Wireless Mesh Networks Based on MBPSO Algorithm to Improvement Throughput

Wireless Mesh Networks can be regarded as a type of communication technology in mesh topology in which wireless nodes interconnect with one another. Wireless Mesh Networks depending on the semi-static configuration in different paths among nodes such as PDR, E2E delay and throughput. This study summarized different types of previous heuristic algorithms in order to adapt with proper algorithm that could solve the issue. Therefore, the main objective of this study is to determine the proper methods, approaches or algorithms that should be adapted to improve the throughput. A Modified Binary Particle Swarm Optimization (MBPSO) approach was adapted to improvements the throughput. Finally, the finding shows that throughput increased by 5.79% from the previous study

A control theoretic approach to achieve proportional fairness in 802.11e EDCA WLANs

This paper considers proportional fairness amongst ACs in an EDCA WLAN for provision of distinct QoS requirements and priority parameters. A detailed theoretical analysis is provided to derive the optimal station attempt probability which leads to a proportional fair allocation of station throughputs. The desirable fairness can be achieved using a centralised adaptive control approach. This approach is based on multivariable statespace control theory and uses the Linear Quadratic Integral (LQI) controller to periodically update CWmin till the optimal fair point of operation. Performance evaluation demonstrates that the control approach has high accuracy performance and fast convergence speed for general network scenarios. To our knowledge this might be the first time that a closed-loop control system is designed for EDCA WLANs to achieve proportional fairness

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Performance Optimization in Wireless Local Area Networks

Wireless Local Area Networks (WLAN) are becoming more and more important for providing wireless broadband access. Applications and networking scenarios evolve continuously and in an unpredictable way, attracting the attention of academic institutions, research centers and industry. For designing an e cient WLAN is necessary to carefully plan coverage and to optimize the network design parameters, such as AP locations, channel assignment, power allocation, MAC protocol, routing algorithm, etc... In this thesis we approach performance optimization in WLAN at di erent layer of the OSI model. Our rst approach is at Network layer. Starting from a Hybrid System modeling the ow of tra c in the network, we propose a Hybrid Linear Varying Parameter algorithm for identifying the link quality that could be used as metric in routing algorithms. Go down to Data Link, it is well known that CSMA (Carrier Sense Multiple Access) protocols exhibit very poor performance in case of multi-hop transmissions, because of inter-link interference due to imperfect carrier sensing. We propose two novel algorithms, that are combining Time Division Multiple Access for grouping contending nodes in non-interfering sets with Carrier Sense Multiple Access for managing the channel access behind a set. In the rst solution, a game theoretical study of intra slot contention is introduced, in the second solution we apply an optimization algorithm to nd the optimal degree between contention and scheduling. Both the presented solutions improve the network performance with respect to CSMA and TDMA algorithms. Finally we analyze the network performance at Physical Layer. In case of WLAN, we can only use three orthogonal channels in an unlicensed spectrum, so the frequency assignments should be subject to frequent adjustments, according to the time-varying amount of interference which is not under the control of the provider. This problem make necessary the introduction of an automatic network planning solution, since a network administrator cannot continuously monitor and correct the interference conditions su ered in the network. We propose a novel protocol based on a distributed machine learning mechanism in which the nodes choose, automatically and autonomously in each time slot, the optimal channel for transmitting through a weighted combination of protocols