Akio Koyama

Bio: Akio Koyama is an academic researcher from Yamagata University. The author has contributed to research in topics: Routing protocol & Zone Routing Protocol. The author has an hindex of 19, co-authored 144 publications receiving 1249 citations.

A QoS routing method for ad-hoc networks based on genetic algorithm

Abstract: A lot of work has been done on routing in MANETs. However, the proposed routing resolutions deal only with the best effort data traffic. Connections with quality of service (QoS) requirements are not supported. QoS routing has been receiving increasingly intensive attention in the wireline network domain. However, for MANETs only few QoS algorithms are proposed to be developed and QoS routing algorithms should be adaptive, flexible, and intelligent. We propose a genetic algorithm (GA) based routing method for Mobile Ad-Hoc Networks (GAMAN). Robustness rather that optimality is the primary concern of GAMAN. The GAMAN uses two QoS parameters for routing. The performance evaluation via simulations shows that GAMAN is a promising QoS routing algorithm for MANETs.

GAMAN : A GA Based QoS Routing Method for Mobile Ad-hoc Networks

Abstract: The Mobile Ad Hoc Networks (MANETs) are useful in many applications environments and do not need any infrastructure support. Much work has been done on routing in MANETs. However, the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention in the wireline network domain. However, these QoS routing algorithms can not be applied directly to MANETs, because of the bandwidth constraints and dynamic network topology of MANETs. Searching for the shortest path with minimal cost and finding delay constrained least-cost paths are NP-complete problems. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, to cope with changing of MANET topology, routing methods should be adaptive, flexible, and intelligent. In this paper, w...

Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks

Abstract: Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, we propose two fuzzy-based systems for cluster head selection in sensor networks. We call these systems: FCHS System1 and FCHS System2. We evaluate the proposed systems by simulations and have shown that FCHS System2 make a good selection of the cluster head compared with FCHS System1 and another previous system.

A web-based e-learning system for increasing study efficiency by stimulating learner's motivation

Abstract: Due to the opportunities provided by the Internet, more and more people are taking advantage of distance learning courses and during the last few years enormous research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is about 30%. One of the reasons is the low study desire when the learner studies the learning materials. In this research, we propose an interactive Web-based e-learning system. The purpose of our system is to increase the e-learning completion rate by stimulating learner's motivation. The proposed system has three subsystems: the learning subsystem, learner support subsystem, and teacher support subsystem. The learning subsystem improves the learner's study desire. The learner support subsystem supports the learner during the study, and the teacher support subsystem supports the teacher to get the learner's study state. To evaluate the proposed system, we developed several experiments and surveys. By using new features such as: display of learner's study history, change of interface color, encourage function, ranking function, self-determination of the study materials, and grouping of learners, the proposed system can increase the learning efficiency.

A GA-based multi-purpose optimization algorithm for QoS routing

Abstract: The routing algorithms for multimedia communication should use for routing more than one QoS parameter. This is because new services such as video on demand and remote meeting systems require better QoS. However, the problem of QoS routing is difficult and to find a feasible route with two independent path constraints is NP-complete. Also, QoS routing algorithms for broadband networks must be adaptive, flexible, and intelligent for efficient network management. In this paper, we propose a multipurpose optimization method for QoS routing based on genetic algorithm (GA). The simulation results show that proposed method has a good performance and is a promising method for QoS routing.

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Abstract: In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication

Abstract: In order to collect information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. Clustering provides an effective way to prolong the lifetime of WSNs. Current clustering approaches often use two methods: selecting cluster heads with more residual energy, and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, most of the previous algorithms have not considered the expected residual energy, which is the predicated remaining energy for being selected as a cluster head and running a round. In this paper, a fuzzy-logic-based clustering approach with an extension to the energy predication has been proposed to prolong the lifetime of WSNs by evenly distributing the workload. The simulation results show that the proposed approach is more efficient than other distributed algorithms. It is believed that the technique presented in this paper could be further applied to large-scale wireless sensor networks.

A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime

Abstract: Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.