A lightweight Distributed Database Management in WSN

Ismail, Shereen; Alkhader, Eman; Gasimova, Aydan; Reza, Hassan

doi:10.1109/isncc52172.2021.9615813

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each type of WSN is discussed in detail with the help of its routing protocols, related applications, and issues. In 2021, Ismail et al 37 presented a lightweight distributed database management in WSN. This approach is based on the concept of a hybrid robot.…”

Section: Ta B L Ementioning

confidence: 99%

“…In the cluster-based architecture, data is stored in three locations: the SN, the CH, and finally the BS. 16 Preceding survey studies and literature review focused on specific topics for instance a review of data aggregation in WSNs, [17][18][19][20][21][22] data compression methods, 21,23 hierarchical routing protocols, [24][25][26] a survey of clustering in heterogeneous and homogeneous WSN, 26 energy-efficient routing protocol, [27][28][29][30] energy efficiency in WSNs, [31][32][33][34] network lifetime, 35,36 data management in WSN, 6,[37][38][39] machine learning [40][41][42][43] and deep learning [38] in WSN, computational intelligence techniques 44 and network monitoring in WSN, 45 big data challenges in WSN, 22,46,47 security issues in WSN, [48][49][50] multi-objective QoS optimization in WSN, 51 localization and coverage in WSN 52,53 (presented in detail in Section 2). This review article, on the other hand, intends to categorize the current data transmission reduction techniques...…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: Ta B L Ementioning

confidence: 99%