PECDF-CMRP: A Power-Efficient Compressive Data Fusion and Cluster-Based Multi-hop Relay-Assisted Routing Protocol for IoT Sensor Networks

Agbulu, G. Pius; Kumar, G. Joselin Retnar; Juliet, Vimala A.; Hassan, S. Adurrashid

doi:10.1007/s11277-022-09905-6

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…In general, the data fusion [ 17 , 18 ] is accomplished by each node discarding multiple copies of similar data. Therefore, the data fusion is used to remove the data redundancies of the network.…”

Section: Related Workmentioning

confidence: 99%

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Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

Qaffas

2023

Sensors

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A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.

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Section: Related Workmentioning

confidence: 99%

“…CHNs with high energy are preferred for performing the reliable data transmission. The residual energy is expressed in Equation (18).…”

Section: Fitness Of Qavoa For Ch Discoverymentioning

confidence: 99%

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

Qaffas

2023

Sensors

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Machine learning‐based data fusion method for wireless sensor networks

Liang,

Yao

2024

Adv Control Appl

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For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

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A Practical IoT-Based PM2.5 Air Contaminant Tracking Mechanism with Suitable Machine Learning Support

Gunasekar,

Kumar,

Agbulu

et al. 2024

Lecture Notes in Electrical Engineering

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PECDF-CMRP: A Power-Efficient Compressive Data Fusion and Cluster-Based Multi-hop Relay-Assisted Routing Protocol for IoT Sensor Networks

Cited by 6 publications

References 35 publications

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks

Machine learning‐based data fusion method for wireless sensor networks

A Practical IoT-Based PM2.5 Air Contaminant Tracking Mechanism with Suitable Machine Learning Support

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