Cross-layer resource allocation algorithms for wireless networks
Notice bibliographique
Résumé
The management of the available resources in wireless networks is an important task that must be executed in an efficient way, in order to optimize the network performance. In this thesis we design a Cross-Layer Resource Allocation Framework that can be applied in IEEE 802.11-based wireless networks and includes: sophisticated user association policies, fast handoff mechanisms, optimized TCP performance over mutli-AP association mechanisms and load-aware channel allocation policies. The user association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP (Access Points) load in the association process. Employing the mechanism in its plain form in wireless mesh networks, we may only achieve low throughput and low user transmission rates. We design a new association framework in order to provide optimal association and network performance. In this framework, we propose a new channel-quality-based user association mechanism inspired by the operation of the infrastructure-based WLANs. Besides, we enforce our framework by proposing an airtime-metric-based association mechanism that is aware of the uplink and downlink channel conditions as well as the communication load. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer, in order to fit it in the operation of wireless mesh networks. Last, we vi design a hybrid association scheme that can be efficiently applied in real deployments to improve the network performance. We evaluate the performance of our system through simulations and testbed experiments, and we show that wireless mesh networks that use the proposed association mechanisms are more capable in meeting the needs of QoS-sensitive applications. According to the IEEE 802.11 standard, the STAs get information about the active APs in their neighborhood by scanning the available channels and listening to transmitted beacons. We propose an IEEE 802.11k compliant framework for cooperative handoff where the STAs are informed about the active APs by exchanging information with neighboring STAs. Besides, the APs share useful information that can be used by the STAs in a handoff process. In this way we minimize the delay of the scanning procedure. We evaluate the performance of our mechanisms through OPNET simulations. We demonstrate that our scheme reduces the scanning delay up to 92%. In the aforementioned research approaches we considered single AP association. However, the high bandwidth demand of applications such as P2P and video-streaming has recently driven the need for connecting to multiple APs and bonding the ADSL backhauls via 802.11 connections. Since 802.11 APs and stations are usually single-radio, the communication to the set of APs naturally requires a time-division multiple access (TDMA) policy. However, the TDMA approach introduces delay in the end-to-end transmissions that can adversely affect the TCP throughput. We first perform an in-depth experimental analysis of how multi-AP TDMA affects the observed roundtrip time of TCP packets. Then we introduce a model that accurately predicts the performance of TCP on such environments. Based on this model, we propose a local resource allocation algorithm (minmax disconnection time) that minimizes this TCP degradation with a very low computational cost. The algorithm splits the original TDMA allocation in slots of shorter sizes such as the time that a station is disconnected from any AP is minimized. We show that the proposed scheme can improve up to 1.5 times the aggregate throughput observed by the station compared to a standard TDMA allocation. We show that the performance of the algorithm is very close to the theoretical upper-bound in several simulation scenarios. Dense deployments of hybrid 802.11-based WLANs result to high levels of interference and vii low end-user throughput. Many frequency allocation mechanisms for WLANs have been proposed by a large body of previous studies. However, none of these mechanisms considers the load that is carried by APs in terms of channel conditions, number of affiliated users as well as communication load, in conjunction. We propose LAC, a load-aware channel allocation scheme for WLANs, which considers all the above performance determinant factors. LAC incorporates an airtime cost metric into its channel scanning process, in order to capture the effects of these factors and select the channel that will provide approximately maximum long-term throughput. We evaluate LAC through extensive OPNET simulations, for many different traffic scenarios. Our simulations demonstrate that LAC outperforms other frequency allocation policies for WLANs in terms of total network throughput by up to 135%. We then extend the previous mechanisms in order to provide efficient channel selection in 802.11 mesh deployments, for minimizing contention and interference among co-channel devices and thereby supporting a plurality of QoS-sensitive applications. We propose ARACHNE, a routingaware channel selection protocol for wireless mesh networks. ARACHNE is distributed in nature, and motivated by our measurements on a wireless testbed. The main novelty of our protocol comes from adopting a metric that captures the end-to-end link loads across different routes in the network. ARACHNE prioritizes the assignment of low-interference channels to links that (a) need to serve high-load aggregate traffic and/or (b) already suffer significant levels of contention and interference. Our protocol takes into account the number of potential interfaces (radios) per device, and allocates these interfaces in a manner that efficiently utilizes the available channel capacity. We evaluate ARACHNE through extensive, trace-driven simulations. We observe that our protocol improves the total network throughput, as compared to three other channel allocation strategies
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».