Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks

Puranikmath, Veena I; Harakannanavar, Sunil S.; Kumar, Sumit; Torse, Dattaprasad A.

doi:10.5815/ijcnis.2019.03.05

Cited by 10 publications

(6 citation statements)

References 14 publications

(19 reference statements)

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“…For sparse big data, use attribute extraction or dimensionality reduction techniques to remove redundant data. 13,14 Then, use the visualization method to transform, transform the unusable data set into a data structure through a series of differentiation solutions, and use it to present it to people through a series of operations. People can interact with the interface through a series of operations.…”

Section: Information Visualizationmentioning

confidence: 99%

“…For data sets with abnormal structures, formats and methods such as conversion or conversion of data types are used. For sparse big data, use attribute extraction or dimensionality reduction techniques to remove redundant data 13 , 14 . Then, use the visualization method to transform, transform the unusable data set into a data structure through a series of differentiation solutions, and use it to present it to people through a series of operations.…”

Section: Theoretical Analysis Of the Image Design Of Smart Citiesmentioning

confidence: 99%

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(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

Luo

2022

J. Electron. Imag.

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Due to the advancement of software and hardware technologies such as geographic information systems, for example, optical fiber storage, mobile network, digital camera, and high-definition network, it has promoted the establishment of a data digital management system. Network sharing, to a certain extent, makes it the smart city video sensor of today's era. It is a key component of the city's smart management system. In the context of the rapid development of the information age, I explore the role of video sensors in the visual design of smart cities. I propose three methods to analyze the theory of big data video sensors: theoretical analysis of big data video sensors, information visualization, and visual signal output, and then model the video signal output to visualize the complex multilayer network decision. Starting from the RBF neural network, I simulate a video perception prediction model system, import the data into Matlab for experimental analysis and simulation, and compare RBF and BP are based on both data predictions. RBF obviously has stronger predictive video perception. Experimental data show that the error between video perception and actual data is within 3%. It can be seen that video sensors play a key role in the construction of smart cities.

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Section: Information Visualizationmentioning

confidence: 99%

Section: Theoretical Analysis Of the Image Design Of Smart Citiesmentioning

confidence: 99%

(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

Luo

2022

J. Electron. Imag.

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“…In the cluster-based approach, distance is considered to group the devices into several clusters. (6) Considering certain criteria in every cluster, a head of the cluster is chosen (7)(8)(9)(10) . The main task of this head of the cluster is to receive aggregate and transmit the data.…”

Section: Introductionmentioning

confidence: 99%

Relationship-Cluster Head Selection and Data Compression Enabled Cluster-Based Aggregation Model for Social Internet of Things

Meghana,

Hanumanthappa,

Prakash

et al. 2023

IJST

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Background:The Social Internet of Things (SIoT) combines IoT with social networking, enabling objects to interact based on social relationships and facilitating user-device interactions. However, SIoT networks generate massive data that must be transmitted and processed efficiently. We propose a clusterbased aggregation model for SIoT to address this, integrating relationshipcluster head selection with K-Means and data compression using Huffman coding. Objectives: The proposed model utilizes K-Means to select cluster heads based on object relationships. Objects are clustered using K-Means, and a Decision Tree considers object profiling and relationships to choose the cluster head. Selected cluster heads employ Huffman coding to compress data before transmission to the sink node. Methods: The data are aggregated at the cluster head and it is compressed before sending it to the destination device. Model is evaluated through simulation-based experiments using the SIoT-CCN simulator. Findings demonstrate that our proposed model outperforms existing approaches in terms of energy consumption, network lifetime, and data aggregation accuracy. Findings: Evaluation metrics include Silhouette Score, Number Distance between Train and Test K-Means, BIC Score, and Gradient of BIC Scores. Results for our proposed model include a Silhouette score of 0.459 for cluster numbers 2, 3, and 4, -1148.951 distance between train and test K-Means, -5227.080 BIC Score, and -96.445 Gradient of BIC score. For the Relation-Based clustering approach without data compression, the Silhouette score is 0.6266, -203.345 distance between Train and test K-Means, -7266.080 BIC Score, and -61.415 Gradient of BIC Scores. The Data Compression-enabled cluster-based aggregation model without relationshipbased clustering achieves a Silhouette score of 0.58, -467.890 distance between train and test K-Means, -8981.786 BIC Scores, and -94.244 Gradient of BIC Score. Novelty: Integrating relationship-cluster head selection with K-Means and data compression with Huffman coding to develop a cluster-based https://www.indjst.org/ 3605

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“…As a result, optimizing the use of existing resources is critical for increasing the efficiency of WSNs. In a realistic environment sensor with multi-tasking, high node density, limited battery capacity, and resource constraints, efficient resource utilization is a challenging task that requires resource identification/discovery, resource allocation, and resource scheduling [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

Kori¹,

Kakkasageri²

2022

IJISA

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In modern world of sensing and distributive systems, traditional Wireless Sensor Networks (WSN) has to deal with new challenges, such as multiple application requirements, dynamic and heterogeneous networks. Senor nodes in WSN are resource constrained in terms of energy, communication range, bandwidth, processing delay and memory. Numerous solutions are proposed to optimize the performance and to increase the lifetime of WSN by introducing new resource management principles. Effective and intelligent resource management in WSN involves in resource identification, resource scheduling, and resource utilization. This paper proposes a Bayesian Game Model (BGM) approach to efficiently identify the best node with the maximum resource in WSN for data transmission, considering energy, bandwidth, and computational delay. The scheme operates as follows: (1) Sensor nodes information such as residual energy, available bandwidth, and node ID, etc., is gathered (2) Energy and bandwidth of each node are used to generate the payoff matrix (3) Implementation of node identification scheme is based on payoff matrix, utilities assigned, strategies and reputation of each node (4) Find Bayesian Nash Equilibrium condition using Starring algorithm (5) Solving the Bayesian Nash Equilibrium using Law of Total Probability and identifying the best node with maximum resources (6) Adding/Subtracting reward (reputation factor) to winner/looser node. Simulation results show that the performance of the proposed Bayesian game model approach for resource identification in WSN is better as compared with the Efficient Neighbour Discovery Scheme for Mobile WSN (ENDWSN). The results indicate that the proposed scheme has up to 12% more resource identification accuracy rate, 10% increase in the average number of efficient resources discovered and 8% less computational delay as compared to ENDWSN.

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Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks

Cited by 10 publications

References 14 publications

(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

Relationship-Cluster Head Selection and Data Compression Enabled Cluster-Based Aggregation Model for Social Internet of Things

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

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