Energy efficient intra-cluster data aggregation technique for wireless sensor network

Bongale, Anupkumar M; Nirmala, C. R.; Bongale, Arunkumar

doi:10.1007/s41870-020-00419-7

Cited by 35 publications

(14 citation statements)

References 16 publications

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“…Table 7 summaries the comparisons of the recent work 79,[83][84][85][86][87][88][89][90][91][92][93][94] on data transmission reduction techniques at the SN stage as well as CH stage to avoid redundancy along with methods and techniques 9,75,84,[95][96][97][98][99][100][101][102][103][104][105][106][107][108] and Naive method also (the traditional technique, in which all sensor measurements are transmitted to the BS unprocessed).…”

Section: Ta B L Ementioning

confidence: 99%

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: Ta B L Ementioning

confidence: 99%

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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“…To accomplish intra cluster routing, the approach selects a collection of nodes utilizing triangle theory as anchor. Bongale et al 20 proposed a method for intra‐cluster data aggregation of data inside clusters. In this method, each member node sends its sensed data either to the CH or to a relay node based on distance.…”

Section: Related Workmentioning

confidence: 99%

HSCR: Hierarchical structured cluster routing protocol for load balanced wireless sensor networks

Pal

2022

Softw Pract Exp

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The robustness of wireless sensor networks (WSNs) has made it a preferred choice for many applications. WSNs should perform the intended sensing task with a limited amount of energy. Clustering methodology is used to reduce sensor nodes' energy consumption and increase the network lifetime by organizing nodes into independent groups. In this article, a hierarchical structured cluster routing (HSCR) protocol based on the principle of an m‐way balanced tree has been proposed for multi‐hop intra‐cluster communication and offload the cluster head. It enhances the network performance by providing an underlying architecture that reduces the intra‐cluster communication distance and the amount of data sent to the cluster head. In the proposed HSCR protocol, an m‐way tree‐based hierarchical structure has been proposed in which balanced structure is formed within each cluster so that data aggregation is performed at each intermediate parent node. The proposed approach can be implemented over any communication protocol used in WSNs. Simulation results prove that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, and network lifetime. Simulation results affirm that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, average intra‐cluster communication distance and network lifetime by 105.32%, 49.23%, 14.98%, 109.31%, and 35.75%, 15.32%, 17%, 24.31%, respectively.

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“…Data are transmitted from every cluster to BS that is sequenced through aggregation. 32 The data aggregation method is employed using CH; in this, the data are aggregated from same clusters and forward to the sink node • This work proposes Auto-Metric Graph Neural Network (AGNN) 33 for CH selection protocol by incorporating criterion of mentioned factors, such as energy and delay for its developed fitness function. Since AGNN is a heuristic approach, it lessens the cognitive strain of CH decision-making by creating its fitness function based on precisely selected factors.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these factors, cluster is designed. Data are transmitted from every cluster to BS that is sequenced through aggregation 32 . The data aggregation method is employed using CH; in this, the data are aggregated from same clusters and forward to the sink node This work proposes Auto‐Metric Graph Neural Network (AGNN) 33 for CH selection protocol by incorporating criterion of mentioned factors, such as energy and delay for its developed fitness function.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan,

Srinivasan

2023

Int J Communication

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SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.

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Energy efficient intra-cluster data aggregation technique for wireless sensor network

Cited by 35 publications

References 16 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

HSCR: Hierarchical structured cluster routing protocol for load balanced wireless sensor networks

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

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