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• W105996926 abstract "Multicasting is now widely used to support real-time applications, such as IPTV, in Wireless Local Area Networks (WLANs). This is because multicast reduces network cost and bandwidth consumption as compared to unicast when delivering a packet to multiple subscribers. However, multicast transmissions are carried out using the base data rate. This well-known behavior results in unfairness among users with diverse data rates. As a result, multicast transmissions severely degrade the network capacity of WLANs. To date, researchers have proposed many approaches to optimize the performance of multicast in WLANs. They include association control, smart antenna, rate adaptation and emulating multicast using unicast transmissions. In association control schemes, APs can actively re-associate stations (STAs) to other APs that provide better data rate and hence optimizing metrics such as airtime. Smart antenna approaches on the other hand utilize superior gains to improve the data rate of STAs with weak signal strength. Rate adaptation methods take advantage of the multi-rate feature supported by IEEE 802.11 standards. For instance, a content-based rate adaptation scheme may categorize contents into different layers by service quality requirements and use high data rates in favorable channel conditions and low data rates otherwise. To date, there is no work that combines association control strategies with smart antennas. To this end, this thesis is the first to consider association of stations and the benefits of smart antennas simultaneously. As the problem ii Abstract iii is NP-hard by a reduction from the Maximum Coverage with Group Budget (MCG) problem, this thesis proposes four novel heuristics to optimize various metrics, including the average load of APs, number of satisfied users, fairness among APs and throughput of APs. In addition, the thesis also studies an optimization that allows APs to rotate the beams of smart antennas to improve coverage and thereby, increases the opportunity of selecting the best subsets of stations. Experimental results in Matlab and Pamvotis show that the proposed heuristics outperform the Best Signal Strength Method (BSSM). Specifically, Heuristic 1, which covers the most users and has the lowest cost, reduces the average load of APs by up to 36.1%, increases the number of satisfied users by up to 237%, and boosts the average throughput of APs by up to 57.1%. Heuristic 2, which picks the subset of users that incurs the least AP load, has a Jain’s Fairness Index of 0.8622, whereas BSSM only achieved a fairness value of 0.6099. Moreover, when considering beam rotation, the performance of these heuristics improves. In particular, for Heuristic 3, beam rotation results in a decrease of up to 39.2% in terms of the average load of APs and Heuristic 4 registered a 61.1% decrease in maximum AP load. This thesis also studies the Multiple Multicast Sessions (MMS) problem. The MMS problem is more complex as it considers multiple multicast groups so that the said heuristics cannot be applied directly. Therefore, this thesis proposes a novel greedy algorithm (NG) that aims to minimize the overall airtime among APs. To better measure the performance of NG, this thesis presents a new metric, APs utilization, which helps quantifies the number of APs that are idle so that these idle APs can be switched off to save power. Simulation results show that NG reduces the average load of APs by up to 70.4% as compared to BSSM. In addition, NG only obtains 60% of APs utilization while BSSM does not let any APs enter sleep mode. DirCast shows the least APs utilization, around 40%, whereas it obtains extremely high load, more than two times that of NG and BSSM. Therefore, DirCast obtains low APs utilization at the expensive of higher AP load.iii is NP-hard by a reduction from the Maximum Coverage with Group Budget (MCG) problem, this thesis proposes four novel heuristics to optimize various metrics, including the average load of APs, number of satisfied users, fairness among APs and throughput of APs. In addition, the thesis also studies an optimization that allows APs to rotate the beams of smart antennas to improve coverage and thereby, increases the opportunity of selecting the best subsets of stations. Experimental results in Matlab and Pamvotis show that the proposed heuristics outperform the Best Signal Strength Method (BSSM). Specifically, Heuristic 1, which covers the most users and has the lowest cost, reduces the average load of APs by up to 36.1%, increases the number of satisfied users by up to 237%, and boosts the average throughput of APs by up to 57.1%. Heuristic 2, which picks the subset of users that incurs the least AP load, has a Jain’s Fairness Index of 0.8622, whereas BSSM only achieved a fairness value of 0.6099. Moreover, when considering beam rotation, the performance of these heuristics improves. In particular, for Heuristic 3, beam rotation results in a decrease of up to 39.2% in terms of the average load of APs and Heuristic 4 registered a 61.1% decrease in maximum AP load. This thesis also studies the Multiple Multicast Sessions (MMS) problem. The MMS problem is more complex as it considers multiple multicast groups so that the said heuristics cannot be applied directly. Therefore, this thesis proposes a novel greedy algorithm (NG) that aims to minimize the overall airtime among APs. To better measure the performance of NG, this thesis presents a new metric, APs utilization, which helps quantifies the number of APs that are idle so that these idle APs can be switched off to save power. Simulation results show that NG reduces the average load of APs by up to 70.4% as compared to BSSM. In addition, NG only obtains 60% of APs utilization while BSSM does not let any APs enter sleep mode. DirCast shows the least APs utilization, around 40%, whereas it obtains extremely high load, more than two times that of NG and BSSM. Therefore, DirCast obtains low APs utilization at the expensive of higher AP load. Statement of Originality This is to certify that the work described in this thesis is entirely my own, except where due reference is made in the text. No work in this thesis has been submitted for a degree to any other university or institution." @default.
• W105996926 created "2016-06-24" @default.
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• W105996926 date "2012-01-01" @default.
• W105996926 modified "2023-09-27" @default.
• W105996926 title "Mutlicast algorithms for dense wireless local area networks" @default.
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