• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.683-690
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• 2006

Reducing power consumption to extend network lifetime is one of the most important challenges in designing wireless sensor networks. One promising approach to conserving system energy is to keep only a minimal number of sensors active and put others into low-powered sleep mode, while the active sensors can maintain a connected covet set for the target area. The problem of computing such minimum working sensor set is NP-hard. In this paper, a centralized Voronoi tessellation (CVT) based approximate algorithm is proposed to construct the near optimal cover set. When sensor's communication radius is at least twice of its sensing radius, the covet set is connected at the same time; In case of sensor's communication radius is smaller than twice of its sensing radius, a connection scheme is proposed to calculate the assistant nodes needed for constructing the connectivity of the cover set. Finally, the performance of the proposed algorithm is evaluated through theoretical analysis and extensive numerical experiments. Experimental results show that the proposed algorithm outperforms the greedy algorithm in terms of the runtime and the size of the constructed connected cover set.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.538-547
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• 2015

The goal of this study is to develop an algorithm to propose optimal deployment of detection sensor nodes in the target area, based on a performance surface, which represents detection performance of active and passive acoustic sonar systems. The performance surface of the active detection system is calculated from the azimuthal average of maximum detection ranges, which is estimated with a transmission loss and a reverberation level predicted using ray-based theories. The performance surface of the passive system is calculated using the transmission loss model based on a parabolic equation. The optimization of deployment configurations is then performed by a hybrid method of a virtual force algorithm and a particle swarm optimization. Finally, the effectiveness of deployment configurations is analyzed and discussed with the simulation results obtained using the algorithm proposed in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.676-682
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• 2018

In wireless sensor networks, we propose an optimization algorithm in order to minimize the consumed energy of nodes for maritime search and rescue. In the marine environment, search and rescue operations are mainly performed on the surveillance side and passively on the rescued side. A self-configurable wireless sensor network can build a system that can send rescue signals in the operations. A simulated annealing algorithm is proposed to minimize the consumed energy of nodes in the networks with many nodes. As the density of nodes becomes higher, the algorithmic computation will increase highly. To search the good result in a proper execution time, the proposed algorithm proposes a new neighborhood generating operation and improves the efficiency of the algorithm. The proposed algorithm was evaluated in terms of the consumed energy of the nodes and algorithm execution time, and the proposed algorithm performed better than other optimization algorithms in the performance results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.437-441
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• 2006

Wireless sensor network consists of sensor nodes which are disseminated closely to each other to collect informations for the various requests of a sensor application applied for sensing phenomenons in real world. Each sensor node delivers sensing informations to an end user by conducting cooperative works such as processing and communicating between sensor nodes. In general, the power supply of a sensor node is depends on a battery so that the power consumption of a sensor node decides the entire life time of a sensor network. To resolve the problem, optimal routing algorithm can be used for prolong the entire life time of a sensor network based on the information on the energy level of each sensor node. In this paper, different from the existing Directed Diffusion and SPTN method, we presents a data dissemination protocol based on lower-power consumption that effectively maximizes the whole life time of a sensor network using the informations on the energy level of a sensor node and shortest-path hops. With the proposed method, a data transfer path is established using the informations on the energy levels and hops, and the collected sensing information from neighboring nodes in the event-occurring area is merged with others and delivered to users through the shortest path.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.91-96
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• 2014

Since the sensors of Internet of Things (IOT) collect various data, the lifetime of sensor network is very important and the data should be aggregated efficiently. The contiguous collection by the certain sensors occurs an excessive battery consumption and successive transmission of same value of data should be avoided. To solve these things, we propose an weight-based cluster head replacement method that divides whole network into several grids and cluster head is selected by remaining energy, density of alive sensors and location of sensor. The aim of algorithm maximizes the lifetime of network. Our simulation results shows that the proposed method is very simple as well as balances energy consumption.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.12-19
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• 2020

The clustered heterogeneous wireless sensor network is comprised of sensor nodes and cluster heads, which are hierarchically organized for different objectives. In the network, we should especially take care of managing node resources to enhance network performance based on memory and battery capacity constraints. For instances, if some interesting events occur frequently in the vicinity of particular sensor nodes, those nodes might receive massive amounts of data. Data congestion can happen due to a memory bottleneck or link disconnection at cluster heads because the remaining memory space is filled with those data. In this paper, we utilize drones as mobile sinks to resolve data congestion and model the network, sensor nodes, and cluster heads. We also design a cost function and a congestion indicator to calculate the degree of congestion. Then we propose a data congestion map index and a data congestion mapping scheme to deploy drones at optimal points. Using control variable, we explore the relationship between the degree of congestion and the number of drones to be deployed, as well as the number of drones that must be below a certain degree of congestion and within communication range. Furthermore, we show that our algorithm outperforms previous work by a minimum of 20% in terms of memory overflow.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.14-22
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• 2007

Wireless sensor networks are composed of numerous sensor nodes and exchange or recharging of the battery is impossible after deployment. Thus, sonsor nodes must be very energy-efficient. As neighboring sensor nodes generally have the data of similar information, duplicate transmission of similar information is usual. To prevent energy wastes by duplicate transmissions, it is advantageous to organize sensors into clusters. The performance of clustering scheme is influenced by the cluster-head election method and the size or the number of clusters. Thus, we should optimize these factors to maximize the energy efficiency of the clustering scheme. In this paper, we propose a new energy consumption model for LEACH which is a well-known clustering protocol and determine the optimal number of clusters based on our model. Our model has accuracy over 80% compared with the simulation and is considerably superior to the existing model of LEACH.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.