A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

Wu, Manman; Li, Zhengliang; Min, Qiusha; Lü, Tao

doi:10.3390/s22249731

Cited by 20 publications

(9 citation statements)

References 39 publications

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“…In [ 29 ], the authors proposed a distributed topology control algorithm, which optimizes the topology based on the real-time residual energy of nodes. Similar works can be found in [ 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Introductionsupporting

confidence: 76%

Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation

Yang

Gao

et al. 2023

Sensors

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In the distributed information fusion of wireless sensor networks (WSNs), the filtering accuracy is commonly negatively correlated with energy consumption. Therefore, a class of distributed consensus Kalman filters was designed to balance the contradiction between them in this paper. Firstly, an event-triggered schedule was designed based on historical data within a timeliness window. Furthermore, considering the relationship between energy consumption and communication distance, a topological transformation schedule with energy-saving is proposed. The energy-saving distributed consensus Kalman filter with a dual event-driven (or event-triggered) strategy is proposed by combining the above two schedules. The sufficient condition of stability for the filter is given by the second Lyapunov stability theory. Finally, the effectiveness of the proposed filter was verified by a simulation.

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Section: Introductionsupporting

confidence: 76%

Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation

Yang

Gao

et al. 2023

Sensors

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“…Meanwhile, the vice cluster head is selected considering the node's residual energy and its distance from the base station. Simulation results demonstrate that DCK-LEACH significantly extends the lifetimes of energy-critical nodes and the overall network lifetime compared to existing protocols such as LEACH, K-LEACH, IEECHS-WSN, and RCH-LEACH [27].…”

Section: Related Workmentioning

confidence: 95%

ANN Based Clusterhead Selection Process in WSN

GANGAL,

SESLI,

HACIOGLU

2024

Preprint

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Wireless sensor networks often face a significant challenge due to their limited energy capacity. To address this challenge, numerous studies are currently underway to explore innovative ways to optimize energy usage. This study proposes an energy-efficient cluster head selection method that is supported by artificial neural networks for cluster-based sensor networks, thereby enhancing their energy efficiency. The neural network is placed in each sensor node. The nodes decide to be the cluster head according to the threshold they set using the ANN, without the need for centralized control. According to the simulation studies conducted, the ANN trained with 5-5-5 neurons survived for 3497 rounds and successfully transmitted 96757 packets to the sink. In contrast, the LEACH algorithm could only survive for 1773 rounds and transmitted 61766 packets to the sink under the same conditions. The study confidently concluded that there was a significant and noteworthy improvement in both the network lifetime and the number of packets sent to the base station. On the other hand, it was also observed that the proposed method showed a notable improvement when compared with similar recent studies conducted with the same parameters by a factor ranging from 3.63 to 6.26.

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“…In [32] In [35], Agrawal et al provided a grey wolf optimizer-based clustering scheme for WSN: GWO-C. In GWO-C, the parameters used for the derivation of fitness function are average distance from CMs to their respective CH, average distance from BS to all CHs, average residual energy of all CHs and CH balancing factor, respectively.…”

Section: Heuristic Clustering Algorithmsmentioning

confidence: 99%

Binary Bat Algorithm Based Clustering Approach for Wireless Sensor Networks

CAO,

XU,

2024

Preprint

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Reducing the energy consumption of nodes and prolonging network lifetime is a key issue in wireless sensor networks (WSN) research field. Clustering is an effective method to solve this problem. However, the effectiveness of existing clustering algorithms is not satisfactory. Therefore, we propose a binary bat algorithm based clustering approach (BBACA) in this paper. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem which is solved by using binary bat algorithm (BBA) with excellent performance. Secondly, a novel binary artificial bat encoding scheme and a fitness function using multiple parameters are designed elaborately. Finally, the final clustering scheme of WSN is obtained through multiple iterations of BBA. Simulation results show that BBACA can significantly reduce the energy consumption of nodes, extend the lifetime of the network, and achieve an outstanding clustering effect compared with other clustering algorithms.

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A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

Cited by 20 publications

References 39 publications

Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation

Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation

ANN Based Clusterhead Selection Process in WSN

Binary Bat Algorithm Based Clustering Approach for Wireless Sensor Networks

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