Data Redundancy Reduction for Energy-Efficiency in Wireless Sensor Networks: A Comprehensive Review

Sahar, Gul; Bakar, Kamalrulnizam Abu; Zuhra, Fatima Tul; Rahim, Sabit; Bibi, Tehmina; Madni, Syed Hamid Hussain

doi:10.1109/access.2021.3128353

Cited by 17 publications

(7 citation statements)

References 95 publications

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“…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: Future Research Directionsmentioning

confidence: 99%

“…In this work, they worked on the importance of sensors for precision agriculture. In 2021, Sahar et al 39 presented a comprehensive review on energy‐efficient data redundancy reduction for WSNs. This review briefly discussed various schemes of data redundancy reduction in WSN and their limitations.…”

Section: Literature Review Of the Current Status Of The Survey In Wsnmentioning

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 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

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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: Literature Review Of the Current Status Of The Survey In Wsnmentioning

confidence: 99%

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

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“…A similarity measure in the context of data mining is a distance whose dimensions indicate object properties. Thus, if the distance between two data points is small, the objects will be highly similar, and vice versa [25]. The majority of aggregation techniques use distance measurements to evaluate the differences between a pair of items [11].…”

Section: Fda-smorp Proposedmentioning

confidence: 99%

Fuzzy Data Aggregation Approach to Enhance Energy-Efficient Routing Protocol for HWSNs

Alshawi¹,

Alhijaj²,

Khudor³

2022

IJCAI

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The sensor nodes' computing capability, communication capabilities, and power supply are severely constrained in WSNs, making sensor battery replacement or recharging difficult or even impossible. Therefore, energy is an important challenge to consider while creating WSNs. In hazardous circumstances, accurate data aggregation and routing are crucial, and the energy consumption of sensors must be closely controlled. Due to environmental conditions and short-distance sensors, however, there is a high possibility of duplicating data. Large datasets include a range of data, some of which are helpful while others are entirely unnecessary. This redundancy reduces performance in terms of redundant transmission and computation expense. Data aggregation, on the other hand, may reduce duplicate data in a network, hence reducing the volume of data sent and increasing the network's lifespan. In this context, two novel energy-conscious approaches called Fuzzy Data Aggregation with Spider monkey optimization (FDA-SMORP) for data aggregation in the cluster head and routing to the sink are presented. These strategies attempt to offset the energy consumption among all nodes in a wireless network such that these nodes exhaust all of their energy and die almost simultaneously. To demonstrate the efficacy of the suggested approaches in terms of minimizing delay caused by route planning, balancing energy usage, and extending network lifetime, the proposed methods are compared to some of the most well-known WSN systems. Povzetek: Razvit je sistem za nadzorovanje potrošnje energije v senzorskih brezžičnih omrežjih.

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“…Data mining may aid in automated or human-driven tactical/strategic decision-making if raw data is reviewed and transformed into relevant information. Therefore, it is essential to develop algorithms for mining sensor data for patterns so that rapid, intelligent decisions may be made [2].…”

Section: Introductionmentioning

confidence: 99%

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Alsawad

2022

IJICI

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The mining and processing of data for wireless sensor networks (WSNs) have been a significant research area in a number of computer science disciplines, including distributed applications, database management systems, and information gathering. The main goal of implementing applications based on WSNs is to make real-time choices, which have proven very difficult due to the limitations of computing, communication, and data mining capacity for WSNs. Thus, due to the nature and unique characteristics of sensor data and the limitations of WSNs, traditional data mining methods cannot be easily applied to them. This paper presents a new approach, the Kalman Filter (KF) with K-Nearest Neighbors (KNN) named (KF-KNN) which is proposed for data classification and collection as well as noise elimination in WSNs, increasing the network efficiency and lifetime. The proposed technique is compared to KNN to illustrate the effectiveness of the recommended ways in increasing energy consumption and prolonging network lifetime.

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Data Redundancy Reduction for Energy-Efficiency in Wireless Sensor Networks: A Comprehensive Review

Cited by 17 publications

References 95 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

Fuzzy Data Aggregation Approach to Enhance Energy-Efficient Routing Protocol for HWSNs

Increase Efficiency And Lifetime In Wsns Through Data Reduction

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