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Author

Toshihiko Kato

Other affiliations: KDDI
Bio: Toshihiko Kato is an academic researcher from University of Electro-Communications. The author has contributed to research in topics: Vehicular ad hoc network & Wireless ad hoc network. The author has an hindex of 18, co-authored 49 publications receiving 862 citations. Previous affiliations of Toshihiko Kato include KDDI.

Papers
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Proceedings ArticleDOI
23 Sep 2002
TL;DR: A new low latency handoff method is proposed, where access points used in a wireless LAN environment and a dedicated MAC bridge are jointly used to alleviate packet loss without altering the Mobile IP specifications.
Abstract: The growing popularity of IEEE 802.11 has made wireless LAN a potential candidate technology for providing high speed wireless access services. Also, by supporting Mobile IP, wireless LAN can meet demands for expanded wireless access coverage while maintaining continuous connectivity from one wireless LAN to another. In the Mobile IP procedure, mobile node movement can be detected from advertisements of foreign agents that differ from the previously received advertisement and the new "care-of" address is registered with the home agent. However, user packets are not forwarded to the new foreign agent until registration is completed and this interruption may degrade the quality of service especially in real-time applications such as audio and video or may lower the TCP throughput due to retransmission timeout. To tackle these issues, we propose a new low latency handoff method, where access points used in a wireless LAN environment and a dedicated MAC bridge are jointly used to alleviate packet loss without altering the Mobile IP specifications. In this paper, we present the design architecture of the proposed method and evaluate its performance in an actual network environment to verify the effectiveness of our approach.

131 citations

Journal ArticleDOI
TL;DR: This paper proposes FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks that has low message overhead and uses a lightweight retransmission mechanism to retransmit a packet when a relay fails.
Abstract: Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.

80 citations

Journal ArticleDOI
TL;DR: This paper discusses the challenges of routing in VANETs based on the data acquired from real-world experiments and proposes a routing protocol that is able to learn the best transmission parameters by interacting with the environment and takes into account multiple metrics.
Abstract: Apart from vehicle mobility, data rate (bit rate) and multihop data transmission efficiency (including route length) have a significant impact on the performance of a routing protocol for vehicular ad hoc networks (VANETs). Existing routing protocols do not seriously address all these issues and are not evaluated for a real VANET environment. Therefore, it is difficult for these protocols to attain a high performance and to work properly under various scenarios. In this paper, we first discuss the challenges of routing in VANETs based on the data acquired from real-world experiments and then propose a routing protocol that is able to learn the best transmission parameters by interacting with the environment. The protocol takes into account multiple metrics, specifically data transmission rate, vehicle movement, and route length. We use both real-world experiments and computer simulations to evaluate the proposed protocol.

55 citations

Journal ArticleDOI
TL;DR: The simulation results show that QLAODV can efficiently handle unicast applications in VANETs and is favored by its dynamic route change mechanism, which makes it capable of reacting quickly to network topology changes.
Abstract: In Vehicular Ad hoc Networks (VANETs), general purpose ad hoc routing protocols such as AODV cannot work efficiently due to the frequent changes in network topology caused by vehicle movement. This paper proposes a VANET routing protocol QLAODV (Q-Learning AODV) which suits unicast applications in high mobility scenarios. QLAODV is a distributed reinforcement learning routing protocol, which uses a Q-Learning algorithm to infer network state information and uses unicast control packets to check the path availability in a real time manner in order to allow Q-Learning to work efficiently in a highly dynamic network environment. QLAODV is favored by its dynamic route change mechanism, which makes it capable of reacting quickly to network topology changes. We present an analysis of the performance of QLAODV by simulation using different mobility models. The simulation results show that QLAODV can efficiently handle unicast applications in VANETs.

53 citations

Proceedings ArticleDOI
04 Dec 2009
TL;DR: This work proposes a MANET routing protocol that uses distributed Q-Learning to infer network status information and takes in to consideration link stability and bandwidth efficiency while selecting a route, which can efficiently handle network mobility.
Abstract: A mobile ad hoc network (MANET) is an autonomous collection of mobile nodes that communicate over relatively bandwidth constrained wireless links. Frequent link changes and limited bandwidth make communication in MANET particularly challenging. Based on AODV, we propose a MANET routing protocol considering link stability and bandwidth efficiency. The protocol uses distributed Q-Learning to infer network status information and takes in to consideration link stability and bandwidth efficiency while selecting a route. The protocol can efficiently handle network mobility by a way of preemptively switching to a better route before current route fails. We study the performance of this protocol through simulation and demonstrate its significant improvement compared to AODV.

39 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: In this article, the current state of the art for mobility management in next-generation all-IP-based wireless systems is presented, and the previously proposed solutions based on different layers are reviewed, and their qualitative comparisons are given.
Abstract: Next-generation wireless systems are envisioned to have an IP-based infrastructure with the support of heterogeneous access technologies. One of the research challenges for next generation all-IP-based wireless systems is the design of intelligent mobility management techniques that take advantage of IP-based technologies to achieve global roaming among various access technologies. Next-generation wireless systems call for the integration and interoperation of mobility management techniques in heterogeneous networks. In this article the current state of the art for mobility management in next-generation all-IP-based wireless systems is presented. The previously proposed solutions based on different layers are reviewed, and their qualitative comparisons are given. A new wireless network architecture for mobility management is introduced, and related open research issues are discussed in detail.

672 citations

Dissertation
09 Apr 2004
TL;DR: The current state of the art for mobility management in next-generation all-IP-based wireless systems is presented, and the previously proposed solutions based on different layers are reviewed, and their qualitative comparisons are given.

647 citations

Patent
12 Jun 2012
TL;DR: In this article, the authors propose a packet-centric wireless system, which includes a wireless base station communicating via a transmission control protocol/internet protocol (TCP/IP) to a first data network, one or more host workstations communicating via TCP/IP to the first datacenter, and a subscriber CPE station coupled with the wireless BS over a shared bandwidth over a wireless medium.
Abstract: A packet-centric wireless system includes: a wireless base station communicating via a transmission control protocol/internet protocol (TCP/IP) to a first data network; one or more host workstations communicating via TCP/IP to the first data network; one or more subscriber customer premise equipment (CPE) stations coupled with the wireless base station over a shared bandwidth via TCP/IP over a wireless medium; and one or more subscriber workstations coupled via TCP/IP to each of the subscriber CPE stations over a second network. The system can allocate shared bandwidth among the subscriber CPE stations to optimize end-user quality of service (QoS). The first data network includes at least one of: a wireline network; a wireless network; a local area network (LAN); and a wide area network (WAN). The second network includes at least one of: a wireline network; a wireless network; a local area network (LAN); and a wide area network (WAN).

541 citations

Journal ArticleDOI
TL;DR: This paper provides a comprehensive survey on the literature involving machine learning algorithms applied to SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security.
Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

436 citations