A Distributed Round-Based Prediction Model for Hierarchical Large-Scale Sensor Networks

Saad, Ghina; Harb, Hassan; Jaoude, Chady Abou; Jaber, Ali

doi:10.1109/wimob.2019.8923312

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Saad et al 114 in 2019 proposed a periodic LSTM method as the SNs have limited resources but due to continuous sensing, the huge amount of gathered data is redundant or highly correlated which is a major issue in scaling WSN applications. Periodic LSTM is a distributed prediction model which is proposed for cluster-based WSN and it executes in rounds.…”

Section: Data Transmission Reduction Techniques At the Bs Stagementioning

confidence: 99%

“…As a result, achieving a longer network lifetime is still considered the most difficult issue in the research. 12,13,[57][58][59][60][61][62][63][64][65]67,79,[83][84][85][86][87][88][89][90][91][92][93][94][109][110][111][112][114][115][116][117]121…”

Section: Energy Efficiency Under Adverse Environmental Conditionsmentioning

confidence: 99%

“…As a result, concerning future directions, there is still a need to emphasize data prediction models to reduce the cost of redundant data transmission and data delay. 12,13,59,62,63,67,76,87,90,93,[109][110][111][112][113][114][115][116][117]121,136…”

Section: Data Prediction Modelmentioning

confidence: 99%

“…Second, neighboring SNs sense the same and spatially correlated data due to random deployment and close proximity of SNs. In References 39,60,62,84,87,89,92,113,114,117, data redundancy is focused, exclusively for redundant data transmissions, energy depletion, packet loss, data accuracy, WSN lifespan. As a result, there is a need to develop effective energy-efficient data prediction methods and improve the overall network's lifespan.…”

Section: Data Redundancymentioning

confidence: 99%

See 3 more Smart Citations

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

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Section: Data Transmission Reduction Techniques At the Bs Stagementioning

confidence: 99%

Section: Energy Efficiency Under Adverse Environmental Conditionsmentioning

confidence: 99%

Section: Data Prediction Modelmentioning

confidence: 99%

Section: Data Redundancymentioning

confidence: 99%

See 2 more Smart Citations

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The sink executes long short-term memory (LSTM) along with time series model to find the predicted data during sleep mode. This will help in amount of transmission done by a Sensing Point and hence improves lifetime ratio [7]. There are multiple Sensing points which are distributed under various Systems in wireless sensor network.…”

Section: Literaure Surveymentioning

confidence: 99%

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

L¹,

M²

2019

IJEAT

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WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

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Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks

Al-Qurabat

Abdulzahra²,

Idrees

2022

J Supercomput

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A Distributed Round-Based Prediction Model for Hierarchical Large-Scale Sensor Networks

Cited by 4 publications

References 15 publications

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks

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