• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.803-812
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• 2006

This paper analytically evaluates the FEC(Forward Error Correction) symbol size's effect on the performance and energy consumption of 802.11 protocol with the block FEC algorithm over WSN(Wireless Sensor Network). Since the basic recovery unit of block FEC algorithms is symbols not bits, the FEC symbol size affects the packet correction rate even with the same amount of FEC check bits over a given WSN channel. Precisely, when the same amount of FEC check bits are allocated, the small-size symbols are effective over channels with frequent short bursts of propagation errors while the large ones are good at remedying the long rare bursts. To estimate the effect of the FEC symbol site, the paper at first models the WSN channel with Gilbert model based on real packet traces collected over TIP50CM sensor nodes and measures the energy consumed for encoding and decoding the RS (Reed-Solomon) code with various symbol sizes. Based on the WSN channel model and each RS code's energy expenditure, it analytically calculates the transmission efficiency and power consumption of 802.11 equipped with RS code. The computational analysis combined with real experimental data shows that the RS symbol size makes a difference of up to 4.2% in the transmission efficiency and 35% in energy consumption even with the same amount of FEC check bits.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.375-380
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• 2016

The proliferation of online social networking services (OSNSs) and smartphones has enabled people to easily make friends with a large number of users in the online communities, and interact with each other. This leads to an increase in the usage rate of OSNSs. However, individuals who have immersed into their digital lives, prioritizing the virtual world against the real one, become more and more isolated in the physical world. Thus, their socialization processes that are undertaken only through lots of face-to-face interactions and trial-and-errors are apt to be neglected via 'Add Friend' kind of functions in OSNSs. In this paper, we present a friend recommendation system based on the on/off-line contextual information for the OSNS users to have more serendipitous offline interactions. In order to accomplish this, we modeled both offline information (i.e., place visit history) collected from a user's smartphone on a 3D tensor, and online social data (i.e., friend relationships) from Facebook on a matrix. We then recommended like-minded people and encouraged their offline interactions. We evaluated the users' satisfaction based on a real-world dataset collected from 43 users (12 on-campus users and 31 users randomly selected from Facebook friends of on-campus users).

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.447-458
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• 2002

In this paper, we design and implement Wireless Transaction Protocol(WTP) and evaluate it for wireless transaction processing in mobile computing environments. The design and implementation of WTP are based on the coroutine model that might be suitable for light-weight portable devices. We test the compatibility between our product and the other products such as Nokia, Kannel and WinWAP For the evaluation of WTP, we use an Internet simulator that can arbitrary generate random wireless errors based on the Gilbert model. In our experiment, the performance of WTP is measured and compared to those of Transmission Control Protocol(TCP) and TCP for Transactions. The experiment shows that WTP outperforms the other two protocols for wireless transaction processing in terms of throughput and delay. Especially, WTP shows much higher performance In ease of high error rate and high probability of burst errors. This comes from the fact that WTP uses a small number of packets to process a transaction compared to the other two protocols and introduces a fixed time interval for retransmission instead of the exponential backoff algorithm. The experiment also shows that the WTP performance is optimized when the retransmission counter is set to 5 or 6 in case of high burst error rate.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.345-350
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• 2016

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.247-258
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• 2004

Recently, there have been researches on storage and retrieval technique of moving objects, which are highly concerned by user in database application area such as video databases, spatio-temporal databases, and mobile databases. In this paper, we propose a new signature-based indexing scheme which supports similar sub-trajectory retrieval at well as good retrieval performance on moving objects trajectories. Our signature-based indexing scheme is classified into concatenated signature-based indexing scheme for similar sub-trajectory retrieval, entitled CISR scheme and superimposed signature-based indexing scheme for similar sub-trajectory retrieval, entitled SISR scheme according to generation method of trajectory signature based on trajectory data of moving object. Our indexing scheme can improve retrieval performance by reducing a large number of disk access on data file because it first scans all signatures and does filtering before accessing the data file. In addition, we can encourage retrieval efficiency by appling k-warping algorithm to measure the similarity between query trajectory and data trajectory. Final]y, we evaluate the performance on sequential scan method(SeqScan), CISR scheme, and SISR scheme in terms of data insertion time, retrieval time, and storage overhead. We show from our experimental results that both CISR scheme and SISR scheme are better than sequential scan in terms of retrieval performance and SISR scheme is especially superior to the CISR scheme.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.337-350
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• 2014

Internet-of-Things(IoT) is the computing paradigm converged with different technologies, where diverse devices are connected via the wireless network, acquire environmental information from their equipped sensors, and are actuated. IoT applications provide smart services to users by interacting with multiple devices connected to the network. IoT devices provide the simple set of the information and also offer smart services by collaborating with other devices. That is, IoT applications always interact with IoT devices which are becoming very popular at a fast pace. However, due to this fact, developing IoT application results in unconventional technical challenges which have not been observed in typical software applications. Moreover, since IoT computing has its own characteristics which are distinguished from other former paradigms such as embedded computing and mobile computing, IoT applications also reveal their own technical challenges. Therefore, we analyze technical challenges occurring in developing IoT applications and present effective solutions to overcome the challenges. To verify identified issues and presented solutions, we present the result of performing a case study of developing an IoT application. Through the case study, we verify how the unconventional technical issues are raised in a real domain and analyze effectiveness of applying the solutions to the application.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.11
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• pp.393-402
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• 2014

We have experienced an explosive increase of the IoT(Internet of Things) technology based devices including smart phones and the wireless communications. Also the growing power consumption in IEEE 802.11 WLANs(Wireless LANs) driven by these dramatic increases in not only mobile users and but also wireless APs(Access Points). To reduce the power consumption, this paper proposes a wireless AP power saving algorithm, which minimizes the transmission power without decrease the transmission and carrier sense ranges. A wireless AP which is use in our algorithm checks its own original coverage periodically for whether there is a new STA(Station) or not when its transmission power is decreased. Moreover, if there are no signaling message to connect the wireless AP, it changes its operation mode Wake-up to sleep. A Result shows that the proposed AP algorithm can reduce the total power consumption of the wireless AP approximated 18% and 35% compared to the conventional wireless AP with and without the existing power saving algorithm, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.