拡張隠れセミマルコフモデルによる複数系列データモデリングとデータ収集・管理手法

In recent years, with the development of devices and the development of data aggregation methods, data to be analyzed and aggregating methods have been changed. Regarding the environment of Internet of Things (IoT), sensors or devices are connected to the communication terminal as access point or mobile phone and the terminal aggregate the sensing data and upload them to the cloud server. From the viewpoint of analysis, the aggregated data are sequential data and the grouped sequence is a meaningful set of sequences because the group represents the owner\u27s information. However, most of the researches for sequential data analysis are specialized for the target data, and not focusing on the "grouped" sequences. In addition from the viewpoint of aggregation, it needs to prepare the special terminals as an access point. The preparation of the equipment takes labor and cost. To analyze the "grouped" sequence and aggregate them without any preparation, this paper aims to realize the analysis method for grouped sequences and to realize the aggregation environment virtually. For analysis of grouped sequential data, we firstly analyze the grouped sequential data focusing on the event sequences and extract the requirements for their modeling. The requirements are (1) the order of events, (2) the duration of the event, (3) the interval between two events, and (4) the overlap of the event. To satisfy all requirements, this paper focuses on the Hidden Semi Markov Model (HSMM) as a base model because it can model the order of events and the duration of event. Then, we consider how to model these sequences with HSMM and propose its extensions. For the former consideration, we propose two models; duration and interval hidden semi-Markov model and interval state hidden-semi Markov model to satisfy both the duration of event and the interval between events simultaneously. For the latter consideration, we consider how to satisfy all requirements including the overlap of the events and propose a new modeling methodology, over-lapped state hidden semi-Markov model. The performance of each method are shown compared with HSMM from the view point of the training and recognition time, the decoding performance, and the recognition performance in the simulation experiment. In the evaluation, practical application data are also used in the simulation and it shows the effectiveness. For the data aggregation, most of conventional approaches for aggregating the grouped data are limited using pre-allocated access points or terminals. It can obtain the grouped data stably, but it needs to additional cost to allocate such terminals in order to aggregate a new group of sequences. Therefore, this paper focus on "area based information" as a target of the grouped sequences, and propose an extraordinary method to store such information using the storage of the terminals that exist in the area. It realize the temporary area based storage virtually by relaying the information with existing terminals in the area. In this approach, it is necessary to restrict the labor of terminals and also store the information as long as possible. To control optimally while the trade-off, we propose methods to control the relay timing and the size of the target storage area in ad hoc dynamically. Simulators are established as practical environment to evaluate the performance of both controlling method. The results show the effectiveness of our method compared with flooding based relay control. As a result of above proposal and evaluation, methods for the grouped sequential data modeling and its aggregation are appeared. Finally, we summarize the research with applicable examples.電気通信大学201

Max-min Fairness in 802.11 Mesh Networks

In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks. By exploiting features of the 802.11e/n MAC, in particular TXOP packet bursting, we are able to use this characterisation to establish a straightforward, practically implementable approach for achieving max-min throughput fairness. We demonstrate that this approach can be readily extended to encompass time-based fairness in multi-rate 802.11 mesh networks

W-NINE: a two-stage emulation platform for mobile and wireless systems

More and more applications and protocols are now running on wireless networks. Testing the implementation of such applications and protocols is a real challenge as the position of the mobile terminals and environmental effects strongly affect the overall performance. Network emulation is often perceived as a good trade-off between experiments on operational wireless networks and discrete-event simulations on Opnet or ns-2. However, ensuring repeatability and realism in network emulation while taking into account mobility in a wireless environment is very difficult. This paper proposes a network emulation platform, called W-NINE, based on off-line computations preceding online pattern-based traffic shaping. The underlying concepts of repeatability, dynamicity, accuracy and realism are defined in the emulation context. Two different simple case studies illustrate the validity of our approach with respect to these concepts

Zeroforcing precoding based MAC design to address hidden terminals in MU-MIMO WLANs

© 2015 IEEE. This paper focuses on the Medium Access Control (MAC) layer design for an inevitable Hidden Terminal problem in Multi User Multiple Input Multiple Output (MU-MIMO) Wireless Local Area Networks (WLANs). Specifically, our MAC design is supported by the precoding vectors obtained by Zeroforcing technique which are used to address the Hidden Terminals. An efficient channel sounding process is used by our MAC protocol to obtain the Channel State Information (CSI) from the desired and undesired clients which are used to calculate the precoding vectors at the transmitters (Access Points). Our MAC design then uses these precoding vectors in order to null interferences among the undesired clients to avoid collision of signals and to maintain the concurrent transmissions among the desired clients. The the parameters such as network capacity, signaling overheads and fairness are considered in the design. Our MAC layer design shows a slightly higher signaling overhead compared to RTS/CTS scheme. However, due to the concurrent transmissions after the handshaking process, the cost of singling overheads are compensated. The simulation study of our MAC layer design shows a remarkable constant network capacity gain of 4-5 times in comparison to traditional RTS/CTS. Moreover, the gain is irrespective to the available air-time

Neuro-Fuzzy Computing System with the Capacity of Implementation on Memristor-Crossbar and Optimization-Free Hardware Training

In this paper, first we present a new explanation for the relation between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. Then, based on these results, we propose a new neuro-fuzzy computing system which can effectively be implemented on the memristor-crossbar structure. One important feature of the proposed system is that its hardware can directly be trained using the Hebbian learning rule and without the need to any optimization. The system also has a very good capability to deal with huge number of input-out training data without facing problems like overtraining.Comment: 16 pages, 11 images, submitted to IEEE Trans. on Fuzzy system