• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.43-50
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• 2009

Flooding and SPIN, which are well-known WSN(Wireless Sensor Network) proactive protocols, spontaneously disseminate the sensed data without a request from an arbitrary sink node. However, these methods disseminate the data even to some nodes that do not need it, which is energy inefficient. In this paper, we introduce a semi-proactive protocol to disseminate only to pertinent nodes instead of all nodes in order to overcome this weakness. Thus some nodes, such as arbitrary sink nodes that need the sensed data, could easily obtain the data within some hops. The simulation result shows that the proposed protocol has higher average node energy efficiency than that of well-known earlier work, SPIN. If a proactive protocol, such as SPIN, is changed to semi-proactive and has only a 1-hop burden, then the energy efficiency enhancement is up to about 83% compared with SPIN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.6-11
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• 2021

Routing protocols play a pivotal role in the energy management and lifespan of any Wireless Sensor Network. Lower network lifetime has been one of the biggest concerns in LEACH protocol due to dead nodes. The LEACH protocol suffers from uneven energy distribution problem due to random selection of a cluster head. The cluster head has much greater responsibility compared to other non- cluster head nodes and consumes greater energy for its roles. This results in early dead nodes due to energy lost for the role of cluster- head. This study proposes an approach to balance the energy consumption of the LEACH protocol by using a semi-deterministic opportunity coefficient to select the cluster head. This is calculated in each node with the battery energy level and node ID. Ultimately, based on the opportunity cost, cluster head will be selected and broadcasted for which other nodes with higher opportunity cost will agree. It minimizes the chances of nodes with lower battery level being elected as cluster head. Our simulation experiments demonstrate that cluster heads chosen using our proposed algorithm perform better than those using the legacy LEACH protocol.

• International Journal of Contents
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• v.8 no.2
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• pp.13-18
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• 2012

In wireless sensor networks, an energy efficient data gathering scheme is one of core technologies to process a query. The cluster-based data gathering methods minimize the energy consumption of sensor nodes by maximizing the efficiency of data aggregation. However, since the existing clustering methods consider only uniform network environments, they are not suitable for the real world applications that sensor nodes can be distributed unevenly. To solve such a problem, we propose a balanced multi-hop clustering scheme in non-uniform wireless sensor networks. The proposed scheme constructs a cluster based on the logical distance to the cluster head using a min-distance hop count. To show the superiority of our proposed scheme, we compare it with the existing clustering schemes in sensor networks. Our experimental results show that our proposed scheme prolongs about 48% lifetime over the existing methods on average.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.149-156
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• 2011

This paper reveals a distance estimation algorithm based on multi-code ultrasonic and wireless sensor network. For measuring the distances among the sensor nodes, each ultrasonic transmitter transmits multi-code ultrasonic signal simultaneously. Receivers use cross correlation method to separate the coded signals. The information of measured distances is broadcasted to each sensor node by wireless sensor network. The wireless sensor network measures the distance among the sensor nodes using the received signal strength of the broadcasting. The multi-code ultrasonic have a limitation of measurable distance. And the received signal strength is affected from an environment. This paper measures a distance using ultrasonic and a received signal strength in short range. These measured data are applied to the least square estimation algorithm. By the expansion of the fitting curve, a distance measurement in long range using the received signal strength is compensated. The coupled system reduce the error to an acceptable level.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.49-56
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• 2007

In the sensor networks, the first step in the signal processing is noise reduction. It is obvious that the noise in the sensing data by sensor nodes have a bad effect on the collaborative signal processing results. In this paper we propose noise reduction filters for the wireless sensor networks. The proposed filters use only relative distance information between sensor nodes thus it is very simple in complexity and easy to implement. Also it will be able to extend the sensor network life time by adjusting communication radius of sensors within proper limit to reduce consuming power. Various simulation results are presented to verify our approaches, we observed that the proposed filters can reduce the effect of the noise on the sensing data.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.996-1004
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• 2013

In this paper, we propose two clustering schemes to prolong lifetime by improving unbalance of energy consumption among sensor nodes in wireless sensor networks. The first proposed scheme make up clusters according to density of sensor nodes in initial stage of wireless sensor networks for reducing energy consumption of wireless sensor networks. After the initial stage, a cluster header is selected by a relay scheme that determines a cluster header of next round among cluster members. by estimating of energy consumption of cluster members for improving unbalance of energy consumption among cluster members.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1546-1564
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• 2012

Pattern-based query processing has not attracted much attention in wireless sensor network though its counterpart has been studied extensively in data stream. The methods used for data stream usually consume large memory and much energy. This conflicts with the fact that wireless sensor networks are heavily constrained by their hardware resources. In this paper, we use piece wise representation to represent sensor nodes' collected data to save sensor nodes' memory and to reduce the energy consumption for query. After getting data stream's and patterns' approximated line segments, we record each line's slope. We do similar matching on slope sequences. We compute the dynamic time warping distance between slope sequences. If the distance is less than user defined threshold, we say that the subsequence is similar to the pattern. We do experiments on STM32W108 processor to evaluate our strategy's performance compared with naive method. The results show that our strategy's matching precision is less than that of naive method, but our method's energy consumption is much better than that of naive approach. The strategy proposed in this paper can be used in wireless sensor network to process pattern-based queries.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3B
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• pp.123-134
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• 2008

In this paper, a routing algorithm for wireless sensor networks is proposed to improve the efficiency of energy consumption in sensor nodes. Each sensor node has the value called ‘Flooding Level’ obtained through the initial flooding from a sink node instead of sending beacon messages in multi-hop sensor field. This value can be used for guaranteeing the sensor nodes to connect with a sink node and determining the roles of cluster-head and cluster-gateway node efficiently and simply during the clustering. If different algorithms are added to our protocol, it will work better in the side of energyefficiency. This algorithm is evaluated through analysis and extensive simulations.