We have been supported by various social systems, e.g., the Internet and transportation networks. It is natural for individuals to desire better quality of experience (QoE), e.g., watching high quality video without delay and jitter, and faster travel to the destination. On the other hand, the resources of systems are limited. For example, in case of content distribution services, such resources can be the computation capacity of servers and network capacity. From the viewpoint of the overall systems, global optimization, e.g., improving the total QoE among all users, under the resource constraint is important. In this research, we aim to achieve the global optimization under the mutual interactions among users' selfish decision making.
Software update and video streaming require massive content and/or long-term content distribution. Specifically, new release of content causes sudden increase of requests, and thus the distribution server tends to be a bottleneck. To tackle this problem, several systems, e.g., Windows update, recently apply Peer-to-Peer (P2P) file distribution where clients called peers upload retrieved fragments of the whole content, i.e., pieces, to other peers. However, some peers will not be willing to upload pieces to others, which are called free riders, due to communication overhead. Tit-for-Tat (TFT) strategy in game theory can alleviate such free riding behavior by encouraging equivalent exchange of pieces among each pair of peers. In this research, the determination problem of optimal piece flow in the TFT-based P2P file distribution is formulated as integer linear programming (ILP). With the help of knowledge obtained by analyzing the optimal piece flow, we aim to achieve distributed optimal content distribution
Network functions virtualization (NFV) is a new paradigm to achieve flexible and agile network services by decoupling network functions from proprietary hardware and running them on generic hardware as virtual network functions (VNFs). NFV technologies have been playing a key role of 5G networks and Datacenter networks. One of the challenging issues of NFV networking is resource allocation where physical resources (e.g., physical servers and links) are allocated to a logical network or logical path (service path) according to users' demand. In this research, we aim at desigining optimal NFV networks with the help of mathematical optimzation approaches.
In the 2011 Great East Japan Earthquake, both fixed and mobile communication networks had been unavailable for long time and in wide areas, due to damage of information communication infrastructures. As a result, it has been reported that there were many cases where evacuees and rescuers could not collect and distribute important information, e.g., damage information, evacuation information, and government information. In this research, we aim to achieve an evacuation guiding system that can speedily and safely navigate evacuees to appropriate refuges. Specifically, we tackle this problem by combining several functions, e.g., automatic road-state estimation and evacuation guiding under implicit collaboration among evacuees and their mobile devices, road network state information sharing among evacuees, and road network risk analysis using geographical big-data.
Broadcast is one of the powerful schemes to speedily distribute information over networks. When the information size is relatively large, pull-based broadcast is used. In the pull-based broadcast, a sending node first transfers small-size meta information of the original information to the neighboring node. Then, the receiver node only requests the original information if it is missing. The cryptocurrency system, Bitcoin, is one of the systems applying the pull-based broadcast for distributing blocks, each of which is a chunk of transactions and becomes up to 1 MB. On the other hand, it has been pointed out that the pull-based broadcast has vulnerability of delaying information propagation, which can be achieved by misuse of timeout mechanisms. In case of the Bitcoin system, delaying information propagation will affect the competition among miners. In this research, we aim to reveal the vulnerability of the pull-based broadcast with the help of epidemiology-inspired mathematical modeling and event-driven simulators.
We have been faced with the new era of IoT, where various devices are connected the Internet in addition to the conventional PCs and mobile phones. Most of the IoT devices are managed and controlled through wireless communications. On the other hand, most radio frequency bands have already been allocated to various existing systems. Thus, it is important to solve the spectrum exhaustion problem and to improve the spectrum utilization. To tackle this problem, cognitive radio has been studied, in which unlicensed users, i.e., secondary users (SUs), utilize the spectrum of a licensed user, i.e., primary user (PU), while avoiding interference in PU's communications. In this research, we aim to achieve a distributed cooperative control mechanism that can consider both cooperation among SUs for improving detection performance of PU's communication and competitive relationship among SUs to acquire individual communication opportunities.