WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as RSS variability, environmental noise, and difficulty in detecting floor and building levels, compounded by limited labeled data and the high costs of collecting received signal strength (RSS). This paper introduces quantum stochastic contrastive learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish bounded probabilistic augmentation, diversity of the strong view, the suitability of the symmetric contrastive objective under heterogeneous augmentation channels, and expected similarity stability under zero-mean perturbations, supported by formal proofs. Leveraging these foundations, QSCL uses quantum computing (QC) to generate strong data augmentations via stochastic perturbations, thereby enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatio-temporal encoder (STE) that integrates convolutional layers with channel and spatial attention modules (CBAM-style) to capture spatial and temporal dependencies in sequential data. Furthermore, a symmetric cross-view contrastive loss is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with limited labeled data and resilience to quantum and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments.

Federated Learning (FL) enables decentralized model training but faces critical challenges in jointly optimizing privacy, accuracy, and communication efficiency, essential for resource-constrained wireless IoT deployments. We introduce ADP-QFed, an Adaptive Differentially Private Quantized Federated Learning framework that addresses these challenges through layer-wise adaptive noise injection and dual-bit deterministic quantization. By computing layer-specific sensitivity and importance scores, ADP-QFed dynamically calibrates privacy noise to minimize accuracy loss while ensuring rigorous (ε, δ)-differential privacy guarantees. The framework employs n-bit quantization for local computation and m-bit quantization for transmission, reducing communication overhead by up to 75%. Experiments on MNIST, FMNIST, and CIFAR-10 achieve test accuracies of 99.41%, 91.06%, and 82.94%, respectively, outperforming existing privacy-preserving FL methods by an average of 3.5%. These results are obtained while maintaining a privacy budget under ε = 2.25, representing a 40% reduction compared to state-of-the-art methods at similar accuracy levels. ADP-QFed advances practical privacy-preserving FL for edge sensing in low-altitude IoT systems by simultaneously optimizing privacy guarantees, model utility, and energy efficiency in wireless environments.

Collaborative indoor positioning requires maps with explicitly defined spatial connectivity. This paper presents a framework for constructing indoor map layouts using spatial entities and reachability structures. We introduce the Floor-Group-Area (FGA) Matrix Problem to visualize and optimize layout connectivity through column reordering. The framework exports 1bpp OGMs and spatial metadata in JSON. In user evaluations, trained participants completed full layouts of a four-story museum in 20 minutes, with all computationally intensive algorithms running in few seconds on a standard PC.

Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However, achieving accurate positioning remains challenging, especially in GPS-degraded environments. Pedestrian Dead Reckoning (PDR), which estimates position based on inertial sensor data, offers a promising alternative but typically demands substantial effort in algorithm design and system integration. This complexity often leads to prolonged development timelines and elevated costs. To address these challenges, we present the PDR Sensor (PDRS), a plug-and-play, black-box PDR module optimized for seamless integration into wearable and IoT platforms. PDRS integrates a microprocessor and an inertial measurement unit (IMU) within a compact, low-power module. It executes real-time sensor fusion onboard, providing position updates via standard interfaces (I2C, SPI, UART). Experimental evaluations demonstrate its high robustness, achieving a step detection error of just 0.16% over 1250 steps and a traveled distance error of 1.87% under diverse conditions. The system further exhibits low latency (2.5 ms) and modest power consumption (52.35 mA). Unlike conventional studies, the PDRS leverages a holistic co-design of hardware and software to enhance performance and integration aspects, simplifying adoption without requiring deep expertise. By lowering technical barriers, PDRS enables developers and researchers to prioritize innovation in IoT and wearable technologies, accelerating prototyping, reducing development time, and advancing industry progress. Future work aims to enhance accuracy, reduce power usage and module size, and integrate AI-driven techniques to better mitigate drift.

Accurate indoor localization and navigation enable real-time, ubiquitous, location-based services. Over the past decade, data-driven approaches for inertial odometry have shown the potential to enhance indoor positioning accuracy. However, low-cost inertial measurement units (IMUs), commonly used in smartphones and IoT devices, are prone to significant noise, leading to drift and degraded performance in navigation algorithms. This article presents a novel, lightweight, and real-time end-to-end framework, DeepILS, designed to process raw inertial data for precise pedestrian localization in indoor environments. DeepILS utilizes a residual network enhanced with channel-wise and spatial attention mechanisms, enabling accurate velocity and position estimation across diverse motion dynamics. The framework’s effectiveness is validated using four benchmarks and two newly introduced datasets in real-time edge scenarios. These datasets were collected across diverse indoor environments at the KAIST campus and Incheon National Airport, using multiple hardware platforms, including the KAIST IoT positioning module and Android smartphones. Experimental results, including tests on unseen data and comprehensive ablation studies, demonstrate that DeepILS improves localization accuracy by 70% compared to state-of-the-art methods while effectively mitigating sensor noise and enhancing robustness in real-world environments. Specifically, DeepILS exhibits excellent edge performance on IoT devices, making it highly suitable for real-time applications.

Deep learning-based inertial navigation remains a formidable challenge due to the intricate temporal dynamics of human motion and the stringent computational constraints of edge devices. This study introduces NanoMST, a highly efficient multi-scale transformer architecture designed for precise pedestrian inertial motion tracking with minimal computational overhead. The proposed model integrates a hierarchical multi-scale embedding strategy with a scale-adaptive attention mechanism, effectively capturing motion patterns across diverse temporal resolutions while optimizing efficiency through hardware-aware quantization. With 298K parameters and 7.59M floating-point operations, NanoMST achieves performance comparable to substantially larger models while maintaining an exceptionally low computational burden. Extensive evaluations on benchmark datasets, including OxIOD, RoNIN, and RIDI, yield average trajectory errors of 2.68m on RoNIN, 1.64m on RIDI, and 1.80m on OxIOD. The quantized 8-bit implementation reduces the model size from 1.23MB to 0.41MB while retaining 94% of the original model’s accuracy. Profiling on edge devices confirms real-time feasibility, with inference latencies ranging from 0.18 milliseconds to 0.96 milliseconds across various smartphone generations and an average throughput exceeding 6,000 samples per second, surpassing contemporary architectures such as IMUNet and CTIN. This study illustrates an efficient engineering approach for deep learning-based inertial tracking, demonstrating that high-precision sequential motion estimation can be achieved with a minimal computational footprint. The efficiency and real-time capability of NanoMST make it particularly suitable for deployment in resource-constrained environments, including mobile, wearable, and Internet of Things (IoT) applications.

The balance between sampling rate and response time is important for the efficiency of Global Navigation Satellite System (GNSS) based Indoor Outdoor Detection (IOD) systems. This paper explores the tradeoff between sampling rate and observation window, investigating how these factors influence the accuracy of IOD. For data collection, we adopt Raspberry Pi 4B+ with PmodGPS receivers, GlobalTop FGPMMOPA6H, allowing us to customize the data sampling. Our study utilizes Support Vector Machine (SVM) as the base model for the experiment, training the model with resampled datasets to simulate different sampling rates and observation windows. The results indicate that a higher sampling rate and shorter observation window lead to improved accuracy and response time. By adjusting the sampling rate from 1Hz to 10Hz, the accuracy can be improved by 2% and approximately 21 seconds entry delay improvement. Further, shortening the observation window will provide better response time, which in our case can achieve up to 10 minutes for exit delay. The study comparing with related works also indicate the importance of Assisted Global Positioning System (AGPS) and multiple GNSS for the IOD, with both AGPS and multiple GNSS, the responsiveness can be improved significantly.

This paper introduces a real-time pedestrian dead reckoning (PDR) algorithm for Internet of Things (IoT) devices. It is motivated by a practical challenge encountered during the development and operation of a positioning system named KAILOS, which provides positioning services via a smartphone application. Its dependence on smartphones causes data collection constraints imposed by operating systems. To mitigate this issue, we designed and incorporated a dedicated IoT device into the system, enabling full access to sensing data. This invention demands developing and porting a PDR algorithm into the IoT device to address the communication bottleneck with a remote positioning server. Our goal was to process a large amount of data from an Inertial Measurement Unit (IMU) instantly on the device to produce precise relative location information without forwarding all of it to the server. To this end, we propose a new approach for detecting steps using acceleration differential in our PDR. Additionally, a dynamic gyroscope bias update strategy is also included to enhance the capability of heading estimation. These advancements not only enhance the accuracy of the PDR algorithm but also facilitate its implementation on IoT devices. We practically deployed the PDR algorithm into our IoT hardware platform called Kailos Tag (K-Tag). Via the extensive experiments conducted both indoors and outdoors, we found that our real-time PDR outperformed the conventional methods. It reduces step detection errors (SDE) to approximately 1.6%, travel distance errors (TDE) to below 1.8%, and end/start errors(E/SE) to about 3.2m regardless of environment. Moreover, it enables an average positioning latency of 2.49 ms, while consuming only 20% of CPU usage and 8.6% of total power consumption.

Quantum computing, combined with deep learning, leverages principles like superposition and entanglement to enhance complex data-driven tasks. The Noisy Intermediate-Scale Quantum (NISQ) era presents opportunities for hybrid quantum-classical architectures to address this challenge. Despite significant progress, practical applications of these hybrid models are limited. This letter proposes a novel hybrid quantum-classical deep learning architecture, integrating Quantum Convolutional Neural Networks (QCNNs) and Long-Short-Term Memory (LSTM) networks, enhanced by Cluster State Signal Processing. Furthermore, this letter addresses indoor-outdoor detection using high-dimensional signal data, utilizing the Cirq platform—a Python framework for developing and simulating Noisy Intermediate Scale Quantum (NISQ) circuits on quantum computers and simulators. The approach addresses noise and decoherence issues. Preliminary results show that the QCNN-LSTM model outperforms pure quantum and hybrid models in accuracy and efficiency. This validates the practical benefits of hybrid architectures, paving the way for advancements in complex data classification like indoor-outdoor detection.

Many methods using various sensors of the latest smartphones are being studied for accurate indoor navigation. In particular, sensor fusion frameworks integrate all information collectible from smartphones, such as Wi-Fi signals, and measurements obtained from gyroscopes, accelerometers, or magnetometers. However, sensor measurements contain unpredictable real-time errors made in dynamic indoor environments. In this paper, we propose a new sensor fusion framework that attains high positioning accuracy by learning errors. The proposed system discriminates errors in the sensor measurements and accumulates the errors to adjust the measurement values based on the accumulated error distributions. High positioning accuracy was achieved in experiments conducted in two typical environments, a corridor-type space, and an open space.

WLAN fingerprinting has been extensively studied due to the widespread deployment of WLAN infrastructure. However, extensive calibration effort is required to collect fingerprints as are necessary for radio map construction, which is time-consuming and labor-intensive. Although many studies have been conducted to reduce the calibration efforts, it is inevitable to be applied in limited circumstances because it requires some location labels for initializing the learning models or requires continuous use of sensor data. In this research, we introduce a practical radio map construction method in a multi-story building utilizing only limited uses of sensors. The method determines the unlabeled data of each floor by clustering method utilizes the correlation of Wi-Fi and barometer data, and a radio map is constructed through a semi-supervised learning technique using candidates of location labels, PDR sensor data, and WLAN fingerprints. The extensive experiments carried out in three large multi-story buildings demonstrate that the method successfully builds accurate radio maps without any explicit effort to collect location reference.

Utilizing implicitly crowdsourced data is a popular approach for a Wi-Fi radio map construction for indoor positioning. The main advantage of implicit crowdsourcing is demanding less effort. A Wi-Fi radio map is constructed in an automated way by analyzing crowdsourced data. However, some of the studies working on the crowdsourcing approach do not consider a multi-floor environment, making their methods less practical. In this paper, we propose a method separating implicitly crowdsourced data by floor. The proposed method assumes that the crowd-sourced data include sequences of barometer data and that the information of the building where the data were collected is given. The proposed method can transform the crowdsourcing-based method for single-floor environments into a method for multifloor environments.

KAIST Indoor Localization System (KAILOS) was introduced in 2014 and became one of the world-first complete indoor positioning solutions. It is mainly based on the WiFi signal to estimate location and provide positioning service via mobile application that has many restrictions by the Operating System. This study proposes an extended architecture of KAILOS to make use of the IoT device for removing the barriers of the mobile-app-based approach and conveniently providing indoor positioning service. Design of the IoT device that contains hardware and software is also presented. Via this study, we prove that the IoT-based approach is promising and can be a crucial feature to widen applications of indoor localization services.

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.

Indoor navigation is a representative application of an indoor positioning system that uses a variety of equipment, including smartphones with various sensors. Many indoor navigation systems utilize Wi-Fi signals, as well as a variety of inertial sensors, such as a 3D accelerometer, digital compass, gyroscope, and barometer, to improve the accuracy of user location tracking. The inertial sensors are vulnerable to changes in the surrounding environments and sensitive to users behavior, but little research has been conducted on sensor fusion under these conditions. In this paper, we propose a dynamic sensor fusion framework (DSFF) that provides accurate user tracking results by dynamically calibrating inertial sensor readings in a sensor fusion process. The proposed method continually learns the errors and biases of each sensor due to the changes in user behavior patterns and surrounding environments. The learned patterns are then dynamically applied to the user tracking process to yield accurate results. The results of experiments conducted in both a single-story and a multi-story building confirm that DSFF provides accurate tracking results. The scalability of the DSFF will enable it to provide more accurate tracking results with various sensors, both existing and under development.

Many recent papers have directed attention to wireless indoor localization using Channel State Information (CSI) from the IEEE 802.11 OFDM scheme. Compared to the Received Signal Strength Index which contains only single source information, CSI from wireless communication contains channel characteristics per-subcarrier and thus brings higher positioning accuracy. Nevertheless, wireless interference, attenuation and multi-path problems mean that one cannot easily get exact location of a transmitter device. In this paper, we propose CompFi, an offline/online localization system for 5 GHz Wi-Fi that exploits both phase and amplitude of CSI complex values given in a phasor format as fingerprint data. Our novel Partially Connected Neural Network consists of 3-layer partially and 1-layer fully connected neural networks to make the best use of the CSI characteristics. Using regression analysis, our device-free localization system achieved 2-D distance error of about 1.74 m even under slight movement of the transmitter device at grid training and test points in a room environment with many structures.

Radio map construction automation by location-labeling of crowdsourced fingerprints is drawing a great attention these days. It allows radio maps of most of buildings in cities to be constructed at a very low cost. This paper proposes an adaptive semi-supervised location-labeling method for the crowdsourced fingerprints. The method is distinguished from the existing semi-supervised learning methods in that it uses address-labeled fingerprints collected during offline mobile payments for its location references. Despite inexactly specified location references, the method finds an optimal placement of location-unlabeled fingerprint sequences by varying the locations of address-labeled fingerprints. When the proposed method was evaluated at three large-scale landmark buildings in Seoul, the effectiveness of using location references collected during mobile payments for the proposed adaptive semi-supervised location-labeling method was apparent. Highly precise radio maps could be constructed for the buildings without any manual calibration efforts. The method can be used to automatically construct radio maps for most downtown buildings.

RSS values observed from a smartphone are related with distances to each AP. Therefore, AP locations can be estimated when enough number of location-labeled Wi-Fi fingerprints are obtained. Since manually collecting Wi-Fi fingerprints costs human labor, crowdsourcing approach is preferred. Crowdsourced Wi-Fi fingerprints usually need an additional step to tag a location label. The low accuracy of indirectly acquired location labels affects the result of AP location estimation. Therefore, some AP locations need to be discarded if the error of estimated AP location is high. To measure the error, it is necessary to survey the ground truth of AP location. Since surveying true AP locations also costs human labor, an error prediction method is helpful. We propose the neural network that predicts the error of an estimated AP location. The performance of the proposed method was tested on KAIST N1 building, Cheongju airport, and Lotte World mall.

The fingerprinting-based indoor positioning technique requires a database, i.e., a radio map, containing indoor scenes through the training phase. Since that offline phase is labor intensive, numerous studies are underway to minimize the effort. In this paper, we present a concept that applies to a collaborative crowdsourcing method. We designed a system that relies on people and robots moving in the indoor space to construct a radio map. Experiments at two testbeds provide proof of the concept and are the result of the last step of the proposed system.

Subway is one of the public transport which carries people at the exact time. In the metropolis all over the world, it transfers countless people in the rush hour. Since there is no traffic jam in the subway, accuracy is one of the most crucial characters of the subway. However, subway trains are often delayed by some reasons like an accident. The delay can make many people confused and inconvenient. In this context, the need for dynamic timetable model which corrects the error of timetable in real time has emerged. Since the method to detect train moving is necessary to modify timetable, various solutions are proposed in the indoor positioning way, such as Wi-Fi fingerprint, magnetometer and so on. A method using Wi-Fi fingerprint is a primary way in indoor positioning, but it is tough to build a radio map for every single station. Shin et al. suggested a solution using a magnetometer, with simple judging criteria named 'decision peak.' This research proved that a magnetometer is a practical solution. Nonetheless, it can not detect the train moving in some cases. We propose a new method to detect the subway moving using a linear accelerometer, based on the essence of the problem.

By the infrastructure of cellular network changing, the conventional methods has became not be adequate. As an alternative, a probabilistic model for dynamic input aiming on signals from multiple cellular station is proposed in this study, and the feasibility was verified. The result from the experiment can be shown a little petty, but if one experiments in a better environment and utilizes LTE and 5G network, about 20~30m of the positioning error can be expected.

WiFi fingerprinting methods are widely used in indoor positioning field, but it requires time and efforts to collect fingerprints. Crowdsourcing techniques have been actively studied to reduce the collection cost, but it still needs user's explicit involvement such as installing and operating an application. In this paper, we propose a network fingerprinting method without the explicit involvement. it collects unlabeled fingerprints including received signal strength(RSS) of probe request message(PRqM) by multiple APs. After collecting the fingerprints, we perform singular vector decomposition(SVD), latent semantic analysis(LSA) and location optimization to construct radio map. The proposed method achieved 2.93m accuracy of radio map and 3.72m accuracy of positioning.

A global indoor positioning system (GIPS) is a system that provides positioning services in most buildings in villages and cities globally. Among the various indoor positioning techniques, WLAN-based location fingerprinting has attracted considerable attention because of the wide availability of WLAN and relatively high resolution of the fingerprint-based positioning techniques. This paper introduces methods and tools to construct a GIPS by using WLAN fingerprinting. An unsupervised learning-based method is adopted to construct radio maps using fingerprints collected via crowdsourcing, and a probabilistic indoor positioning algorithm is developed for the radio maps constructed with the crowdsourced fingerprints. Along with these techniques, collecting indoor and radio maps of buildings in villages and cities is essential for a GIPS. This paper aims to collect indoor and radio maps from volunteers who are interested in deploying indoor positioning systems for their buildings. The methods and tools for the volunteers are also described in the process of developing an indoor positioning system within the larger GIPS. An experimental GIPS, named KAIST indoor locating system (KAILOS), was developed integrating the methods and tools. Then indoor navigation systems for a university campus and a large-scale indoor shopping mall were developed on KAILOS, revealing the effectiveness of KAILOS in developing indoor positioning systems. The more volunteers who participate in developing indoor positioning systems on KAILOS-like systems, the sooner GIPS will be realized.

A home indoor positioning system (HIPS) is a positioning system that provides location information of mobile devices, such as smartphones, for location-based IoT applications in a home environment. This paper introduces methods and algorithms to construct an HIPS using Wi-Fi signals. In the proposed system, an intelligent mobile robot, such as a home vacuum robot, is utilized to automatically construct radio maps for the system. Along with radio maps, the system adopts new algorithms to provide highly accurate positioning services and provides interfaces to support variable location-based IoT applications.

Numerous attempts have been made to estimate position using Wi-Fi signals. Radio map, which is the collection of the received signal strength indicator (RSSI) of Wi-Fi signals along with their collected location information, is essential for the estimation of positon in a high resolution. However, the radio map construction is not only labor intensive, but also it requires periodic updates to cope with the changes of Wi-Fi environments. This paper proposes a practical location-labeling method for crowdsourced fingerprints to construct Wi-Fi radio maps in a large shopping mall environment. In the first step, the initial radio map is constructed based on a small number of reference data obtained from mobile payment transactions. In the second step, the path of the crowdsourced fingerprint sequence is accumulated and then their collected locations are estimated to improve the radio map. Experiments performed at a landmark building revealed the proposed method was effective in location-labeling of crowdsourced fingerprints.

A magnetic indoor positioning system utilizes distorted geomagnetic fields indoors to estimate locations. However, existing magnetic positioning methods have difficulties in positioning, especially in a wide space because of the ambiguity of magnetic data. Multiple magnetic sensors should work in collaboration to yield a high accuracy, hindering the techniques from being used by mobile devices such as smartphones. To address this problem, we propose a new magnetic indoor positioning method using deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying features of magnetic landmarks detected from a dense magnetic map. The magnetic map is constructed by a robot. The proposed method achieved over 80% accuracy in a two-dimensional environment.

A radio map is a collection of signal fingerprints labeled with their collected locations. It is known that the performance of a fingerprint-based positioning systems is closely related to the precision and accuracy of the underlying radio maps. However, little has been studied on the performance of radio maps in relation to the fingerprint collection methods and the radio map models, which determine the accuracy and precision of radio maps, respectively. This paper evaluates the performance of various radio map construction methods in both indoor and outdoor environments. Four radio map construction methods, i.e., a point-by-point manual calibration, a walking survey, a semisupervised learning-based method, and an unsupervised learning-based method, have been compared. We also evaluate the performance of various types of radio map models that represent the characteristics of collected fingerprints. To demonstrate the importance of the radio map model, a new model named signal fluctuation matrix (SFM) was developed, and its performance was compared with that of the three conventional radio map models, respectively. The evaluation revealed that the performance of the radio maps was very sensitive to the design of radio map models and the number of fingerprints collected at each location. The performance achieved by SFM-based positioning was comparable with that of the other models despite using a small number of fingerprints.

The static k-Nearest Neighbor (k-NN) method for localization has limitations in accuracy due to the fixed k value in the algorithm. To address this problem, and achieve better accuracy, we propose a new dynamic k-Nearest Neighbor (Dk-NN) method in which the optimal k value changes based on the topologies and distances of its nearest neighbors. The proposed method has been validated using the WLAN-fingerprint data sets collected at COEX, one of the largest convention centers in Seoul, Korea. The proposed method significantly reduced both the mean error distances and the standard deviations of location estimations, leading to a significant improvement in accuracy by ~ 23% compared to the cluster filtered k-NN (CFK) method, and ~ 17% compared to the k-NN (k = 1) method.

Wireless Local Area Network (WLAN) location fingerprinting has become a prevalent approach to indoor localization. However, its widespread adoption has been hindered by the need for manual efforts to collect location-labeled fingerprints for the calibration of a localization model. Several semi-supervised learning methods have been applied to reduce such manual efforts by exploiting unlabeled fingerprints, but they still require some amount of labeled fingerprints for initializing the learning process. In this research, in order to obviate the need for location labels or references, we propose a novel unsupervised learning method that calibrates a localization model using unlabeled fingerprints based on a hybrid global-local optimization scheme. The method determines the optimal placement of fingerprint sequences on an indoor map, under the constraint imposed by the inner structure shown on the map such as walls and partitions. An efficient interaction between a global and a local optimization in the hybrid scheme drastically reduces the complexity of the learning task. Experiments carried out in a single- and a multi-story building revealed that the proposed method could successfully build a precise localization model without any location reference or explicit efforts to collect labeled samples.

This paper presents KAIST indoor locating system (KAILOS), which aims to provide a global indoor positioning service based mainly on Wi-Fi fingerprints. KAILOS integrates various techniques and tools which are categorized into three components in large: KAI-Map, KAI-Pos, and KAI-Navi. KAIMap comprises crowdsourcing indoor maps and radio maps. KAI-Pos is the positioning system installed on KAI-Map. KAI-Navi is an in-and-outdoor integrated navigation system installed on KAI-Map and KAI-Pos. A positioning technology stack is also introduced in this paper, and the components of KAILOS are presented in the framework of the positioning technology stack.

A radio map is a collection of fingerprints and collected location information. To build more accurate Wi-Fi positioning system (WPS), a more precise radio map should be prepared in advance. Despite GPS signals are usually unreachable in most indoor venues, GPS signals are commonly used as reference locations to tag the collected fingerprints. As a result, WPS has not been able to achieve high performance in accuracy indoors, even if the place is rich in Wi-Fi signals. This paper introduces a novel method to construct a radio map without any use of GPS signals. Users' home or office address is used instead to tag the locations of collected Wi-Fi signals. Since the method utilizes Wi-Fi signals collected indoors, a more precise radio map construction is possible. For evaluation, we tested the method at four deliberately selected residential and downtown areas in Daejeon and Seoul, Korea. Once the data collection rate rises above 50%, the average error distance was within 10m or less. This indicates that address-based crowdsourcing radio map construction can be an effective means to construct global-scale but precise radio maps at low cost.

The schedule of subway provides information on the arrival and departure of a train at stations. The subway passengers often refer to the subway schedule to make an appointment or to make their own schedule. Many apps on subway schedule are available for smartphone users. For example, in Korea, there are more than 10 apps that users can download to get the schedule of subway. However, most of the apps provide the schedule of subway based on fixed time table. As a result, there is no way to inform the differences between the time on the time table and the actual arrival time of a subway train. In this paper, we propose a method to provide correct information on the schedule of subway. Detecting the arrival of a train is required, and the difference between the time on time table and the actual arrival time of the train should be known. The method detects the arrival of a train based on the magnetometer sensing data. Then it identifies the arrived stop and computes the time differences between the schedule and the actual. The identified time differences are reflected to the time table to inform more correct schedule to the rest of the users. When we tested the detection accuracy of a subway train using the proposed technique at Seoul subway line, it showed over 90% accurate results. This indicates that the proposed method can be widely accepted to the subway schedule app developers.

Research on place extraction has been of interest for the detection of meaningful places that users visit. Because interpretations of meaningful places may be different according to location-based applications, a universal place extraction algorithm that is able to detect all kinds of meaningful places needs to be developed. Unfortunately, most previously proposed place extraction algorithms failed to show high place detection accuracy and also failed to perfectly detect meaningful places. In this work, we propose a new place extraction algorithm that can significantly enhance the accuracy of place extraction. The basic concept of the proposed algorithm is a superstate model, which is an extension of the Hidden Markov Model (HMM); we substituted superstates for the simple probabilistic distributions of the HMM. Our proposed algorithm shows remarkable detection accuracy in place extraction, significantly higher than any other previously proposed algorithms. Furthermore, the proposed algorithm can efficiently operate in mobile environments because its computations are simple.

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.

MOTIVATION: The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. RESULTS: In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions. AVAILABILITY: http://code.google.com/p/simultaneous-pin/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recently, many computational methods for predicting protein-protein interaction (PPI) have been developed by utilizing domain-domain interaction or associated information. However, most of the methods lack of reflecting the collaboration effect of multiple do-mains to the prediction of PPI. In this paper, we develop a computational model that considers not only inter relationship between protein pair but also the intra-domain functional cohesion effect in PPI. In the computational model, a value assigning method to reflect the intra and inter collaboration devised and the computed values are stored in interaction significance (IS) matrix. Then an equation for PPI prediction is devised on IS matrix. For S. cerevisiae PPI data from DIP, MINT and IntAct, domain data from Pfam-A, the prediction method achieved 73.91% and 92.02% sensitivity and specificity respectively.

The coming of the e-science era provides more opportunities to improve the work of scientists by integrating sensors, resources, and services in GRID and Internet environments. We can expect much more improvement in scientists' work if they are supported by an appropriate tool. In this paper, we propose an e-science workbench that helps scientists design and perform their e-science experiments on the GRID or internet environments without the support of programmers. Since the e-science workbench may contain many modules and functions, we adopt a layered architecture in constructing the system. In order to convince potential users of the effectiveness of using the e-science workbench, we developed two domain specific e-Science workbenches. One is a health workbench and the other is a bio workbench. We observed that we could build the u-health and bio workbenches very systematically due to the underlying middle and bottom e-science workbench layers.

In this paper, we propose a formulated method for the analysis of conserved domain combinations and report an overview of domain combinations by identifying domain patterns and analyzing their functional annotations. The proposed method measures co-occurrence frequency and mutual dependency of domains in a domain combination using association rules. The method is useful to estimate the meaningfulness of a given domain combination in terms of conservation. Using the method, we extracted domain patterns in S.cerevisiae proteins and investigated GO term annotations of the domains. According to the investigation, domains in S.cerevisiae proteins are turned out to form patterns in which the members of the patterns are highly affiliated to one another. Also, extracted patterns are revealed to have a tendency of being associated with molecular functions.

An error-comprising workflow definition of mission critical business process might incur serious problems to an enterprise. Although workflow designer is responsible for the error-comprising workflow definitions, workflow system has to be equipped with an intelligent workflow modeling tool preventing workflow designers from specifying error-comprising workflow definitions. Faults and mistakes of process designers have to be detected and reported to them by the tool at workflow build time. Access conflicts and improper specification of exceptions are two typical examples of such an error-comprising workflow definition. In this paper, we develop an access conflict detection and an uncaught exception detection techniques. A simple workflow definition language, named SWDL, is developed and the techniques are successfully developed on SWDL using Set Constraint System. With slight modifications and scope restrictions, the proposed techniques can be used in any workflow definition language either by translating it into SWDL or by referring to the techniques for the developing its own techniques. This indicates that general conventional programming language analysis techniques can be used in the analysis of workflow definitions by introducing an intermediate workflow definition language and developing analysis techniques on it.

The interoperation between workflow management systems in different organization became indispensable. If the interfaces between the two have standardized specification, it will be easy to add module to workflow system. Therefore, we suggest a workflow engine independent interoperability module for workflow system using workflow interface 2. This approach will provide the portability with the interoperation support module.

Races might result in unintended nondeterministic execution of parallel programs and thus race detection is one of the critical issues to be resolved in debugging of shared-memory parallel programs. On-the-fly race detection techniques have been developed as one of approaches for the problem. However on-the-fly race detection techniques suffer from the huge run-time overhead because the whole execution behavior of the program being debugged must be monitored at run-time. In this paper we present a practical loop transform technique which can significantly reduce the monitoring overhead required for detecting races on-the-fly in parallel programs. Our technique achieves the improvement by minimizing the number of iteration counts to be monitored of each parallel loop by transforming the original loop with the technique. An experimental performance measurement of our technique shows dramatic improvement on the monitoring overhead and it detects more races than those detected by traditional on-the-fly techniques.

Mapping virtual process topology to physical processor topology is one of the most important issues in parallel computing. The mapping problem for switch-based cluster systems of irregular topology is very complicated due to the connection irregularity and routing complexity. This paper proposes two mapping schemes for irregular cluster systems, which try to map the nearest neighbors in the process topology to physically adjacent processors. In addition, an application-oriented performance metric, weighted cardinality, is introduced to represent the quality of mapping. A simulation study shows that, for a virtual topology of a 16/spl times/16 mesh, the proposed mapping schemes result in better mapping quality and about 15/spl sim/20% shorter communication latency compared to random mapping. The proposed algorithms should also be beneficial when they are applied to metacomputing and cluster of cluster systems, where the communication costs are an order of magnitude different depending on the relative position of the processor nodes.

This paper proposes a Barrier Tree for Meshes (BTM) to minimize the barrier synchronization latency for two-dimensional (2D) meshes. The proposed BTM scheme has two distinguishing features. First, the synchronization tree is 4-ary. The synchronization latency of the BTM scheme is asymptotically /spl theta/(log/sub 4/ n), while that of the fastest scheme reported in the literature is bounded between /spl Omega/(log/sub 3/ n) and /spl theta/(n/sup 1/2/), where n is the number of member nodes. Second, nonmember nodes are neither involved in the construction of a BTM nor actively participate in the synchronization operations, which avoids interference among different process groups during synchronization. This not only results in low setup overhead, but also reduces the synchronization latency. The low setup overhead is particularly effective for the dynamic process model provided in MPI-2. Extensive simulation study shows that, for up to 64/spl times/64 meshes, the BTM scheme results in about 40/spl sim/70 percent shorter synchronization latency and is more scalable than conventional schemes.