553 research outputs found

    Energy efficiency and traffic offloading in wireless mesh networks with delay bounds

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    In this paper, we study a wireless access network based on the Institute of Electrical and Electronics Engineers 802.11 standard and enriched with features such as caching and mesh networking. This system is analysed in terms of energy efficiency and traffic offloading, two objectives that are somewhat in contrast but both relevant to network and service providers as they directly impact the operational cost. In addition, QoS is also accounted for in the form of guaranteed bandwidth and bounded delay. To this aim, we developed a mathematical model of the system and solved it to optimality by means of integer linear programming. We can thus show how much can be saved both in terms of energy and traffic, also considering various tradeoff points among the two contrasting objectives. As a last step, we provide an investigation on the benefits of adding traffic aggregation features to the mathematical model

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

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    ํ•™์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

    QUALITY-OF-SERVICE PROVISIONING FOR SMART CITY APPLICATIONS USING SOFTWARE-DEFINED NETWORKING

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    In the current world, most cities have WiFi Access Points (AP) in every nook and corner. Hence upraising these cities to the status of a smart city is a more easily achievable task than before. Internet-of-Things (IoT) connections primarily use WiFi standards to form the veins of a smart city. Unfortunately, this vast potential of WiFi technology in the genesis of smart cities is somehow compromised due to its failure in meeting unique Quality-of-Service (QoS) demands of smart city applications. Out of the following QoS factors; transmission link bandwidth, packet transmission delay, jitter, and packet loss rate, not all applications call for the all of the factors at the same time. Since smart city is a pool of drastically unrelated services, this variable demand can actually be advantageous to optimize the network performance. This thesis work is an attempt to achieve one of those QoS demands, namely packet delivery latency. Three algorithms are developed to alleviate traffic load imbalance at APs so as to reduce packet forwarding delay. Software-Defined Networking (SDN) is making its way in the network world to be of great use and practicality. The algorithms make use of SDN features to control the connections to APs in order to achieve the delay requirements of smart city services. Real hardware devices are used to imitate a real-life scenario of citywide coverage consisting of WiFi devices and APs that are currently available in the market with neither of those having any additional requirements such as support for specific roaming protocol, running a software agent or sending probe packets. Extensive hardware experimentation proves the efficacy of the proposed algorithms

    Cooperation Strategies for Enhanced Connectivity at Home

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    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

    Implementing energy saving algorithms for Ethernet link aggregates with ONOS

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    During the last few years, there has been plenty of research for reducing energy consumption in telecommunication infrastructure. However, many of the proposals remain unim-plemented due to the lack of flexibility in legacy networks. In this paper we demonstrate how the software defined networking (SDN) capabilities of current networking equipment can be used to implement some of these energy saving algorithms. In particular, we developed an ONOS application to realize an energy-aware traffic scheduler to a bundle link made up of Energy Efficient Ethernet (EEE) links between two SDN switches. We show how our application is able to dynamically adapt to the traffic characteristics and save energy by concentrating the traffic on as few ports as possible. This way, unused ports remain in Low Power Idle (LPI) state most of the time, saving energy.Comment: 8 pages, 10 figure

    MP-SDWN: a novel multipath-supported software defined wireless network architecture

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