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- 저자 : Duong Thuy-Van T. (검색결과 5 건)
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A Fusion of Data Mining Techniques for Predicting Movement of Mobile Users
Thuy Van T. Duong,Dinh Que Tran 한국통신학회 2015 Journal of communications and networks Vol.17 No.6
Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clusteringbased- sequential-pattern-mining (CSPM) and sequential-patternmining- based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.
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Thuy-Van T. Duong,Le Huu Binh,Vuong M. Ngo 한국통신학회 2022 ICT Express Vol.8 No.1
Wireless Mesh Networks is increasingly being applied widely with explosive traffic demand. This leads to a great challenge for traditional routing protocols in ensuring Quality of Service. We propose a QoS-guaranteed intelligent routing algorithm in this paper for WMN with heavy traffic load using reinforcement learning to improve its performance. We build a reward function for the Q-Learning algorithm to choose a route so that the packet delivery ratio is the highest. Concurrently, the learning rate coefficient is flexibly changed to determine constraints of the end-to-end delay. Our performance evaluations show that the proposed algorithm has significantly improved performance compared with other well-known routing algorithms.
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Thuy-Van T. Duong,Le Huu Binh 한국전자통신연구원 2022 ETRI Journal Vol.44 No.5
In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other wellknown routing algorithms.
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Le Huu Binh,Thuy-Van T. Duong 한국통신학회 2021 Journal of communications and networks Vol.23 No.1
Load balancing routing and quality of transmission(QoT) aware routing have been increasingly studied in mesh wirelessnetworks (WMN) to improve their performance. For the loadbalancing routing, the traffic bottleneck in the network can be resolved. However, it can decrease QoT because the routes may passthrough multiple hops. On the other hand, the QoT aware routingoften improves the QoT of the routes, but it can increase thetraffic bottleneck due to the unbalanced traffic load in the network. Therefore, the investigation of load balancing routing takinginto account QoT is very essential, especially in the case of a wideand ultra-high speed WMN. In this paper, we propose a load balancingrouting algorithm under the constraints of QoT for WMN. Our method uses the principle of the software defined networking(SDN) to choose the load balancing routes satisfying the constraintsof QoT. Our performance evaluations using OMNeT++ have shownthe effectiveness of the proposed algorithm in improving QoT of thedata transmission routes, increasing the packet delivery ratio andthe network throughput, decreasing the end-to-end delay.
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Binh Le Huu,Duong Thuy-Van T. 한국통신학회 2024 ICT Express Vol.10 No.1
5G-based MANET has received a lot of attention recently. Its fundamental feature is that nodes are constantly subjected to high traffic loads, while QoS requirements are extremely stringent. When applied to 5G-based MANETs, existing routing protocols have shown drawbacks. In this paper, we propose an enhanced AODV protocol solution for 5G-based MANETs. Using reinforcement learning, each node updates a state information database of intermediate nodes along routes to destinations. This database is used by the routing algorithm to find guaranteed QoS routes. Our solution is highly efficient in terms of throughput, end-to-end delay, and SNR, according to the simulation results.
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