Data Prediction Model in Wireless Sensor Networks: A Machine Learning Approach

Jain, Khushboo; Gupta, Manali; Abraham, Ajith

doi:10.1007/978-3-030-96299-9_13

Cited by 2 publications

(14 citation statements)

References 16 publications

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“…In temporal correlation, the event tracking may require SNs to periodically perform transmit similar sensed attributes. In spatial correlation, if the two SN crosses a predefined range and they came in close proximity with each other it leads to sensing of duplicate data, resulting in high network traffic as proposed by References 58,59,62–67,89–90.…”

Section: Future Research Directionsmentioning

confidence: 99%

“…When two or more SNs sense and transmit similar and correlated data simultaneously, the data collision and data loss occur. Due to packet collisions, it becomes difficult to locate the SNs in case of a fault which is a challenge in References 12,60,67,87,116 for further directions.…”

Section: Future Research Directionsmentioning

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%

“…Table 4 summarizes the comparisons of the recent work [57][58][59][60][61][62][63][64][65][66][67] on data transmission reduction techniques at the SN stage presented in Table 2 along with the comparing method. Table 4 also depicts the various proficiency parameters which the researcher analysis and presents in their work.…”

Section: Data Transmission Reduction Techniques At the Sn Stagementioning

confidence: 99%

“…Simulation parametric values at SN/CH stage and BS stage Comparison of methods and proficiency metrics for data transmission reduction techniques at SN stage/CH stage and BS may require SNs to periodically perform transmit similar sensed attributes. In spatial correlation, if the two SN crosses a predefined range and they came in close proximity with each other it leads to sensing of duplicate data, resulting in high network traffic as proposed byReferences 58,59,[62][63][64][65][66][67][89][90] …”

mentioning

confidence: 99%

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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: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%