

Khalifeh¹,

Darabkh²

2012

Mobile Multimedia - User and Technology Perspectives

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“…They created a Redmine based portal, to support resource reservation and measurement analysis [12]. Similar initiatives [15], replicating OMFs features, created restricted, yet similar frameworks. MiNT focused in extending the experiment capability of the physical infrastructure [8] by combining the NS-2 simulator [2] with the hardware nodes.…”

Section: Related Workmentioning

confidence: 99%

Experimentation made easy with the AMazING panel

Martins

Barraca

Gomes

et al. 2012

Proceedings of the Seventh ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterizatio

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Experimental testbeds for evaluating solutions in computer networks, are today required as a complement to simulation and emulation. As these testbeds become larger, and accessible to a broader universe of the research community, dedicated management tools become mandatory. These tools ease the complex management of the testbed specific resources, while providing an environment for researchers to define their experiments with large flexibility. While there are currently several management tools, the research community is still lacking tools that smooth the experimentation workflow. These were key aspects that we considered when developing the management infrastructure for our wireless testbed[4] (AMazING). We developed a experimentation support framework supported by an attractive GUI, automation and scripting capabilities, as well as experiment versioning and integrated result gathering and analysis.

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“…We carried out an experimental performance evaluation of the proposed scheme using the EXTREME Testbed R [10]. The configuration that we used for experiments in the testbed is shown in Figure 4.…”

Section: A Setup Of the Extreme Testbedmentioning

confidence: 99%

A supervised learning approach to cognitive access point selection

Bojović

Baldo

Nin-Guerrero

et al. 2011

2011 IEEE GLOBECOM Workshops (GC Wkshps)

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In this paper we present a cognitive AP selection scheme based on a supervised learning approach. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and leverages on this data in order to learn how to predict the performance of the available APs in order to select the best one. The prediction capabilities in our scheme are achieved by employing a Multi-layer Feed-forward Neural Network (MFNN) to learn the correlation between the observed environmental conditions and the obtained performance. Our experimental performance evaluation carried out in a testbed using the IEEE 802.11 technology shows that our solution effectively outperforms legacy AP selection strategies in a variety of scenarios. I. INTRODUCTIONMobile users are often located in areas where many Access Points (APs) are available. The traditional scenario in which this fact arises is that of IEEE 802.11 WLAN, due to the popularity of this technology and the high density of deployed APs in home, enterprise and public areas. A more recent scenario with similar characteristics is that of 3G femtocells deployed with the open subscriber group paradigm, where the mobile user needs to select the femtocell to attach to among several alternatives with partially overlapping coverage areas. Depending on the propagation environment and the traffic load, the performance that the mobile user can get from each AP may vary significantly; as a consequence, it is interesting for the mobile user to identify and select the AP that will provide the best performance.Regardless of the technology, the fundamental issue of the AP selection problem is that the performance achievable from a particular AP depends on so many environmental factors and with such complicate relationships that is not feasible to model it analytically without gross simplifications. For example, in the case of the 802.11 technology, AP schemes based solely on theoretical considerations [1]-[3] work fine only in specific situations, but fail to work properly over the big variety of conditions that are encountered in realistic scenarios, as we will show in this paper.To solve this problem, we propose a Cognitive Networking approach [4] based on learning from the past experience. In our approach, the mobile station is equipped with a cognitive engine that learns from its past experience how the environmental conditions influence the performance of each available AP; the cognitive engine then uses this knowledge to select the AP that is expected to provide the best performance. The learning is said to be supervised since it is based on known training data, which in our case consists of the measurement data gathered by the mobile station in the past.

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EXTREME: combining the ease of management of multi-user experimental facilities and the flexibility of proof of concept testbeds

Cited by 23 publications

References 6 publications

Current Challenges and Opportunities in VoIP over Wireless Networks

Current Challenges and Opportunities in VoIP over Wireless Networks

Experimentation made easy with the AMazING panel

A supervised learning approach to cognitive access point selection

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