塩田 茂雄

This paper presents an analytical model for network throughput of WLANs, taking into account heterogeneous conditions, namely network nodes transmit different length frames with various offered load individually. The airtime concept, which is often used in multihop network analyses, is firstly applied for WLAN analysis. The proposed analytical model can cover the situation that there are saturation and non-saturation nodes in the same network simultaneously, which is the first success in the WLAN analyses. This paper shows the network throughput characteristics of four scenarios. Scenario 1 considers the saturation throughputs for the case that one or two length frames are transmitted at the identical offered load. Scenarios 2 and 3 are prepared for investigating the cases that all network nodes transmit different length frames at the identical offered load and identical length frames at the different offered loads, respectively. The heterogeneous conditions for not only frame length but also offered load are investigated in Scenario 4.

We propose an algorithm for the connectivity-based sensor localization. We formulate the sensor localization into a variant of the classical MDS, in which the squared error between the measured inter-sensor distances and the calculated ones based on estimated sensor locations is used as a stress function. The proposed formulation is applicable even if the distances of several sensor pairs are unknown, and thus it does not require the minimum-hop-count assumption to estimate all intersensor distances. This feature is suitable especially for anisotropic networks, in which the minimum-hop-count assumption does not hold in general. We propose a technique, referred to as stress relaxation in this paper, which allows us to get a good solution while avoiding local minimums of the stress function. Simulation experiments verify that the proposed algorithm significantly outperforms the localization scheme based on the classical MDS.

This paper proposes an algorithm for counting the number of distinct targets using binary proximity sensors randomly deployed for target detection. This paper focuses on the fact that sensors simultaneously detecting a target should be in close proximity to each other. Thus, if multiple targets exist in a region of watch, sensors detecting them at a specific time are divided into several clusters, and each cluster represents one target. Based on such consideration, this paper uses the number of clusters of target-detecting sensors as an estimator of the number of distinct targets. To find clusters of sensors, the notion of mutual nearest neighbor (MNN) is employed. The MNN is a set of target-detecting sensors, where the largest distance within the set is smaller than the distance to any target-detecting sensor outside the set. The notion of the MNN yields a polynomial-time algorithm for partitioning a set of target-detecting sensors into several clusters. Simulation experiments verify that the proposed algorithm gives very precise estimates of the number of distinct targets if sensors are densely deployed.

We propose a method for estimating the size, perimeter length, and location of a time-variant target object moving in a monitored area. This method uses only binary information from sensors of which locations are unknown, where the binary information includes whether each sensor has detected the target object or not. Analysis based on integral geometry provides the relationship between the number of sensors detecting the target object and the target object parameters to be estimated. Because this relationship is linear, a linear filter, such as the Kalman filter, is applicable to estimate parameters if we can assume that the dynamics of the parameters are linear. As a concrete example, the size, shape, and location of an active thunder area is estimated. The model discussed in this paper is applicable as a model of participatory sensing.

We propose a light-weight performance analysis of Wi-Fi offload using a simple Markov model. Although the proposed model does not describe the behavior of the contention window of each station in detail, it allows us to analyze the non-saturation throughput while considering interaction among interfering stations and the queueing behavior of each station. In addition to this, the proposed model does not need to use the assumption that frames arrive at relay station according to a Poisson process. In this paper, we show how to apply the mean field approximation to the model to significantly reduce the amount of computation. We show that the proposed model accurately estimates the throughput performance of the Wi-Fi offload, and that the mean-field approximation is still valid in the scenario of the Wi-Fi offload.

This paper presents analytical expressions of network throughput for wireless 2-hop networks with tethering function at any offered load. Network throughputs can be obtained analytically by expressing collision probabilities and frame existence probabilities with respect to each node. The validities of the analytical expressions are confirmed from quantitative agreements between analytical and simulation results.

In image-based ray tracing, the possible permutations of reflectors (walls, doors, and sides of pieces of furniture) are all chosen to exhaustively generate images of a signal source and, for each generated image, the existence of an actual ray path that leaves the image and reaches the destination is investigated. In a typical indoor environment, where the reflectors are arranged parallel or perpendicular to each other, permutations of reflectors generate many image duplicates; that is, different permutations of reflectors generate the same image. Focusing on this fact, this paper proposes a novel technique for accelerating the image-based ray tracing. The key idea of the proposal is to choose a limited set of the permutations of reflectors to generate necessary and sufficient images without duplication. The proposed technique avoids the redundant ray-path search to reduces the run time of ray tracing without degrading the accuracy. The simulation experiments verify that the proposed technique makes the ray tracing much less demanding of computation.

The spatial relations between sensors placed for target detection can be inferred from the responses of individual sensors to the target objects. Motivated by this fact, this paper proposes a method for estimating the location of sensors by using their responses to target objects. The key idea of the proposal is that when two or more sensors simultaneously detect an object, the distances between these sensors are assumed to be equal to a constant called the basic range. Thus, new pieces of proximity information are obtained whenever an object passes over the area in which the sensors are deployed. This information is then be aggregated and transformed into a two dimensional map of sensors by solving a nonlinear optimization problem, where the value of the basic range is estimated together. Simulation experiments verify that the proposed algorithm yields accurate estimates of the locations of sensors.

This paper proposes an algorithm for counting the number of distinct targets based on the responses of binary proximity sensors. A basic idea of the proposal is that sensors detecting targets at a specific time should be partitioned into several clusters, where each cluster is composed of sensors detecting a common target. Thus, the number of clusters would be a natural choice of the estimator for the target counting. This paper presents a mathematical framework and a simple algorithm for partitioning a set of target-detecting sensors into several clusters. Simulation experiments verify that the proposed algorithm gives precise estimates of the number of distinct targets.