32 research outputs found

    Cooperative Energy-efficient Management of Federated WiFi Networks

    Get PDF
    The proliferation of overlapping, always-on IEEE 802.11 access points (APs) in urban areas, can cause inefficient bandwidth usage and energy waste. Cooperation among APs could address these problems by allowing underused devices to hand over their wireless stations to nearby APs and temporarily switch off, while avoiding to overload a BSS and thus offloading congested APs. The federated house model provides an appealing backdrop to implement cooperation among APs. In this paper, we outline a distributed framework that assumes the presence of a multipurpose gateway with AP capabilities in every household. Our framework allows cooperation through the monitoring of local wireless resources and the triggering of offloading requests toward other federated gateways. Our simulation results show that, in realistic residential settings, the proposed framework yields an energy saving between 45 and 86 percent under typical usage patterns, while avoiding congestion and meeting user expectations in terms of throughput. Furthermore, we show the feasibility and the benefits of our framework with a real test-bed deployed on commodity hardware

    Cooperation Strategies for Enhanced Connectivity at Home

    Get PDF
    WHILE AT HOME , USERS MAY EXPERIENCE A POOR I NTERNET SERVICE while being connected to their 802.11 Access Points (APs). The AP is just one component of the Internet Gateway (GW) that generally includes a backhaul connection (ADSL, fiber,etc..) and a router providing a LAN. The root cause of performance degradation may be poor/congested wireless channel between the user and the GW or congested/bandwidth limited backhaul connection. The latter is a serious issue for DSL users that are located far from the central office because the greater the distance the lesser the achievable physical datarate. Furthermore, the GW is one of the few devices in the home that is left always on, resulting in energy waste and electromagnetic pollution increase. This thesis proposes two strategies to enhance Internet connectivity at home by (i) creating a wireless resource sharing scheme through the federation and the coordination of neighboring GWs in order to achieve energy efficiency while avoiding congestion, (ii) exploiting different king of connectivities, i.e., the wired plus the cellular (3G/4G) connections, through the aggregation of the available bandwidth across multiple access technologies. In order to achieve the aforementioned strategies we study and develop: โ€ข A viable interference estimation technique for 802.11 BSSes that can be implemented on commodity hardware at the MAC layer, without requiring active measurements, changes in the 802.11 standard, cooperation from the wireless stations (WSs). We extend previous theoretical results on the saturation throughput in order to quantify the impact in term of throughput loss of any kind of interferer. We im- plement and extensively evaluate our estimation technique with a real testbed and with different kind of interferer, achieving always good accuracy. โ€ข Two available bandwidth estimation algorithms for 802.11 BSSes that rely only on passive measurements and that account for different kind of interferers on the ISM band. This algorithms can be implemented on commodity hardware, as they require only software modifications. The first algorithm applies to intra-GW while the second one applies to inter-GW available bandwidth estimation. Indeed, we use the first algorithm to compute the metric for assessing the Wi-Fi load of a GW and the second one to compute the metric to decide whether accept incoming WSs from neighboring GWs or not. Note that in the latter case it is assumed that one or more WSs with known traffic profile are requested to relocate from one GW to another one. We evaluate both algorithms with simulation as well as with a real test-bed for different traffic patterns, achieving high precision. โ€ข A fully distributed and decentralized inter-access point protocol for federated GWs that allows to dynamically manage the associations of the wireless stations (WSs) in the federated network in order to achieve energy efficiency and offloading con- gested GWs, i.e, we keep a minimum number of GWs ON while avoiding to create congestion and real-time throughput loss. We evaluate this protocol in a federated scenario, using both simulation and a real test-bed, achieving up to 65% of energy saving in the simulated setting. We compare the energy saving achieved by our protocol against a centralized optimal scheme, obtaining close to optimal results. โ€ข An application level solution that accelerates slow ADSL connections with the parallel use of cellular (3G/4G) connections. We study the feasibility and the potential performance of this scheme at scale using both extensive throughput measurement of the cellular network and trace driven analysis. We validate our solution by implementing a real test bed and evaluating it โ€œin the wild, at several residential locations of a major European city. We test two applications: Video-on-Demand (VoD) and picture upload, obtaining remarkable throughput increase for both applications at all locations. Our implementation features a multipath scheduler which we compare to other scheduling policies as well as to transport level solution like MTCP, obtaining always better results

    IEEE 802.11 ๊ธฐ๋ฐ˜ Enterprise ๋ฌด์„  LAN์„ ์œ„ํ•œ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์ „ํ™”์ˆ™.IEEE 802.11์ด ๋ฌด์„  LAN (wireless local area network, WLAN)์˜ ์‹ค์งˆ์ ์ธ ํ‘œ์ค€์ด ๋จ์— ๋”ฐ๋ผ ์ˆ˜ ๋งŽ์€ ์—‘์„ธ์Šค ํฌ์ธํŠธ(access points, APs)๊ฐ€ ๋ฐฐ์น˜๋˜์—ˆ๊ณ , ๊ทธ ๊ฒฐ๊ณผ WLAN ๋ฐ€์ง‘ ํ™˜๊ฒฝ์ด ์กฐ์„ฑ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ™˜๊ฒฝ์—์„œ๋Š”, ์ด์›ƒํ•œ AP๋“ค์— ๋™์ผํ•œ ์ฑ„๋„์„ ํ• ๋‹นํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ”ผํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, ์ด๋Š” ํ•ด๋‹น AP๋“ค์ด ๊ฐ™์€ ์ฑ„๋„์„ ๊ณต์œ ํ•˜๊ฒŒ ํ•˜๊ณ  ๊ทธ๋กœ ์ธํ•œ ๊ฐ„์„ญ์„ ์•ผ๊ธฐํ•œ๋‹ค. ๊ฐ„์„ญ์œผ๋กœ ์ธํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ฑ„๋„ ํ• ๋‹น(channelization) ๊ธฐ๋ฒ•์ด ์ค‘์š”ํ•˜๋‹ค. ๋˜ํ•œ, ํ•œ ์กฐ์ง์ด ํŠน์ • ์ง€์—ญ์— ๋ฐ€์ง‘ ๋ฐฐ์น˜๋œ AP๋“ค์„ ๊ด€๋ฆฌํ•œ๋‹ค๋ฉด ํŠน์ • ์‚ฌ์šฉ์ž๋ฅผ ์„œ๋น„์Šคํ•  ์ˆ˜ ์žˆ๋Š” AP๊ฐ€ ์—ฌ๋Ÿฟ์ผ ์ˆ˜ ์žˆ๋‹ค. ์ด ๊ฒฝ์šฐ, ์‚ฌ์šฉ์ž ์ ‘์†(user association, UA) ๊ธฐ๋ฒ•์ด ์ค€์ •์ (quasi-static) ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ ๋ชจ๋‘์—์„œ ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฐ€์ง‘ ๋ฐฐ์น˜๋œ WLAN ํ™˜๊ฒฝ์—์„œ ์™€์ดํŒŒ์ด(WiFi) ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด ์ฑ„๋„ ํ• ๋‹น ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์—์„œ๋Š” ๊ฐ๊ฐ์˜ AP์— ์ฑ„๋„์„ ํ• ๋‹นํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ„์„ญ ๊ทธ๋ž˜ํ”„(interference graph)๋ฅผ ์ด์šฉํ•˜๋ฉฐ ์ฑ„๋„ ๊ฒฐํ•ฉ(channel bonding)์„ ๊ณ ๋ คํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์ฃผ์–ด์ง„ ์ฑ„๋„ ๊ฒฐํ•ฉ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•ด๋‹น AP๊ฐ€ ๋™์  ์ฑ„๋„ ๊ฒฐํ•ฉ์„ ์ง€์›ํ•˜๋Š”์ง€ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ์ฃผ ์ฑ„๋„(primary channel)์„ ๊ฒฐ์ •ํ•œ๋‹ค. ํ•œํŽธ, ์ค€์ •์  ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ UA ๋ฌธ์ œ๋Š” ๋‹ค์†Œ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ๊ฐ์˜ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ค๋ฅธ UA ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ค€์ •์  ํ™˜๊ฒฝ์—์„œ์˜ UA ๊ธฐ๋ฒ•์€ ๋ฉ€ํ‹ฐ์บ์ŠคํŠธ ์ „์†ก, ๋‹ค์ค‘ ์‚ฌ์šฉ์ž MIMO (multi-user multiple input multiple output), ๊ทธ๋ฆฌ๊ณ  AP ์ˆ˜๋ฉด๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ ๊ณผ ํ•จ๊ป˜ AP๊ฐ„์˜ ๋ถ€ํ•˜ ๋ถ„์‚ฐ(load balancing)๊ณผ ์—๋„ˆ์ง€ ์ ˆ์•ฝ์„ ๊ณ ๋ คํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์—์„œ UA ๋ฌธ์ œ๋Š” ๋‹ค๋ชฉ์ ํ•จ์ˆ˜ ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์ •์‹ํ™”ํ•˜์˜€๊ณ  ๊ทธ ํ•ด๋ฅผ ๊ตฌํ•˜์˜€๋‹ค. ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ UA ๊ธฐ๋ฒ•์€ ํ•ธ๋“œ์˜ค๋ฒ„(handover, HO) ์Šค์ผ€์ค„ ๋ฌธ์ œ๋กœ ๊ท€๊ฒฐ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋„๋กœ์˜ ์ง€ํ˜•์„ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž๊ฐ€ ์ ‘์†ํ•  AP๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” HO ์Šค์ผ€์ค„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž๋Š” ๋‹จ์ง€ ๋‹ค์Œ AP๋กœ ์—ฐ๊ฒฐ์„ ๋งบ์„ ์‹œ๊ธฐ๋งŒ ๊ฒฐ์ •ํ•˜๋ฉด ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ ๋งค์šฐ ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์ธ HO ๊ธฐ๋ฒ•์„ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ๊ทธ๋ž˜ํ”„ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•(graph modeling technique)์„ ํ™œ์šฉํ•˜์—ฌ ๋„๋กœ๋ฅผ ๋”ฐ๋ผ ๋ฐฐ์น˜๋œ AP์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ํ‘œํ˜„ํ•œ๋‹ค. ํ˜„์‹ค์ ์ธ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์œ„ํ•ด ์ง์„  ๊ตฌ๊ฐ„, ์šฐํšŒ ๊ตฌ๊ฐ„, ๊ต์ฐจ๋กœ, ๊ทธ๋ฆฌ๊ณ  ์œ ํ„ด ๊ตฌ๊ฐ„ ๋“ฑ์„ ํฌํ•จํ•˜๋Š” ๋ณต์žกํ•œ ๋„๋กœ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•œ๋‹ค. ๋„๋กœ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฐ ์‚ฌ์šฉ์ž์˜ ์ด๋™ ๊ฒฝ๋กœ๋ฅผ ์˜ˆ์ธกํ•˜๊ณ , ๊ทธ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ฐ ์‚ฌ์šฉ์ž ๋ณ„ HO์˜ ๋ชฉ์  AP ์ง‘ํ•ฉ์„ ์„ ํƒํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” HO ์Šค์ผ€์ค„ ๊ธฐ๋ฒ•์˜ ์„ค๊ณ„ ๋ชฉ์ ์€ HO ์ง€์—ฐ ์‹œ๊ฐ„์˜ ํ•ฉ์„ ์ตœ์†Œํ™”ํ•˜๊ณ  ๊ฐ AP์—์„œ ํ•ด๋‹น ์ฑ„๋„์„ ์‚ฌ์šฉํ•˜๋ ค๋Š” ์‚ฌ์šฉ์ž ์ˆ˜๋ฅผ ์ค„์ด๋ฉด์„œ WiFi ์—ฐ๊ฒฐ ์‹œ๊ฐ„์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ค€์ •์  ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆํ•œ ์ฑ„๋„ ํ• ๋‹น ๊ธฐ๋ฒ•๊ณผ UA ๊ธฐ๋ฒ•์˜ ํ˜„์‹ค์„ฑ์„ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•œ ์‹œํ—˜๋Œ€(testbed)๋ฅผ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ด‘๋ฒ”์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ค€์ •์  ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•๋“ค๊ณผ ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค์˜ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์˜€๋‹ค.As the IEEE 802.11 (WiFi) becomes the defacto global standard for wireless local area network (WLAN), a huge number of WiFi access points (APs) are deployed. This condition leads to a densely deployed WLANs. In such environment, the conflicting channel allocation between the neighboring access points (APs) is unavoidable, which causes the channel sharing and interference between APs. Thus, the channel allocation (channelization) scheme has a critical role to tackle this issue. In addition, when densely-deployed APs covering a certain area are managed by a single organization, there can exist multiple candidate APs for serving a user. In this case, the user association (UA), i.e., the selection of serving AP, holds a key role in the network performance both in quasi-static and vehicular environments. To improve the performance of WiFi in a densely deployed WLANs environment, we propose a channelization scheme. The proposed channelization scheme utilizes the interference graph to assign the channel for each AP and considers channel bonding. Then, given the channel bonding assignment, the primary channel location for each AP is determined by observing whether the AP supports the static or dynamic channel bonding. Meanwhile, the UA problem in the quasi-static and vehicular environments are slightly different. Thus, we devise UA schemes both for quasi-static and vehicular environments. The UA schemes for quasi-static environment takes account the load balancing among APs and energy saving, considering various techniques for performance improvement, such as multicast transmission, multi-user MIMO, and AP sleeping, together. Then, we formulate the problem into a multi-objective optimization and get the solution as the UA scheme. On the other hand, the UA scheme in the vehicular environment is realized through handover (HO) scheduling mechanism. Specifically, we propose a HO scheduling scheme running on a server, which determines the AP to which a user will be handed over, considering the road topology. Since a user only needs to decide when to initiate the connection to the next AP, a very fast and efficient HO in the vehicular environment can be realized. For this purpose, we utilize the graph modeling technique to map the relation between APs within the road. We consider a practical scenario where the structure of the road is complex, which includes straight, curve, intersection, and u-turn area. Then, the set of target APs for HO are selected for each user moving on a particular road based-on its moving path which is predicted considering the road topology. The design objective of the proposed HO scheduling is to maximize the connection time on WiFi while minimizing the total HO latency and reducing the number of users which contend for the channel within an AP. Finally, we develop a WLAN testbed to demonstrate the practicality and feasibility of the proposed channelization and UA scheme in a quasi-static environment. Furthermore, through extensive simulations, we compare the performance of the proposed schemes with the existing schemes both in quasi-static and vehicular environments.1 Introduction 1.1 Background and Motivation 1.2 Related Works 1.3 Research Scope and Proposed Schemes 1.3.1 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding 1.3.2 User Association for Load Balancing and Energy Saving in Enterprise WLAN 1.3.3 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks 1.4 Organization 2 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding 2.1 System Model 2.2 Channel Sharing and Bonding 2.2.1 Interference between APs 2.2.2 Channel Sharing 2.2.3 Channel Bonding 2.3 Channelization Scheme 2.3.1 Building Interference Graph 2.3.2 Channel Allocation 2.3.3 Primary Channel Selection 2.4 Implementation 3 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs 3.1 System Model 3.1.1 IEEE 802.11 ESS-based Enterprise WLAN 3.1.2 Downlink Achievable Rate for MU-MIMO Groups 3.1.3 Candidate MU-MIMO Groups 3.2 User Association Problem 3.2.1 Factors of UA Objective 3.2.2 Problem Formulation 3.3 User Association Scheme 3.3.1 Equivalent Linear Problem 3.3.2 Solution Algorithm 3.3.3 Computational Complexity (Execution Time) 3.4 Implementation 4 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks 4.1 System Model 4.2 Graph-Based Modeling 4.2.1 Division of Road Portion into Road Segments 4.2.2 Relation between PoAs on a Road Segment 4.2.3 Directed Graph Representation 4.3 Handover Scheduling Problem 4.3.1 Problem Formulation 4.3.2 Weight of Edge 4.3.3 HO Scheduling Algorithm 4.4 Handover Scheduling Operation 4.4.1 HO Schedule Delivery 4.4.2 HO Triggering and Execution 4.4.3 Communication Overhead 5 Performance Evaluation 5.1 CentralizedChannelizationSchemeforWirelessLANsExploitingChannel Bonding 5.1.1 Experiment Settings 5.1.2 Comparison Schemes 5.1.3 Preliminary Experiment for Building Interference Graph 5.1.4 Experiment Results 5.2 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs 5.2.1 Performance Metrics 5.2.2 Experiment Settings 5.2.3 Experiment Results 5.2.4 Simulation Settings 5.2.5 Comparison Schemes 5.2.6 Simulation Results 5.2.7 Simulation for MU-MIMO System 5.3 A Graph-BasedHandover Scheduling for Heterogenous Vehicular Networks 5.3.1 Performance Metrics 5.3.2 Simulation Settings 5.3.3 Simulation Results 6 Conculsion Bibliography AcknowledgementsDocto

    Reliable Multicast transport of the video over the WiFi network

    Get PDF
    Le transport multicast est une solution efficace pour envoyer le mรชme contenu ร  plusieurs rรฉcepteurs en mรชme temps. Ce mode est principalement utilisรฉ pour fournir des flux multimรฉdia en temps rรฉel. Cependant, le multicast classique de l IEEE 802.11 n'utilise aucun mรฉcanisme d acquittement. Ainsi, l รฉchec de rรฉception implique la perte dรฉfinitive du paquet. Cela limite la fiabilitรฉ du transport multicast et impact la qualitรฉ des applications vidรฉo. Pour rรฉsoudre ce problรจme, 802.11v et 802.11aa sont dรฉfinis rรฉcemment. Le premier amendement propose Direct Multicast Service (DMS). D'autre part, le 802.11aa introduit GroupCast with Retries (GCR). GCR dรฉfinit deux nouvelles politiques de retransmission : Block Ack (BACK) et Unsolicited Retry (UR).Dans cette thรจse, nous รฉvaluons et comparons les performances de 802.11v/aa. Nos rรฉsultats montrent que tous les nouveaux protocoles multicast gรฉnรจrent un overhead de transmission important. En outre, DMS a une scalabilitรฉ trรจs limitรฉe, et GCR-BACK n'est pas appropriรฉ pour des grands groupes multicast. D autre part, nous montrons que DMS et GCR-BACK gรฉnรจrent des latences de transmission importantes lorsque le nombre de rรฉcepteurs augmente. Par ailleurs, nous รฉtudions les facteurs de pertes dans les rรฉseaux sans fil. Nous montrons que l'indisponibilitรฉ du rรฉcepteur peut รชtre la cause principale des pertes importantes et de leur nature en rafales. En particulier, nos rรฉsultats montrent que la surcharge du processeur peut provoquer un taux de perte de 100%, et que le pourcentage de livraison peut รชtre limitรฉ ร  35% lorsque la carte 802.11 est en mode d รฉconomie d'รฉnergie.Pour รฉviter les collisions et amรฉliorer la fiabilitรฉ du transport multicast, nous dรฉfinissons le mรฉcanisme Busy Symbol (BS). Nos rรฉsultats montrent que BS รฉvite les collisions et assure un taux de succรจs de transmission trรจs important. Afin d'amรฉliorer davantage la fiabilitรฉ du trafic multicast, nous dรฉfinissons un nouveau protocole multicast, appelรฉ Block Negative Acknowledgement (BNAK). Ce protocole opรจre comme suit. L AP envoi un bloc de paquets suivi par un Block NAK Request (BNR). Le BNR permet aux membres de dรฉtecter les donnรฉes manquantes et d envoyer une demande de retransmission, c.ร .d. un Block NAK Response (BNAK). Un BNAK est transmis en utilisant la procรฉdure classique d accรจs au canal afin d'รฉviter toute collision avec d'autres paquets. En plus, cette demande est acquittรฉe. Sous l'hypothรจse que 1) le rรฉcepteur est situรฉ dans la zone de couverture du dรฉbit de transmission utilisรฉ, 2) les collisions sont รฉvitรฉes et 3) le terminal a la bonne configuration, trรจs peu de demandes de retransmission sont envoyรฉes, et la bande passante est prรฉservรฉe. Nos rรฉsultats montrent que BNAK a une trรจs grande scalabilitรฉ et gรฉnรจre des dรฉlais trรจs limitรฉs. En outre, nous dรฉfinissons un algorithme d'adaptation de dรฉbit pour BNAK. Nous montrons que le bon dรฉbit de transmission est sรฉlectionnรฉ moyennant un overhead trรจs rรฉduit de moins de 1%. En plus, la conception de notre protocole supporte la diffusion scalable de lavvidรฉo. Cette caractรฉristique vise ร  rรฉsoudre la problรฉmatique de la fluctuation de la bande passante, et ร  prendre en considรฉration l'hรฉtรฉrogรฉnรฉitรฉ des rรฉcepteurs dans un rรฉseau sans fil.The multicast transport is an efficient solution to deliver the same content to many receivers at the same time. This mode is mainly used to deliver real-time video streams. However, the conventional multicast transmissions of IEEE 802.11 do not use any feedback policy. Therefore missing packets are definitely lost. This limits the reliability of the multicast transport and impacts the quality of the video applications. To resolve this issue, the IEEE 802.11v/aa amendments have been defined recently. The former proposes the Direct Multicast Service (DMS). On the other hand, 802.11aa introduces Groupcast with Retries (GCR) service. GCR defines two retry policies: Block Ack (BACK) and Unsolicited Retry (UR).In this thesis we evaluate and compare the performance of 802.11v/aa. Our simulation results show that all the defined policies incur an important overhead. Besides, DMS has a very limited scalability, and GCR-BACK is not appropriate for large multicast groups. We show that both DMS and GCR-BACK incur important transmission latencies when the number of the multicast receivers increases. Furthermore, we investigate the loss factors in wireless networks. We show that the device unavailability may be the principal cause of the important packet losses and their bursty nature. Particularly, our results show that the CPU overload may incur a loss rate of 100%, and that the delivery ratio may be limited to 35% when the device is in the power save mode.To avoid the collisions and to enhance the reliability of the multicast transmissions, we define the Busy Symbol (BS) mechanism. Our results show that BS prevents all the collisions and ensures a very high delivery ratio for the multicast packets. To further enhance the reliability of this traffic, we define the Block Negative Acknowledgement (BNAK) retry policy. Using our protocol, the AP transmits a block of multicast packets followed by a Block NAK Request (BNR). Upon reception of a BNR, a multicast member generates a Block NAK Response (BNAK) only if it missed some packets. A BNAK is transmitted after channel contention in order to avoid any eventual collision with other feedbacks, and is acknowledged. Under the assumption that 1) the receiver is located within the coverage area of the used data rate, 2) the collisions are avoided and 3) the terminal has the required configuration, few feedbacks are generated and the bandwidth is saved. Our results show that BNAK has a very high scalability and incurs very low delays. Furthermore, we define a rate adaptation scheme for BNAK. We show that the appropriate rate is selected on the expense of a very limited overhead of less than 1%. Besides, the conception of our protocol is defined to support the scalable video streaming. This capability intends to resolve the bandwidth fluctuation issue and to consider the device heterogeneity of the group members.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

    Cooperation Strategies for Enhanced Connectivity at Home

    Get PDF
    WHILE AT HOME , USERS MAY EXPERIENCE A POOR I NTERNET SERVICE while being connected to their 802.11 Access Points (APs). The AP is just one component of the Internet Gateway (GW) that generally includes a backhaul connection (ADSL, fiber,etc..) and a router providing a LAN. The root cause of performance degradation may be poor/congested wireless channel between the user and the GW or congested/bandwidth limited backhaul connection. The latter is a serious issue for DSL users that are located far from the central office because the greater the distance the lesser the achievable physical datarate. Furthermore, the GW is one of the few devices in the home that is left always on, resulting in energy waste and electromagnetic pollution increase. This thesis proposes two strategies to enhance Internet connectivity at home by (i) creating a wireless resource sharing scheme through the federation and the coordination of neighboring GWs in order to achieve energy efficiency while avoiding congestion, (ii) exploiting different king of connectivities, i.e., the wired plus the cellular (3G/4G) connections, through the aggregation of the available bandwidth across multiple access technologies. In order to achieve the aforementioned strategies we study and develop: โ€ข A viable interference estimation technique for 802.11 BSSes that can be implemented on commodity hardware at the MAC layer, without requiring active measurements, changes in the 802.11 standard, cooperation from the wireless stations (WSs). We extend previous theoretical results on the saturation throughput in order to quantify the impact in term of throughput loss of any kind of interferer. We im- plement and extensively evaluate our estimation technique with a real testbed and with different kind of interferer, achieving always good accuracy. โ€ข Two available bandwidth estimation algorithms for 802.11 BSSes that rely only on passive measurements and that account for different kind of interferers on the ISM band. This algorithms can be implemented on commodity hardware, as they require only software modifications. The first algorithm applies to intra-GW while the second one applies to inter-GW available bandwidth estimation. Indeed, we use the first algorithm to compute the metric for assessing the Wi-Fi load of a GW and the second one to compute the metric to decide whether accept incoming WSs from neighboring GWs or not. Note that in the latter case it is assumed that one or more WSs with known traffic profile are requested to relocate from one GW to another one. We evaluate both algorithms with simulation as well as with a real test-bed for different traffic patterns, achieving high precision. โ€ข A fully distributed and decentralized inter-access point protocol for federated GWs that allows to dynamically manage the associations of the wireless stations (WSs) in the federated network in order to achieve energy efficiency and offloading con- gested GWs, i.e, we keep a minimum number of GWs ON while avoiding to create congestion and real-time throughput loss. We evaluate this protocol in a federated scenario, using both simulation and a real test-bed, achieving up to 65% of energy saving in the simulated setting. We compare the energy saving achieved by our protocol against a centralized optimal scheme, obtaining close to optimal results. โ€ข An application level solution that accelerates slow ADSL connections with the parallel use of cellular (3G/4G) connections. We study the feasibility and the potential performance of this scheme at scale using both extensive throughput measurement of the cellular network and trace driven analysis. We validate our solution by implementing a real test bed and evaluating it "in the wild, at several residential locations of a major European city. We test two applications: Video-on-Demand (VoD) and picture upload, obtaining remarkable throughput increase for both applications at all locations. Our implementation features a multipath scheduler which we compare to other scheduling policies as well as to transport level solution like MTCP, obtaining always better result

    Improving the Performance of Wireless LANs

    Get PDF
    This book quantifies the key factors of WLAN performance and describes methods for improvement. It provides theoretical background and empirical results for the optimum planning and deployment of indoor WLAN systems, explaining the fundamentals while supplying guidelines for design, modeling, and performance evaluation. It discusses environmental effects on WLAN systems, protocol redesign for routing and MAC, and traffic distribution; examines emerging and future network technologies; and includes radio propagation and site measurements, simulations for various network design scenarios, numerous illustrations, practical examples, and learning aids

    Performance and energy efficiency in wireless self-organized networks

    Get PDF
    fi=vertaisarvioitu|en=peerReviewed
    corecore