Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks

Hu, Huangshui; Yu-Xin, Guo; Wang, Chuhang; Gao, Dong

doi:10.1109/access.2022.3188258

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…Khedr et al [29] developed an approach to reduce the data delivery latency of the network called as fuzzy C-means with routing protocol clustering model. Huang-Shui et al [30] presented a new method to increase the lifespan of WSNs in an effective manner. For that purpose affinity propagation clustering approach and as well chaotic lion swarm optimization is used for optimal path selection in the network.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Al-Heeti,

Hammad,

Mustafa

2024

Bulletin EEI

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Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

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Section: Related Workmentioning

confidence: 99%

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Al-Heeti,

Hammad,

Mustafa

2024

Bulletin EEI

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“…Consequently, the hungry vultures certainly become violent and surround strong vultures to obtain food instead of discovering food themselves. The exploration and development stage of vultures are varied based on the starvation rate that is expressed in Equations ( 5) and (6).…”

Section: Vulture Starvation Ratementioning

confidence: 99%

“…Each node in the WSN is installed with limited storage, energy and processing capacities. Therefore, energy preservation is considered as an important task for improving the life expectancy, and clustering is considered as the most energy-saving approach in the WSN [ 6 , 7 , 8 ]. A clustering approach is used to group the sensors, namely clusters where each cluster has one header node, i.e., Cluster Head (CH).…”

Section: Introductionmentioning

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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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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“…To overcome this issue, work in [7] proposes use of "Low-Energy Dynamic Clustering" (LEDC) which assists in improving clustering performance via dynamically grouping nodes based on destination node locations. Similar models are discussed in [8,9,10], which propose use of "Voronoi Adaptive Clustering" (VAC), Fuzzy Inferences, and "Chaotic Lion Swarm Optimization" (CLSO), that integrate bioinspired computing with low complexity routing processes. Extensions to these models are discussed in [11,12,13], which propose use of Hierarchical Clustering, Uneven Clustering, and sensor node's training for intellectual data transmission by using of clustering and reinforcement learning (SARSA), which aim at reducing complexity while maintaining higher QoS for multiple scale networks.…”

Section: Literature Reviewmentioning

confidence: 99%

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

Annaji M Kuthe

2023

IJRITCC

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To enhance energy level in WSN is a research requirement, which assists in improving their lifetime over a series of communications. To achieve this target, a various variety of clustering & sleep scheduling models are discussed by researchers. Most of these models deploy static clustering & sleep scheduling operations, which limits their applicability & scalability levels. Moreover, dynamic clustering & scheduling models are highly complex, which reduces temporal QoS performance under real-time use cases. In order to reduce the probability of these issues, this text discusses design of the proposed Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN. The proposed model initially deploys a Grey Wolf Optimization (GWO) Method for dynamic sleep scheduling via temporal performance analysis. The GWO Method models a fitness function that combines temporal usage levels, temporal Quality of Service (QoS), and temporal energy levels. Based on this modelling process, nodes were categorized into wake & sleep nodes, which were further clustered via destination-aware Fan Shaped Clustering (FSC), that assisted in improving QoS performance under multiple scenarios. The FSC Model was combined with a QoS-aware routing model, that assisted in selection of routing paths that can achieve low delay, high throughput, and high packet delivery with higher energy efficiency levels. Efficiency of the model was tested on node & network conditions, and its QoS performance was checked in terms of communication delay, consumption of energy, level of throughput, and Packet Delivery Ratio (PDR) levels. On the basis of these comparative evaluations, it is observed that the new proposed model is able to enhance end-to-end delay by 8.5%, reduce level of energy by 15.5%, while increasing throughput by 8.3%, and PDR by 1.5%, thus making it useful for a different real-time conditions.

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Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks

Cited by 9 publications

References 34 publications

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

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

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

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