• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.345-350
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• 2021

The maintenance of sensor networks, installed in a wide area has been an issue for a long time. In order to solve this problem, studies to supply energy to a sensor network using a robot has been carried out by several researchers. In this study, for a sensor network consisting of power nodes supplied with energy by multiple robots and sensor nodes around them, we propose a method of allocating a work area using a modified k-means algorithm so that the robots move the minimum distance. Through the simulation study using the energy transfer rate of the robot as a variable, it is shown that nodes of each allocated area can maintain survival, and the validity of the proposed modified k-means algorithm is verified.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.9
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• pp.13-19
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• 2008

Time synchronization of nodes in sensor network synchronizes sensor nodes to one time clock. This is very essential in sensor networks so that the information collected and reported from the sensor nodes becomes meaningful. If sensor nodes are not synchronized, disaster report with time information can be wrong analyzed and this may lead to big calamity. With the limitation of battery and computing power, time synchronization algorithm imported in sensor nodes has to be as simple as it doesn't need big complexity, nor generates many synchronization messages. To reduce the synchronization error, hop count should be kept small between reference node to initiate synchronization and sensor nodes to be synchronized. Therefore, multiple reference nodes are used instead of single reference node. The use of multiple reference nodes introduce the requirement of synchronization among reference nodes in the network. Several algorithms have been proposed till now, but the synchronization among reference nodes are not well considered. This paper proposes improved time synchronization for sensor networks by synchronizing multiple reference nodes inside the network. Through simulation, we validated the effects of new algorithm.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.8-21
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• 2009

A wireless sensor network is a computer network which consists of spatially distributed devices, called sensor nodes. In wireless sensor networks, energy efficiency is a key issue since sensor nodes must resides upon limited energy. To retrieve sensor information without dealing with the network issues, a sensor network is treated as conceptual database on which query can be requested. When multiple queries are requested for processing in a wireless sensor network, energy consumption can be significantly reduced if common partial results among similar queries can be effectively shared. In this paper, we propose an energy efficient multi-query processing technique based on the coverage relationship between multiple queries. When a new query is requested, our proposed technique derives an equivalent query from queries running at the moment, if it is derivable. Our technique first computes the set of running queries that may derive a partial result of the new query and then test if this set covers all the result of the new query attribute-wise and tuple-wise. If the result of the new query can be derived from the results of executing queries, the new query derives its result at the base station instead of being executed in the sensor network.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.311-316
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• 2006

We propose the optical sensor network using optical CDMA with bipolar receiver and modified Pseudorandom Noise codes which are widely used in the wireless communication network. We numerically analyze the performances in the optical sensor network. In the proposed network, multiple access interferences between two sensors are suppressed by performing synchronization between the optical encoder and the optical decoder and adjusting the delay times of optical delay lines. Numerical simulations confirm that the performance can be acquired by suppressing the beat noise among optical signals as the number of sensors increases.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.952-959
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• 2016

This paper proposes a novel indoor positioning system (IPS) that uses a calibrated camera sensor network and dense 3D map information. The proposed IPS information is obtained by generating a bird's-eye image from multiple camera images; thus, our proposed IPS can provide accurate position information when objects (e.g., the mobile robot or pedestrians) are detected from multiple camera views. We evaluate the proposed IPS in a real environment with moving objects in a wireless camera sensor network. The results demonstrate that the proposed IPS can provide accurate position information for moving objects. This can improve the localization performance for mobile robot operation.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.91-94
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• 2006

In this paper, we deployed the tunnel inspection and monitoring system by wireless sensor network. It is shown that the wireless sensor network which is composed of sensor, wireless communication module, and gateway system can be applied to tunnel monitoring system. Sensors included herein are acceleration transducers, fire-alarm sensors, water-level sensors, and magnetic contact sensors. It is also found that the wireless sensor network can deliver sensing data reliably by ad-hoc networking technology. The gateway system that can send the sensing data to server by CDMA (code division multiple access) is developed. Finally, monitoring system is constructed by web service technology, and it is observed that this system can monitor the present state of tunnel without difficulties. Furthermore, the above system provides an alternative to inspect and monitor the tunnel efficiently where the conventional wired system cannot be applied.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8B
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• pp.1003-1011
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• 2011

In this paper, a real-time multi-source sensor data management system based on wireless sensor network is proposed and implemented. The proposed management system is designed to transmit the wireless data to the server in order to monitor and control the multi-source target's status efficiently by analyzing them. The proposed system is implemented to make it possible to control and transmit the wireless sensor data by classifying them, of which data are issued from the clustered sources composed of a number of the remote multiple sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple data sources. From these results, it is verified that the proposed system has a good performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.10
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• pp.50-58
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• 2013

In Wireless Sensor Network, clustering scheme is used to prolong the lifetime of WSN by efficient usage of energy of sensor. In the distributed clustering protocol just like LEACH, every sensor in a network plays a cluster head role once during each epoch. So the FND is prolonged. But, even though every sensor plays a head role, the energy consumed by each sensor is different because the energy consumed increases according to the distance to the Base Station by the way of multiple increase. In this paper, we propose a mechanism to select a head depending on the distance to Base Station, which extends the timing of FND occurrence by 68% compared to the LEACH and makes network stable.