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

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.12B
• /
• pp.1670-1679
• /
• 2011

In the area of wireless sensor networks, sensor coverage and network connectivity problems are caused by a limited detection range and the communication distance of the nodes. To solve the coverage and connectivity problems, many studies are suggested, but most research is restricted to apply into the real environment because they didn't consider various environmental factors on wireless sensor network deployment. So in this paper, we propose a node deployment strategy considering environmental factors and the number of nodes in surveillance and reconnaissance sensor networks(SRSN). The proposed node deployment method divides the installation of the surveillance and reconnaissance sensor networks system into four steps such as identification of influences factors for node placement through IPB process, sensor node deployment based on sensing range, selection of monitoring site, and relay node deployment based on RF communication range. And it deploys the sensor nodes and relay nodes considered the features of the surveillance and reconnaissance sensor network system and environmental factors. The result of simulation indicates that the proposed node deployment method improves sensor coverage and network connectivity.

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

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

• The KIPS Transactions:PartA
• /
• v.13A no.1 s.98
• /
• pp.45-52
• /
• 2006

To perform on-line maintenance for Distributed Information System, it is indispensable to disseminate files to participant nodes in the network. When users' requests for file deployment occur simultaneously in a short period a deployment server falls into overload phase, which is often called Flash Crowds. h common solution to avoid Flash Crowds is to increase hardware capacity. In this paper, we propose a software solution based on P2P, which does not cost any additional expense. In the proposed solution, nodes in the network are grouped into subnetworks one of which is composed of only neighboring nodes. In each subnetwork, copies of deployment files can be transferred to each other. Consequently, it brings about the effect of load balancing in deployment server. To raise the effectiveness, target files for deployment are packed into one package. Before being transferred, each package is divided into multiple equal-sized segments. A deployment server in a normal phase transmits a package requested from nodes in segment units. However a deployment server is overloaded, if segments already exist in the subnetwork participant nodes in the subnetwork receive necessary segments from neighboring nodes. In this paper, we propose data structures and algorithm for this approach and show performance improvement through simulation.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.4C
• /
• pp.338-346
• /
• 2012

Wireless sensor network (WSN) technology provides a variety of medical and healthcare solutions to assist detection and communication of body conditions. However, data reliability inside WSN might be influenced to healthcare routing protocol due to limited hardware resources of computer, storage, and communication bandwidth. For this reason, we have conducted various wireless communication experiments between nodes using parameters such as RF strength, battery status, and deployment status to get a optimal performance of mobile healthcare routing protocol. This experiment may also extend the life time of the nodes. Performance analysis is done to obtain some important parameters in terms of distance and reception rate between the nodes. Our experiment results show optimal distance between nodes according to battery status and RF strength, or deployment status and RF strength. The packet reception rate according to deployment status and RF strength of nodes was also checked. Based on this performance evaluation, the optimized sensor node battery and deployment in the developed our mobile healthcare routing protocol were proposed.

• Journal of IKEEE
• /
• v.11 no.3
• /
• pp.100-107
• /
• 2007

One of the fundamental problems in wireless sensor networks is the efficient deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation and a experiment using two rectangular and one L shape area. We found the minimum number of sensor nodes for the complete coverage of modeled area, and discovered the optimum location of each nodes. The real deploy experiment using sensor nodes shows the 94.6%, 92.2% and 95.7% error free communication rate respectively.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.99-100
• /
• 2006

One of the fundamental problems in sensor networks is the deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation using two dimensional L shape model. The actual length of the L shape model is about 100m each. We found the minimum number of 15 nodes are sufficient for the complete coverage of modeled area. We also found the optimum location of each nodes. The real deploy experiment using 15 sensor nodes shows the 95.7%. error free communication rate.