Energy‐Efficient Data Gathering Scheme Based on Broadcast Transmissions in Wireless Sensor Networks

Lee, Soo Bin; Lee, Howon

doi:10.1155/2013/402930

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…However, it does not have efficient measurement in the sensing department. A distributed data compression framework was established in [9]. But the classification was not implementing.…”

Section: Fig1: Wsnmentioning

confidence: 99%

Ensembled Adaboost Learning with Id3 Algorithm for Energy Aware Data Gathering in WSN

Kalaimani*,

Kavitha

2019

IJITEE

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In this paper, data collection is the operation of gathering a lot of details from the sensor nodes and shipping it to the sink node. The use of Network is increasingly required to perform these processes, so, increases energy consumption. A lot of WSN architectures are designed to solve this complex problem. By using a technique called Decision Tree Classifier using Adaboost (DTCA) algorithm, can extension that data collection efficiency, as well as reducing the delay and Power consumption. In the proposed methodology, the power of each sensor node should be estimated at the outset. Then the mobile sink node receives the information from the high power sensor nodes with minimal delay. The mobile sink node classifies the data pockets using the Decision Tree Classifier. This classifies based on the relationship between the sensor nodes in WSN. That relationship is measure using the method of population Pearson product moment correlation coefficient. Adaboost algorithm is a combination of several weak non-linear classifiers to create a higher classification. Then finally, it sends classified particulars to Base Station. The operation of the DTCA system is convey out with divergent parameters such as classification time, EC, (Network Lifetime) NL, data collection capability, Classification Accuracy (CA), (FPR) false positive rate and delay.

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“…However, it does not have efficient measurement in the sensing department. A distributed data compression framework was established in [9]. But the classification was not implementing.…”

Section: Fig1: Wsnmentioning

confidence: 99%

Ensembled Adaboost Learning with Id3 Algorithm for Energy Aware Data Gathering in WSN

Kalaimani*,

Kavitha

2019

IJITEE

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“…As low accuracy of the triaxial acceleration sensor results in serious drift of SINS during run time, we proposed indoor positioning method of WSN as a kind of compensation scheme [ 18 , 19 ]. Node arrangement diagram of WSN on the fully mechanized coal face is shown in Figure 4 , and positioning mathematical model is built, as shown in Figure 5 .…”

Section: Shearer Positioning Strategy Based On Sins/wsnmentioning

confidence: 99%

Integrated Positioning for Coal Mining Machinery in Enclosed Underground Mine Based on SINS/WSN

Fan

Jing

et al. 2014

The Scientific World Journal

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To realize dynamic positioning of the shearer, a new method based on SINS/WSN is studied in this paper. Firstly, the shearer movement model is built and running regularity of the shearer in coal mining face has been mastered. Secondly, as external calibration of SINS using GPS is infeasible in enclosed underground mine, WSN positioning strategy is proposed to eliminate accumulative error produced by SINS; then the corresponding coupling model is established. Finally, positioning performance is analyzed by simulation and experiment. Results show that attitude angle and position of the shearer can be real-timely tracked by integrated positioning strategy based on SINS/WSN, and positioning precision meet the demand of actual working condition.

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“…The Energy-Efficient Data Gathering approach was introduced in [3]. The approach measures the relationship among sensing information of the SNs, it failed to use efficient classification method to classify collected data.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

J.Srimathi¹,

Mayil²

2019

IJCNC

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The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL, data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP. The results observed that FGEORBC technique improves the data gathering and NL with minimum time as well as EC than the state-of-the-art methods.

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Energy‐Efficient Data Gathering Scheme Based on Broadcast Transmissions in Wireless Sensor Networks

Cited by 5 publications

References 19 publications

Ensembled Adaboost Learning with Id3 Algorithm for Energy Aware Data Gathering in WSN

Ensembled Adaboost Learning with Id3 Algorithm for Energy Aware Data Gathering in WSN

Integrated Positioning for Coal Mining Machinery in Enclosed Underground Mine Based on SINS/WSN

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

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