• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.182-196
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• 2008

The location estimation using sensor network has been considerably researched. The methods taking the differences of the forms of location estimation between indoors and outdoors into consideration have been studied. While it is possible for outdoor location to be estimated because outdoor location estimation has a consistent distribution during unit period through the value of RSSI(Received Signal Strength Indication) on outdoor location estimation, Indoor location estimation is difficult since multi-path and interference indoors are higher than those outdoors and indoor location estimation can be affected by other factors. In this paper, we revise the information of RSSI changed by multi-path and interference through the Moving Average method and K-means algorithm and propose the method of estimation for the value of RSSI with reliability in the group of signals received during unit period. We also suggest the way to put some weights on fixed nodes in network using a snooping node on location estimation and then evaluate the efficiency of location awareness as compared with the existing method by implementing proposed method on system through the reconfiguration of network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2002-2019
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• 2017

RPL routing protocol for low-power and lossy networks is an Internet Engineering Task Force (IETF) recommended IPv6 based protocol for routing over Low power Lossy Networks (LLNs). RPL is proposed for networks with characteristics like small packet size, low bandwidth, low data rate, lossy wireless links and low power. RPL is a proactive routing protocol that creates a Directed Acyclic Graph (DAG) of the network topology. RPL is increasingly used for Internet of Things (IoT) which comprises of heterogeneous networks and applications. RPL proposes a single path routing strategy. The forwarding technique of RPL does not support multiple paths between source and destination. Multipath routing is an important strategy used in both sensor and ad-hoc network for performance enhancement. Multipath routing is also used to achieve multi-fold objectives including higher reliability, increase in throughput, fault tolerance, congestion mitigation and hole avoidance. In this paper, M-RPL (Multi-path extension of RPL) is proposed, which aims to provide temporary multiple paths during congestion over a single routing path. Congestion is primarily detected using buffer size and packet delivery ratio at forwarding nodes. Congestion is mitigated by creating partially disjoint multiple paths and by avoiding forwarding of packets through the congested node. Detailed simulation analysis of M-RPL against RPL in both grid and random topologies shows that M-RPL successfully mitigates congestion and it enhances overall network throughput.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.609-616
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• 2023

In a wireless network environment, since sensors are not always connected to power, the life of a battery, which is an energy source supplied to sensors, is limited. Therefore, various studies have been conducted to extend the network life, and a layer-based routing protocol, LEACH(: Low-energy Adaptive Clustering Hierarchy), has emerged for efficient energy use. However, the LEACH protocol, which transmits fused data directly to the sink node, has a limitation in that it consumes as much energy as the square of the transmission distance when transmitting data. To improve these limitations, this paper proposes an algorithm that can minimize the transmission distance with multi-hop transmission where cluster heads are chained between cluster heads through relative distance calculation from sink nodes in every round.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1661-1670
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• 2016

In this paper, we present a risk prediction system and customized evacuation pathfinding algorithm in fire scenarios. For the risk prediction, we apply a multi-level clustering mechanism using collected temperature at sensor nodes throughout the network in order to predict the temperature at the time that users actually evacuate. Based on the predicted temperature and its reliability, we suggest an evacuation pathfinding algorithm that finds a suitable evacuation path from a user's current location to the safest exit. Simulation results based on FDS(Fire Dynamics Simulator) of NIST for a wireless sensor network consisting of 47 stationary nodes for 1436.41 seconds show that our proposed prediction system achieves a higher accuracy by a factor of 1.48. Particularly for nodes in the most reliable group, it improves the accuracy by a factor of up to 4.21. Also, the customized evacuation pathfinding based on our prediction algorithm performs closely with that of the ground-truth temperature in terms of the ratio of safe nodes on the selected path, while outperforming the shortest-path evacuation with a factor of up to 12% in terms of a safety measure.

• Journal of KIISE:Information Networking
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• v.35 no.4
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• pp.292-300
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• 2008

In multi-hop wireless networks like Wireless Mesh Networks (WMN) and Wireless Sensor Networks (WSN), nodes often rely on batteries as their power source. In such cases, energy efficient routing is critical. Many schemes have been proposed to find the most energy efficient path, but most of them do not achieve optimality on network lifetime. Once found, the energy efficient path is constantly used such that the energy of the nodes on the path is depleted quickly. As an alternative, the approaches that dynamically change the path at run time have also been proposed. These approaches, however, involve high overhead of establishing multiple paths. In this paper, we first find an optimal multi-path routing using LP. Then we apply an approximation algorithm to derive a near-optimal solution for single-path routing. We compare the performance of the proposed scheme with several other existing algorithms through simulation.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• 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.