Takayuki Nishio

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Fog computing is expected to be an enabler of mobile cloud computing, which extends the cloud computing paradigm to the edge of the network. In the mobile cloud, not only central data centers but also pervasive mobile devices share their heterogeneous resources (e. g. CPUs, bandwidth, content) and support services. The mobile cloud based on such resource(More)
Keywords: Adaptive resource discovery Energy-efficient Heterogeneous wireless networks Mobile cloud computing a b s t r a c t Mobile cloud computing (MCC) is aimed at integrating mobile devices with cloud computing. It is one of the most important concepts that have emerged in the last few years. Mobile devices, in the traditional agent-client architecture(More)
—Differential games are multi-agent versions of optimal control problems, which have been used for modeling control systems of smart grids. Thus, a differential-game theoretic approach is a promising mathematical framework enabling to discuss a demand-side energy management system where there are multiple decision-making entities. This paper first indicates(More)
SUMMARY We discuss the division of radio resources in the time and frequency domains for wireless local area network (WLAN) devices powered with microwave energy. In general, there are two ways to avoid microwave power transmission (MPT) from influencing data communications: adjacent channel operation of continuous MPT and WLAN data transmission and(More)
SUMMARY The IEEE 802.11 wireless LAN (WLAN) is based on carrier sense multiple access with collision avoidance (CSMA/CA) protocol. CSMA/CA uses a backoff mechanism to avoid collisions among stations (STAs). One disadvantage of backoff mechanisms is that STAs must wait for some period of time before transmission, which degrades spectral efficiency. Moreover,(More)
—The coverage probability and average ergodic rate of normalized SNR-based scheduling in a downlink cellular network are derived by modeling the locations of the base stations and users as two independent Poison point processes. The scheduler selects the user with the largest instantaneous SNR normalized by the average SNR. Our results confirm that(More)