• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.107-114
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• 2011

Location-Based Service(LBS) is a service that provides a variety of convenience in life using location information that can be obtained by mobile communication network or satellite signal. In order to provide LBS precisely and efficiently, we need the location determination technology, platform technology and server technology. In this study, we studied on how we can reduce the error on location determination of objects such people and things. Fingerprint location determination method was applied to this study because it can be used at current wireless communication infrastructure and less influenced by a variety of noisy environment than other location determination methods. We converted the probability value to logarithmic scale value because using the sum of k probability values is not suitable to be applied to weight determination. In order to confirm the performance of suggested method, we developed location determination test program with Visual Basic 6.0 and performed the test. According to indoor and outdoor test results, the suggested stochastic method reduced the distance error by 17%, 18% and 9% respectively at indoor environment and 25%, 11% and 4% at outdoor environment compared with deterministic NN, kNN and kWNN fingerprint methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.147-156
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• 2005

In developing the ubiquitous computing technology based services (ubiquitous services), evaluating how much value of those services is created or added is very crucial. The efforts to evaluate the ubiquitous services have been progressed in two perspectives - technical perspective and behavioral perspective. Despite its importance, however, the methodologies which integrate both perspectives have been still very rare. Hence, this paper aims perspectives have been still very rare. Hence, this paper aims to propose an integrated ubiquitous service valuation methodology based on the multi-layered approach including technical and behavioral perspectives. To do so, we conducted a case study with currently existing location based service (LBS) such as navigation systems by conducting focus group interview (FGI) and field survey.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.526-533
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• 2016

The researches protecting user's location in location-based services(LBS) have received much attention. Especially k-anonymity is the most popular privacy preservation method. k-anonymization means that it selects k-1 other dummies or clients to make the cloaking region. This reduced the probability of the query issuer's location being exposed to untrusted parties to 1/k. But query's location may expose to adversary when k-1 dummies are concentrated in query's location or there is dummy in where query can not exist. Therefore, we proposed the dummy system model and algorithm taking the real environment into account to protect user's location privacy. And we proved the efficiency of our method in terms of experiment result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2827-2848
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• 2021

Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group under considerations of privacy and efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.611-618
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• 2009

Recently, an indoor location-aware technology has been focused on as a key technology for context awareness in ubiquitous computing environments. The conventional Cricket system was designed with a non-centralized architecture, which has advantages in terms of user privacy, deployment, scalability, decentralized administration, network heterogeneity, and low cost. In this paper, an indoor location system based on TDOA between RF and ultrasound signals is designed, which improves the Cricket system. A 2.4GHz frequency is employed for transmitting RF messages, which is in an ISM band. The beaconing frequency is doubled to enhance the channel utilization rate. The ultrasonic pulse duration is optimized to increase the coverage of ultrasonic signals. The function of calculating location coordinates is embedded in a listener. The location-update rate and location accuracy are also improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2968-2974
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• 2014

When lighting has installed indoor, we control lighting using human-detecting sensors for people who pass at night and places that are lack of quantity of light. However, malfunction can be caused by positions of inappropriate sensors, and in the case of passages of big buildings, it is a problem that even after a person pass, light apparatuses are turned on for a long time. In this paper, we propose lighting control system based on location positioning for energy saving that control lighting in accordance with passers's position through indoor location positioning. This system use the fingerprinting technology that is one of the location positioning technologies and RSSI data that is collected by a smart device. Using those, it can turn on only lightings that are included in the positioned location and reduce unnecessary power consumption. As a result of experiment, on condition that four people were existing and illumination was 308 lux, we assured reduction effect of 49 Wh.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1528-1533
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• 2020

Recently, indoor location recognition technology using Wi-Fi fingerprints has been applied and operated in various industrial fields and public services. Along with the interest in machine learning technology, location recognition technology based on machine learning using wireless signal data around a terminal is rapidly developing. At this time, in the process of collecting radio signal data required for machine learning, the accuracy of location recognition is lowered due to distorted or unsuitable data for learning. In addition, when location recognition is performed based on data collected at a specific location, a problem occurs in location recognition at surrounding locations that are not included in the learning. In this paper, we propose a learning data preprocessing technique to obtain an improved position recognition result through the preprocessing of the collected learning data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.166-179
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• 2021

Recently, with the development of technology, computer vision research based on the human visual system has been actively conducted. Saliency maps have been used to highlight areas that are visually interesting within the image, but they can suffer from low performance due to external factors, such as an indistinct background or light source. In this study, existing color, brightness, and contrast feature maps are subjected to multiple shape and orientation filters and then connected to a fully connected layer to determine pixel intensities within the image based on location-based weights. The proposed method demonstrates better performance in separating the background from the area of interest in terms of color and brightness in the presence of external elements and noise. Location-based weight normalization is also effective in removing pixels with high intensity that are outside of the image or in non-interest regions. Our proposed method also demonstrates that multi-filter normalization can be processed faster using parallel processing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.265-271
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• 2023

Recently, location information services are gradually expanding due to the development of information and communication technology. RSSI is widely used to extract indoor and outdoor locations. The indoor and outdoor location estimation methods using RSSI are less accurate due to the influence of radio wave paths, interference, and surrounding wireless devices. In order to improve this problem, a distance estimation method that takes into account the wireless propagation environment is necessary. Therefore, in this study, we propose a distance estimation algorithm that takes into account the radio wave environment. The proposed method estimates the distance by learning RSSI input and output considering the RBF neural network and the propagation environment. To evaluate the performance of the proposed method, the performance of estimating the location of the receiver within a range of up to 55[m] using a BLE beacon transmitter and receiver was compared with the average filter and Kalman filter. As a result, the distance estimation accuracy of the proposed method was 6.7 times higher than that of the average filter and Kalman filter. As shown in the results of this performance evaluation, if the method of this study is applied to location services, more accurate location estimation will be possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.