• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.5-9
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• 2005

One of the imminent problems to be solved within wireless sensor network is to balance out energy dissipation among deployed sensor nodes. In this paper, we present a transmission relay method of communications between BS (Base Station) and CHs (Cluster Heads) for balancing the energy consumption and extending the average lifetime of sensor nodes by the fuzzy logic application. The proposed method is designed based on LEACH protocol. The area deployed by sensor nodes is divided into two groups based on distance from BS to the nodes. RCH (Relay Cluster Head) relays transmissions from CH to BS if the CH is in the area far away from BS in order to reduce the energy consumption. RCH decides whether to relay the transmissions based on the threshold distance value that is obtained as a output of fuzzy logic system. Our simulation result shows that the application of fuzzy logic Provides the better balancing of energy depletion and Prolonged lifetime of the nodes.

• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.225-239
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• 2010

This paper introduces a new approach to modeling relative distance among nodes under a variety of communication rates, due to node's mobility in Mobile Ad-hoc Networks (MANETs). When mobile nodes move to another location, the relative distance of communicating nodes will directly affect the data rate of transmission. The larger the distance between two communicating nodes is, the lower the rate that they can use for transferring data will be. The connection certainty of a link also changes because a node may move closer to or farther away out of the communication range of other nodes. Therefore, the stability of a route is related to connection entropy. Taking into account these issues, this paper proposes a new routing metric for MANETs. The new metric considers both link weight and route stability based on connection entropy. The problem of determining the best route is subsequently formulated as the minimization of an object function formed as a linear combination of the link weight and the connection uncertainty of that link. The simulation results show that the proposed routing metric improves end-to-end throughput and reduces the percentage of link breakages and route reparations.

• Journal of IKEEE
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• v.27 no.4
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• pp.679-682
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• 2023

In wireless sensor networks, communication failure between sensor nodes causes continuous connection attempts, which results in a large power loss. In this paper, an appropriate distance between the CH(Cluster Head) node and the communicating sensor nodes is limited so that a group of clusters of appropriate size is formed on a two-dimensional plane. To equalize the cluster size, sensor nodes in the shortest distance communicate with each other to form member nodes, and clusters are formed by gathering nearby nodes. Based on the proposed cluster uniformity algorithm, the improvement rate of cluster uniformity is shown by simulation results. The proposed method can improve the cluster uniformity of the network by about 30%.

• Journal of Sensor Science and Technology
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• v.19 no.5
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• pp.342-348
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• 2010

The wireless sensor networks have been extensively researched. One of the issues in wireless sensor networks is a developing energy-efficient clustering protocol. Clustering algorithm provides an effective way to extend the lifetime of a wireless sensor networks. In this paper, we proposed an energy efficient clustering scheme by adjusting group size. In sensor network, the power consumption in data transmission between sensor nodes is strongly influenced by the distance of two nodes. And cluster size, that is the number of cluster member nodes, is also effected on energy consumption. Therefore we proposed the clustering scheme for high energy efficiency of entire sensor network by controlling cluster size according to the distance between cluster header and sink.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.139-148
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• 2022

Research into wireless sensor networks (WSNs) is a trendy issue with a wide range of applications. With hundreds to thousands of nodes, most wireless sensor networks interact with each other through radio waves. Limited computational power, storage, battery, and transmission bandwidth are some of the obstacles in designing WSNs. Clustering and routing procedures have been proposed to address these concerns. The wireless sensor network's most complex and vital duty is routing. With the Greedy Perimeter Stateless Routing method (GPSR), an efficient and responsive routing protocol is built. In packet forwarding, the nodes' locations are taken into account while making choices. In order to send a message, the GPSR always takes the shortest route between the source and destination nodes. Weighted directed graphs may be constructed utilising four distinct distance metrics, such as Euclidean, city block, cosine, and correlation distances, in this study. NS-2 has been used for a thorough simulation. Additionally, the GPSR's performance with various distance metrics is evaluated and verified. When compared to alternative distance measures, the proposed GPSR with correlation distance performs better in terms of packet delivery ratio, throughput, routing overhead and average stability time of the cluster head.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.142-147
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• 2018

We present a practical design algorithm for quantizers at nodes in distributed systems in which each local measurement is quantized without communication between nodes and transmitted to a fusion node that conducts estimation of the parameter of interest. The benefits of vector quantization (VQ) motivate us to incorporate the VQ strategy into our design and we propose a low-complexity design technique that seeks to assign vector codewords into sets such that each codeword in the sets should be closest to its associated local codeword. In doing so, we introduce new distance metrics to measure the distance between vector codewords and local ones and construct the sets of vector codewords at each node to minimize the average distance, resulting in an efficient and independent encoding of the vector codewords. Through extensive experiments, we show that the proposed algorithm can maintain comparable performance with a substantially reduced design complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1083-1097
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• 2018

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1273-1287
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• 2009

PEGASIS, a chain-based protocol, forms chains from sensor nodes so that each node transmits and receives from a neighbor. In this way, only one node (known as a HEAD) is selected from that chain to transmit to the sink. Although PEGASIS is able to balance the workload among all of the nodes by selecting the HEAD node in turn, a considerable amount of energy may be wasted when nodes which are far away from sink node act as the HEAD. In this study, DERP (Distance-based Energy-efficient Routing Protocol) is proposed to address this problem. DERP is a chain-based protocol that improves the greedy-algorithm in PEGASIS by taking into account the distance from the HEAD to the sink node. The main idea of DERP is to adopt a pre-HEAD (P-HD) to distribute the energy load evenly among sensor nodes. In addition, to scale DERP to a large network, it can be extended to a multi-hop clustering protocol by selecting a "relay node" according to the distance between the P-HD and SINK. Analysis and simulation studies of DERP show that it consumes up to 80% less energy, and has less of a transmission delay compared to PEGASIS.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.135-144
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• 2020

In graph theory, the least-cost path is discovered by searching the shortest path between a start node and destination node. The least cost is calculated as a one-dimensional value that represents the difference in distance or price between two nodes, and the nodes and edges that comprise the lowest sum of costs between the linked nodes is the shortest path. However, it is difficult to determine the shortest path if each node has multiple attributes because the number of cost types that can appear is equal to the number of attributes. In this paper, a shortest path search scheme is proposed that considers multiple attributes using the Euclidean distance to satisfy various user requirements. In simulation, we discovered that the shortest path calculated using one-dimensional values differs from that calculated using the Euclidean distance for two-dimensional attributes. The user's preferences are reflected in multi attributes and it was different from one-dimensional attribute. Consequently, user requirements could be satisfied simultaneously by considering multiple attributes.