• 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.

• 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.

• 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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1568-1589
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• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.111-134
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• 1999

Although the role of information system has been rapidly increasing in modern management, there have been few researches on the quality assurance in information system. This paper suggests how to apply quality function deployment(QFD) to information system development in order to build up user-centered quality assurance system. The main focus of the paper is how to design the linkages between management strategy, information systems, and the users using quality function deployment.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.169-174
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• 2014

Practical deployment methods for sensor nodes are demanding as applications using sensor nodes increase. In particular, node connectivity is crucial not only for the network longevity but also for direct impacts on sensing and data collection capability. Economic requirement at building sensor networks and often limited access for sensor fields due to hostile environment force to remain at random deployment from air. However, random deployment often result in lost connection problem and inefficient network topology issue due to node irregularity. In this paper, mixed deployment of key nodes that have better communication capability is proposed to support the original deployment into working in an efficient way. Node irregularity is improved by introducing mixed nodes and an efficient mixed node density is also analyzed. Simulation results show that the mixed deployment method has better performance than the existing deployment methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.973-980
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• 2009

Many studies which have been done for efficient installation and management of wireless sensor networks (WSN) include energy savings, key managements and sensor deployments. Sensor deployment problem is one of the most important and fundamental issues among them in that the topic is directly related with the system cost and performance. In this paper, we suggest a sensor deployment scheme that reduces the system cost of WSN while satisfying the fundamental system requirements of connectivity between sensor nodes and sensing coverage. Using graphical information system(GIS) which contains region-dependent information related with connectivity condition, the initial positions of sensors in the procedure simulated annealing (SA) are determined. The GIS information helps in reducing system cost reduction not only at the initial deployment of SA but also at the final deployment of SA which is shown by computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2173-2193
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• 2013

In order to reduce the distribution cost of sensor nodes, a mobile sensor deployment has been proposed. The mobile sensor deployment can be solved by finding the optimal layout and planning the movement of sensor nodes with minimum energy consumption. However, previous studies have not sufficiently addressed these issues with an efficient way. Therefore, we propose a new deployment approach satisfying these features, namely a tree-based approach. In the tree-based approach, we propose three matching schemes. These matching schemes match each sensor node to a vertex in a rake tree, which can be trivially transformed to the target layout. In our experiments, the tree-based approach successfully deploys the sensor nodes in the optimal layout and consumes less energy than previous works.