Wooyong Chung

A soft computing approach to localization in wireless sensor networks

In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs). The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes. Soft computing plays a crucial role in both schemes. In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes. The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA). In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN). The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods.

A new range-free localization method using quadratic programming

In this paper, we propose a new range-free localization algorithm called optimal proximity distance map using quadratic programming (OPDMQP). First, the relationship between geographical distances and proximity among sensor nodes in the given wireless sensor network is mathematically built. Then, the characteristics of the given network is represented as a set of constraints on the given network topology and the localization problem is formulated into a quadratic programming problem. Finally, the proposed method is applied to two anisotropic networks the topologies of which are very similar to those of the real-world applications. Unlike the most of previous localization methods which work well in the isotropic networks but not in the anisotropic networks, it is shown that the proposed method exhibits excellent and robust performances not only in the isotropic networks but also in the anisotropic networks.

A new kernelized approach to wireless sensor network localization

In this paper, a new approach to range-free localization in Wireless Sensor Networks (WSNs) is proposed using nonlinear mapping, and the kernel function is introduced. The localization problem in the WSN is formulated as a kernelized regression problem, which is solved by support vector regression (SVR) and multi-dimensional support vector regression (MSVR). The proposed methods are simple and efficient in that no additional hardware is required for the measurements, and only proximity information and position information of the anchor nodes are used for the localization. The proposed methods are composed of three steps: the measurement step, kernelized regression step, and localization step. In the measurement step, the proximity information of the given network is measured. In the regression step, the relationships among the geographical distances and the proximity among sensor nodes is built using kernelized regression. In the localization step, each sensor node finds its own position in a distributed manner using a kernelized regressor. The simulation result demonstrates that the proposed methods exhibit excellent and robust location estimation performance.

Robust DV-hop algorithm for localization in Wireless Sensor Network

In this paper, we propose a novel localization algorithm in Wireless Sensor Network. The proposed method is a kind of range-free localization algorithm which only uses the proximity information between sensor nodes. In the proposed method, a robust weighted algorithm is presented to calculate the average hop distances between sensor nodes and anchor nodes. The proposed method is applied to an isotropic network and three anisotropic networks. The simulation results demonstrate that the proposed method shows excellent and robust location estimation results.

A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation*This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometric Engineering Research Center (BERC) at Yonsei University.

Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.

Structure Learning of Bayesian Networks Using Dual Genetic Algorithm

A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.

General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning

Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

Context-aware application for smart home based on Bayesian network

This paper deals with a context-aware application based on Bayesian network in the smart home. Bayesian network is a powerful graphical tool for learning casual dependencies between various context events and obtaining probability distributions. So we can recognize the resident`s activities and home environment based on it. However as the sensors become various, learning the structure become difficult. We construct Bayesian network simple and efficient way with mutual information and evaluated the method in the virtual smart home.

Localization Method in Wireless Sensor Networks using Fuzzy Modeling and Genetic Algorithm

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on the angle of arrival (AOA) or distance measurements. In this paper, we propose an enhanced centroid localization method based on edge weights of adjacent nodes using fuzzy modeling and genetic algorithm when node connectivities are known. The simulation results shows that our proposed centroid method is more accurate than the simple centroid method using connectivity only.

Intelligent Modeling of User Behavior based on FCM Quantization for Smart home

In the vision of ubiquitous computing environment, smart objects would communicate each other and provide many kinds of information about user and their surroundings in the home. This information enables smart objects to recognize context and to provide active and convenient services to the customers. However in most cases, context-aware services are available only with expert systems. In this paper, we present generalized activity recognition application in the smart home based on a naive Bayesian network(BN) and fuzzy clustering. We quantize continuous sensor data with fuzzy c-means clustering to simplify and reduce BN`s conditional probability table size. And we apply mutual information to learn the BN structure efficiently. We show that this system can recognize user activities about 80% accuracy in the web based virtual smart home.