• Journal of Communications and Networks
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• v.17 no.1
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• pp.84-92
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• 2015

Multi-hop relay networks have been widely considered as a promising solution to extend the coverage area and to reduce the deployment cost by deploying the relay stations (RSs) in mobile communication systems. Suitable deployment for the RSs is one of the most important features of the demand nodes (DNs) to obtain a high data transmission rate in such systems. Considering a tradeoff among the network throughput, the deployment budget, and the overall coverage of the systems, efficient RS deployment schemes and corresponding algorithms must be developed and designed. A novel cluster-based RS deployment scheme is proposed in this paper to select the appropriate deployment locations for the relay stations from the candidate positions. To make an ideal cluster distribution, the distances between the DNs are calculated when deploying the RSs. We take into account the traffic demands and adopt a uniform cluster concept to reduce the data transmission distances of the DNs. On the basis of the different candidate positions, the proposed scheme makes an adaptive decision for selecting the deployment sites of the RSs. A better network throughput and coverage ratio can be obtained by balancing the network load among the clusters. Simulation results show that the proposed scheme outperforms the previously known schemes in terms of the network throughput and the coverage ratio. Additionally, a suitable deployment budget can be implemented in multi-hop relay networks.

• International Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.1-6
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• 2020

A deployment mechanism is designed to stow into a small volume efficiently. The panels are fabricated by carbon fiber reinforced plastics (CFRPs). The parameters for the deployment are determined by considering the number of panels, the folding/twisting angles, and the driving force for a deployment device. In addition, a surface accuracy of the manufactured reflector is measured through a photogrammetry methodology. The deployment behavior of CFRP reflector is observed by using the zero-gravity device which compensates the gravity effect during the deployment. The zero-gravity device is constructed wire, motor, controller and loadcell. During the deployment of the reflector panel, the wire and motor compensate for its weight by the feedback process of the controller. Tests result show that a zero-gravity device compensates for the weight of the panel during the deployment of the CFRP reflector.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.161-168
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• 2008

The deployable space structure is necessary to minimize the satellite volume and launch cost. For the deployment, passive deployment mechanism has widely been used to attenuate a latch shock induced when the structure is just fully deployed. To reduce the latch shock, viscous damper is applied to the passive deployment mechanism and it can control the deployment speed of the structure. In this paper, dynamic analysis of the deployable space structure using the passive deployment mechanism with the viscous damper has been performed. The viscous damping values have been optimized through numerical simulation. The satellite's attitude influenced by pyro activation for the release of the structure has also been investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3823-3840
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• 2017

This paper analyzes the nodes deployment optimization problem in energy constrained wireless sensor networks, which multi-hop cooperative beamforming (CB) based cooperative-multi-input-single-output (CMISO) transmission is adopted to reduce the energy consumption. Firstly, we establish the energy consumption models for multi-hop SISO, multi-hop DSTBC based CMISO, multi-hop CB based CMISO transmissions under random nodes deployment. Then, we minimize the energy consumption by searching the optimal nodes deployment for the three transmissions. Furthermore, numerical results present the optimal nodes deployment parameters for the three transmissions. Energy consumption of the three transmissions are compared under optimal nodes deployment, which shows that CB based CMISO transmission consumes less energy than SISO and DSTBC based CMISO transmissions. Meanwhile, under optimal nodes deployment, the superiorities of CB based CMISO transmission over SISO and DSTBC based CMISO transmissions can be more obvious when path-loss-factor becomes low.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.990-1013
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• 2015

Deploying sensors into a target region is a key issue to be solved in building a wireless sensor network. Various deployment algorithms have been proposed by the researchers, and most of them are evaluated under the ideal conditions. Therefore, they cannot reflect the real environment encountered during the deployment. Moreover, it is almost impossible to evaluate an algorithm through practical deployment. Because the deployment of sensor networks require a lot of nodes, and some deployment areas are dangerous for human. This paper proposes a deployment approach to solve the problems mentioned above. Our approach relies on the satellite images and the Virtual Force Algorithm (VFA). It first extracts the topography and elevation information of the deployment area from the high resolution satellite images, and then deploys nodes on them with an improved VFA. The simulation results show that the coverage rate of our method is approximately 15% higher than that of the classical VFA in complex environment.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.354-365
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• 2013

Node deployment is one of the important problems in achieving good quality of service in wireless sensor network. The purpose of this paper is to develop an interactive system that supports user's decision makings in designing sensor fields. The system provides grid-based initial deployment algorithm supporting three types of node deployment pattern, area-fill, path-cover, and barrier-cover deployment pattern. After initial deployment, an iterative refinement algorithm can be applied, which takes care of the irregularity of the deployment area and the heterogeneity of sensors. The proposed system helps users to effectively deploy nodes in the sensor field, analyse the detection performance of the deployment, and perform network simulations. The developed system can be utilized as a part of the development environment of the surveillance sensor network system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.383.1-383
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• 2002

This research is concerned with ground experiments for satellite solar array deployment. The dynamic characteristics of the strain energy hinge is very important since it affects the shape and the speed of the solar array deployment. the rapid deployment results in overshoot and undesirable residual vibrations. The experimental results are in good agreement with theoretical results.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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• pp.1138-1143
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• 2006

Sensor deployment is an important issue in the mobile wireless sensor network. In this paper, we propose a deployment algorithm for mobile sensor network to spread out mobile sensor nodes widely as well as regularly. Since the proposed algorithm uses tree topology in deploying the sensor nodes, calculating power as well as spreading speed can be reduced compare to other deployment algorithms. The performance of the proposed algorithm is simulated using NS-2 simulator and demonstrated.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.387-388
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• 2020

As the utilization of microservice architectural style in diverse applications are increasing, the microservice deployment cost became a concern for many companies. We propose an approach to reduce the deployment cost by generating an algorithm which minimizes the cost of basic operation of a physical machine and the cost of resources assigned to a physical machine. This algorithm will produce optimal resource allocation and deployment location based on genetic algorithm process.