• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.901-920
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• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.

• 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.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2840-2842
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• 2000

This paper describes sensor-based occupancy grid map construction method through complete coverage navigation algorithm in unknown environment. In this paper, we use the updated Baysian model for probabilistic grid map. For map construction, complete coverage navigation method in which mobile robot can navigate complete field through as short path as possible in unknown environment, is used. The computer simulations result show that map construction method using complete coverage algorithm is efficient.

• 한국정보통신설비학회:학술대회논문집
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• 2005.08a
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• pp.349-357
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• 2005

In this paper, a new clustering algorithm using the Guaranteed Minimum Coverage (GMC) is proposed. In the new protocol, an appropriate distribution of clusterheads is accomplished by guaranteeing a stochastic coverage at each clusterhead(CH)s. Using this protocol, the communication cost from clusterheads to their member nodes and the load variance in each clusterheads are reduced. Therefore, the network lifetime can be extended and the fair energy consumption for all CHs can be achieved

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.101-106
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• 2016

A femtocell network has been attracting attention as a promising solution for providing high data rate transmission over the conventional cellular network in an indoor environment. In this paper, we propose a distributed power control algorithm considering both indoor coverage and data load balancing in the femtocell network. As data traffic varies by time and location according to user distribution, each femto base station suffers from an unbalanced data load, which may degrade network performance. To distribute the data load, the base stations are required to adjust their transmission power dynamically. Since there are a number of base stations in practice, we propose a distributed power control algorithm. In addition, we propose the simple algorithm to detect the faulty base station and to recover coverage. We also explain how to insert a new base station into a deployed network. We present the simulation results to evaluate the proposed algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.10
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• pp.708-714
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• 2014

Wireless sensor networks (WSNs) generally consist of densely deployed sensor nodes that depend on batteries for energy. Having a large number of densely deployed sensor nodes causes energy waste and high redundancy in sensor data transmissions. The problems of power limitation and high redundancy in sensing coverage can be solved by appropriate scheduling of node activity among sensor nodes. In this paper, we propose a cellular automata based node scheduling algorithm for prolonging network lifetime with a balance of energy savings among nodes while achieving high coverage quality. Based on a cellular automata framework, we propose a new mathematical model for the node scheduling algorithm. The proposed algorithm uses local interaction based on environmental state signaling for making scheduling decisions. We analyze the system behavior and derive steady states of the proposed system. Simulation results show that the proposed algorithm outperforms existing protocols by providing energy balance with significant energy savings while maintaining sensing coverage quality.

• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.304-309
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• 2007

This paper describes an efficient path generation method for area coverage. Its applications include robots for de-mining, cleaning, painting, and so on. Our method is basically based on a divide and conquer strategy. We developed a novel cell decomposition algorithm that divides a given area into several cells. Each cell is covered by a robot motion that requires minimum time to cover the cell. Using this method, completeness and time efficiency of coverage are easily achieved. For the completeness of coverage in dynamic environments, we also propose a path following method that makes the robot cover missed areas as a result of the presence of unknown obstacles. The effectiveness of the method is verified using computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.789-794
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• 2005

This paper describes an algorithm for minimal turning complete coverage Path planning for cleaning robots. This algorithm divides the whole cleaning area by cellular decomposition, and then provides the path planning among the cells employing a flow network. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The minimal turning of the robots is directly related to the faster motion and energy saving. The proposed algorithm is compared with previous approaches in simulation and the result shows the validity of the algorithm.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.8
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• pp.99-109
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• 1992

First, the coupling faults of semiconductor memory are classified in detail. The chained coupling fault is introduced and defined, which results from sequential influencing of the coupling effects among memory cells, and its mapping relation is described. The linked coupling fault and its order are defined. Second, the deterministic “Algorithm GA” is proposed, which detects stuack-at faults, transition faults, address decoder faults, unlinked 2-coupling faults, and unlinked chained coupling faults. The time complexity and the fault coverage are improved in this algorithm. Third, it is proved that the march test of an address sequence can detect 97.796% of the linked 2-coupling faults with order 2. The deterministic “Algorithm NA” proposed can detect to the limit. The time complexity and the fault coverage are improved in this algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3171-3191
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• 2016

Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.