• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.3
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• pp.223-233
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• 2014

As a broadcast message authentication method in wireless sensor networks, ${\mu}$TESLA enables sensor nodes efficiently authenticate message from base station (BS). However, if we use ${\mu}$TESLA that has very short length of key slot in unattended wireless sensor network (UWSN), sensors may calculate a huge amount of hashs at once in order to verify the revealed secret key. In contrast, if we set the length of ${\mu}$TESLA's key slot too long in order to reduce the amount of hashs to calculate, BS should wait out the long slot time to release key. In this paper, we suggest variable key slot ${\mu}$TESLA in order to mitigate the problem. As showing experiment results, we prove that our suggestion improve sensor node's response time and decrease of number of hash function calculation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.26-37
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• 2009

This paper suggests a sensor's placement method for detecting the dilemma zone problem when real-time driver's safety service is provided at signalized intersections by multiple pointed traffic sensors using USN environments. For detecting the dangerous situations from vehicles accelerating through yellow intervals, red-light running and stopping abruptly like as dilemma zone problem, VISSIM(microscopic, behavior-based multi-purpose traffic simulation program) is used to perform a real-time multiple detection situation by changing the input data like as various inflow-volume, design speed change, driver perception and response time. As a result, the optimal interval of traffic sensors is 20~27m, and the initialized sensor location from stop-line is different according to road design speed. Moreover, the pattern of detection about dilemma zone is also different according to inflow-volumes. This paper shows that the method is useful to evaluate the sensor's placement problem based on micro-simulation and the results can be used as the basic research for USN services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.784-795
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• 2009

An accurate positioning estimation in the wireless sensor networks (WSN) is very important in which each sensor node is aware of neighbor conditions. The multi-hop positioning estimation technique is considered as one of the suitable techniques for the WSN with many low power devices. However geographical holes, where there is no sensor node, may severely decrease the positioning accuracy so that the positioning error can be beyond the tolerable range. Therefore in this paper, we analyze error factors of DV-hop and hole effect to obtain node's accurate position. The proposed methods include boundary node detection, distance level adjustment, and unreliable anchor elimination. The simulation results show that the proposed method can achieve higher positioning accuracy using the hole detection and enhanced distance calculation methods compared with the conventional DV-hop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5744-5764
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• 2018

In Wireless Sensor Networks (WSNs) for Internet of Things (IoT) environment, fault tolerance is a most fundamental issue due to strict energy constraint of sensor node. In this paper, a robust energy saving data dissemination protocol for IoT-WSNs is proposed. Minimized energy consumption and dissemination delay time based on signal strength play an important role in our scheme. The representative dissemination protocol SPIN (Sensor Protocols for Information via Negotiation) overcomes overlapped data problem of the classical Flooding scheme. However, SPIN never considers distance between nodes, thus the issue of dissemination energy consumption is becoming more important problem. In order to minimize the energy consumption, the shortest path between sensors should be considered to disseminate the data through the entire IoT-WSNs. SPMS (Shortest Path Mined SPIN) scheme creates routing tables using Bellman Ford method and forwards data through a multi-hop manner to optimize power consumption and delay time. Due to these properties, it is very hard to avoid heavy traffic when routing information is updated. Additionally, a node failure of SPMS would be caused by frequently using some sensors on the shortest path, thus network lifetime might be shortened quickly. In contrast, our scheme is resilient to these failures because it employs energy aware concept. The dissemination delay time of the proposed protocol without a routing table is similar to that of shortest path-based SPMS. In addition, our protocol does not require routing table, which needs a lot of control packets, thus it prevents excessive control message generation. Finally, the proposed scheme outperforms previous schemes in terms of data transmission success ratio, therefore our protocol could be appropriate for IoT-WSNs environment.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.418-427
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• 2020

The measurement of meat freshness at meat markets is important for the health of consumers. Currently a variety of sensors have been studied for the measurement of the meat freshness. Therefore, the analysis of sensor data is needed for the reduction of measurement errors. In this paper, we analyze the freshness measurement data of ten sensors based on deep learning. The measured data are composed of beef, pork and chicken, whose reliability and noise-robustness are examined by a deep neural network. Further, to search for multiple sensors better than a torrymeter, PCA (principle component analysis) is carried. Then, we validated that the performance of the three sensors outperforms the torrymeter in the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.3
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• pp.126-133
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• 2009

There are already many researches providing u-Healthcare service, but they have left problems to be improved. First of all, the transmission range between sensor nodes and the gateway are restricted. Hence, patients feel uncomfortable because of they need to possess or locate closed to a gateway all the time when they aggregates their medical data. Also, the existing systems have not considered life environment that is important to analyze patient's diseases. Moreover, a guardian need to located close to patient or possess a mobile device that monitors a patients' status in real time when they are in outdoor. In this research, we present multi-hop packet transfer algorithm and compilation of life environment which help improve the problem of the existing researches. Likewise, we designed and implemented a medical information database and a real-time web monitoring system that manage patients' personal history and monitor a patients' status in real time. In this paper, we design and implement the u-Healthcare system based on mobile environment and we present a result when we tested our u-Healthcare system in scenario environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.280-291
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• 2009

Skyline Query processing is important to wireless sensor applications in order to process multi-dimensional data efficiently. Most skyline researches about sensor network focus on minimizing the energy consumption due to the battery powered constraints. In order to reduce energy consumption, Filtering Method is proposed. Most existing researches have assumed a snapshot skyline query processing and do not consider continuous queries and use data generated in ancestor node. In this paper, we propose an energy efficient method called Bottom up filtering tuple selection for continuous skyline query processing. Past skyline data generated in child nodes are stored in each sensor node and is used when choosing filtering tuple. We also extend the algorithms, called Support filtering tuple(SFT) that is used when we choose the additional filtering tuple. There is a temporal correlation between previous sensing data and recent sensing data. Thus, Based on past data, we estimate current data. By considering this point, we reduce the unnecessary communication cost. The experimental results show that our method outperforms the existing methods in terms of both data reduction rate(DRR) and total communication cost.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.33-39
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• 2016

With recent advances in sensor technology, CMOS-based semiconductor devices and networking protocol, the areas for application of wireless sensor networks greatly expanded and diversified. Such diversification of uses for wireless sensor networks creates a multitude of beneficial possibilities for several industries. In the application of wireless sensor networks for monitoring systems' data transmission process from the sensor node to the sink node, transmission through multi-hop paths have been used. Also mobile sink techniques have been applied. However, high energy costs, unbalanced energy consumption of nodes and time gaps between the measured data values and the actual value have created a need for advancement. Therefore, this thesis proposes a new model which alleviates these problems. To reduce the communication costs due to frequent data exchange, a State Prediction Model has been developed to predict the situation of the peripheral node using a geographic autocorrelation of sensor nodes constituting the wireless sensor networks. Also, a Risk Analysis Model has developed to quickly alert the monitoring system of any fatal abnormalities when they occur. Simulation results have shown, in the case of applying the State Prediction Model, errors were smaller than otherwise. When the Risk Analysis Model is applied, the data transfer latency was reduced. The results of this study are expected to be utilized in any efficient communication method for wireless sensor network monitoring systems where all nodes are able to identify their geographic location.