Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks

Banerjee, Rajib; Chatterjee, Sibashis; Bit, Sipra Das

doi:10.1007/s11277-018-6008-7

Cited by 12 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We showed the advantages and success of our proposed algorithm with experimental study. Since we used statistical approaches in distributed environment simulation, our study has been more successful than other studies [6,7]. Moreover, unlike previous studies [4,8], data confidentiality and data consistency were realized simultaneously.…”

Section: Related Workmentioning

confidence: 88%

Distributed Environment Modeling using Path Compression Algorithm

Bakir

Hakkoymaz

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

In distributed environment, some operations related to objects are performed. For example, objects can be accessed or they can be moved. In our study, events related to objects (object-access, object-move) were created as independent events. In this study, the distributed environment simulation was performed and the effectiveness and success of the path compression algorithm, which we proposed as a result of the experimental study, was demonstrated. The purpose of this study is to show the effectiveness and benefits of the path compression algorithm. Path compression algorithm is an efficient algorithm whose runtime is linear. With the path compression, the long node chain that is formed while data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast and the cost of access is reduced. This result is shown with experimental study by modeling the distributed environment. It is shown comparative the results of the distributed environment simulation according to the various Access/Move (%) rates using binomial distribution. When we use the path compression, the maximum length and mean length of the chain decreases. Thus, with the path compression algorithm, the long node chain created by the objects is broken, the cost of accessing the objects is reduced, and fast access to the objects is ensured. In short, with our study, fast access to data is ensured in a distributed environment.

show abstract

Section: Related Workmentioning

confidence: 88%

Distributed Environment Modeling using Path Compression Algorithm

Bakir

Hakkoymaz

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

show abstract

“…Processing and transmitting massive superfluous data can lead to additional power consumption and greatly decrease network lifetime [15,16]. To improve data processing performance, a path merging protocol, which supports partial discrete wavelet transform-based compression schemes to reduce redundant data transmission in a significant manner through the appropriate aggregation of data packets from merging paths, was proposed in [17]. To manage energy-efficient data collections in WSNs, a dataaware energy conservation scheme and prediction-based data collection framework were proposed to reduce data transmission [18], where the inherent correlation between the consecutive observations of SNs and the data similarity measures between the neighboring SNs are utilized.…”

Section: Related Workmentioning

confidence: 99%

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Zhu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual-metric distance is employed, and redundant nodes are preliminarily identified by the improved k -means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

show abstract

“…Closer to the base station will be considered as cluster head and farer to the base station will not be considered as cluster head. The probability of the nodes becoming Cluster Head is accumulated by a weighted value which is determined by distance between the nodes and the base station [12][13][14] (Fig. 4).…”

Section: Distance-based Cluster Head Election Algorithmmentioning

confidence: 99%

A GUI to Analyze the Energy Consumption in Case of Static and Dynamic Nodes in WSN

Tagare

Narendra

Manjunath

2021

Mobile Computing and Sustainable Informatics

View full text Add to dashboard Cite

Wireless sensor networks consist of spatially dispersed sensors for monitoring physical parameters in various applications and in organizing the collected data at centralized location. These days, energy is one of the main factors that need to be intelligently consumed in wireless sensor networks. Techniques which can perform energy efficient clustering are required to maintain reliable sensing. In this paper, we consider both static and dynamic nodes. For static nodes, direct communication and low energy adaptive clustering hierarchy algorithm calculations are examined and discussed. For dynamic nodes, the distance-based algorithm is presented which ensures that work is allocated evenly to the nodes. Further, the distance and energybased algorithm ensures that the nodes drain out evenly. Simulations are carried out on MATLAB. An easy-to-understand graphical user interface allows us to handily analyze the energy proficiency for various amount of nodes and clusters. The results are shown for four nodes and one cluster.

show abstract

Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks

Cited by 12 publications

References 15 publications

Distributed Environment Modeling using Path Compression Algorithm

Distributed Environment Modeling using Path Compression Algorithm

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

A GUI to Analyze the Energy Consumption in Case of Static and Dynamic Nodes in WSN

Contact Info

Product

Resources

About