An Energy-Efficient and Congestion Control Data-Driven Approach for Cluster-Based Sensor Network

Jan, Syed Rooh Ullah; Jan, Mian Ahmad; Khan, Rahim; Ullah, Hakeem; Alam, Muhammad; Usman, Muhammad

doi:10.1007/s11036-018-1169-x

Cited by 25 publications

(23 citation statements)

References 33 publications

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“…This reduces the amount of data transmitted in the network and in turn leads to a reduction in the overall transmission energy cost. For example, in [87], [88] an in-network data aggregation protocol is proposed that incorporates hierarchical clustering for energy-efficient processing of data. In [87], the cluster head carries out the task of data aggregation and redundancy removal, whereas in [88], such tasks are partially performed by both, individual cluster members and the cluster head.…”

Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

“…For example, in [87], [88] an in-network data aggregation protocol is proposed that incorporates hierarchical clustering for energy-efficient processing of data. In [87], the cluster head carries out the task of data aggregation and redundancy removal, whereas in [88], such tasks are partially performed by both, individual cluster members and the cluster head. Data aggregation schemes can also be classified based on the network topology, timing, and type of the aggregation function, as shown in Fig.…”

Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

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QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…The data fusion method is considered an essential process for preserving sensor energy, in [7], the author proposed an energy efficient method for clustering the nodes in wireless sensor network. Jan et al [8] presented a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system, and the proposed scheme efficiently reduced the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. To reduce the intensity of correlation for WSNs, in [9], the author proposed in-node data aggregation technique that eliminates redundancy in the sensed data in an energy-efficient manner, meanwhile adopted a novel datadriven approach to perform in-node data aggregation using an underlying cluster-based hierarchical network.…”

Section: A Cluster-based Hierarchical Data Fusion Algorithmmentioning

confidence: 99%

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

et al. 2020

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In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime. INDEX TERMS Mobile heterogeneous wireless sensor networks, data fusion, extreme learning machine, bat algorithm, energy efficient, reliability.

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“…A tightly coupled concept associated with data fusion, in heterogeneous and homogeneous WSNs, is known as data aggregation. This concept is becoming popular in research community of WSNs to refine and summarize the gathered data, usually collected from multiple sensors . Data aggregation is defined as the process of collecting or manipulating raw data from multiple sources, ie, sensors, and summarizes it to produce refined data that has minimum redundancy and volume.…”

Section: Introductionmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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An Energy-Efficient and Congestion Control Data-Driven Approach for Cluster-Based Sensor Network

Cited by 25 publications

References 33 publications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

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