A Mechanism Filling Sensing Holes for Detecting the Boundary of Continuous Objects in Hybrid Sparse Wireless Sensor Networks

Xiang, Jianming; Zhou, Zhangbing; Shu, Lei; Rahman, Taj; Wang, Qun

doi:10.1109/access.2017.2654478

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…While working on the prediction of students' performance, [11][12][13] applied data mining techniques to develop a system for students' final marks prediction based on the performance of their students. The principal component analysis-based regression model was trained to predict the students' academic performance.…”

Section: Related Workmentioning

confidence: 99%

“…The gradual increase in education data is due to the continuous generation of such data from different sources, such as e-learning, learning management systems, admission Sustainability 2021, 13, 9775 2 of 21 systems, and student feedback analysis systems. The student data acquired from the aforementioned sources are used for making a simple query-level decision, whereas a huge bulk of data remains unused due to the complex and noisy nature of datasets.…”

Section: Introductionmentioning

confidence: 99%

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Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

et al. 2021

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Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

et al. 2021

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“…Optimum location selection mechanism detecting the variation points for boundary detection and sensor holes also fill up by the mobile sensors. Sensing holes selected on the basis of spatial and variation factors and maintain target list for mobile nodes [45].…”

Section: B Continuous Object Tracking and Boundary Detection Schemesmentioning

confidence: 99%

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

et al. 2021

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With the new age of data innovation, the Internet of Things (IoT) proliferation has drawn enormous thought and has applied to help applications in different fields i.e. natural assurance, military observing, and industrial applications. WSNs are the essential segment of IoT for monitoring as well as tracking. The most preeminent applications provide confinement and identification of continuous objects i.e. wildfire, toxic gas, bio synthetics concoctions, and so forth. In the case of continuous objects such as fire and toxic gases are detected to identify the boundary of damage and alert teams for rescue efforts. It is also helpful in identifying safe paths for rescue. We have investigated various existing surveys that carried out different concepts associated with continuous object tracking and find out the deficit of boundary detection of object. In order to replete the present cleft of analysis, we have inspected various current state-of-the-art works on boundary detection of a continuous object that has yet not been added to the current writing. This paper presents an extensive overview of different continuous object tracking schemes which involve energy efficiency, boundary detection, communication, data aggregation, and network structural design in literature with the aid of featuring taxonomy. We summarized, compared, and classified these schemes along with their analysis and performance. Moreover, for further evaluation mechanism, strengths and weaknesses of these schemes are presented. Finally, various state-of-the-art open research challenges are identified. Moreover, there is a need to overcome these challenges through novel and reliable arrangements by the researchers.

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“…In the protocols, static sensors detect coverage holes locally by using Voronoi diagrams [39]. In [40], J. Xiang propose a sensors locations mechanism used hybrid network by detecting sensing holes using Voronoi diagram. On the basis of better layout effect, it can reduce the energy consumption by the static networks.…”

Section: Related Workmentioning

confidence: 99%

Sensor Deployment Method Based on Faiw-DPSO in DASNs

et al. 2020

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Distributed Active Sensing Networks (DASNs) is a new sensing paradigm, where active and passive sensors are distributed in a field, and collaboratively detect the objects. The detectability is the most important property of DASNs. ''Exposure'' is defined to quantify the dimension limitations in detectability. Thus, it is important to deploy the sensors with the minimum exposure to improve the detectability. To minimize exposure is NP-hard, thus it is necessary to solve it by heuristic algorithms. In this paper, we present a Discrete Particle Swarm Optimization (DPSO)-based solution to achieve the minimum exposure. Furthermore, a feedback-based adjustment on the inertia weight of DPSO is designed to improve the convergence speed and global searching ability of algorithm. By a large number of simulations, this improved DPSO (Faiw-DPSO) is proved to outperform greedy algorithm by up to 74% and perform better than other related algorithms. This algorithm is robust, efficient and self-adaptive in regular and irregular monitoring field. INDEX TERMS Distributed active sensing networks (DASNs), discrete particle swarm optimization (DPSO), exposure, feedback-based adjustment, inertia weight.

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A Mechanism Filling Sensing Holes for Detecting the Boundary of Continuous Objects in Hybrid Sparse Wireless Sensor Networks

Cited by 12 publications

References 38 publications

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

A Survey on Continuous Object Tracking and Boundary Detection Schemes in IoT Assisted Wireless Sensor Networks

Sensor Deployment Method Based on Faiw-DPSO in DASNs

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