Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

Hou, Jiwei; Qiao, Jianhua; Han, Xinglong

doi:10.1109/jsen.2021.3132682

Cited by 19 publications

(11 citation statements)

References 25 publications

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“…As one of the most fundamental tasks in data mining, clustering aims to divide unlabeled data into groups based on similarity without supervision, instead of offering a precise characterization of unobserved samples [21]. The classic clustering algorithms include graph theory-based clustering, combinatorial search techniques-based clustering, fuzzy clustering, neural networks-based clustering, and kernels techniques-based clustering.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

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Section: Clustering Algorithmmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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“…However, EAUCF did not consider the density of nodes in the cluster head election stage, and did not solve the problems of hot spots and energy holes in dynamic networks. In order to solve the problem of node density which is not considered in EAUCF algorithm, EFCR [30] introduces the number of neighboring nodes of a node in the fuzzy input parameter part, at the same time, EFCR algorithm also adds the node's distance to the base station and the node's residual energy as the two parameters affecting the node's energy consumption, and selects the node with the highest degree of cluster-head adaptation for each area as the cluster head in data transmission phase, and collects the data transmitted by non-cluster-head nodes. The transmitted data is fused and sent to the base station.…”

Section: Related Workmentioning

confidence: 99%

“…In this synergistic approach the individual contributes to each dimension of the gbest and is able to obtain a better individual. Take the example of a four-dimensional function f = |x−z| 2 , where z = (10,20,30,40). The global optimum is X* = z.…”

Section: Plos Onementioning

confidence: 99%

Efficient cluster-based routing protocol for wireless sensor networks by using collaborative-inspired Harris Hawk optimization and fuzzy logic

Hu,

Fan,

Wang

2024

PLoS ONE

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In wireless sensor networks, the implementation of clustering and routing protocols has been crucial in prolonging the network’s operational duration by conserving energy. However, the challenge persists in efficiently optimizing energy usage to maximize the network’s longevity. This paper presents CHHFO, a new protocol that combines a fuzzy logic system with the collaborative Harris Hawks optimization algorithm to enhance the lifetime of networks. The fuzzy logic system utilizes descriptors like remaining energy, distance from the base station, and the number of neighboring nodes to designate each cluster head and establish optimal clusters, thereby alleviating potential hot spots. Moreover, the Collaborative Harris Hawks Optimization algorithm employs an inventive coding mechanism to choose the optimal relay cluster head for data transmission. According to the results, the network throughput, HHOCFR is 8.76%, 11.73%, 8.64% higher than HHO-UCRA, IHHO-F, and EFCR. In addition, he energy consumption of HHOCFR is lower than HHO-UCRA, IHHO-F, and EFCR by 0.88%, 39.79%, 34.25%, respectively.

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“…Ref. [ 8 ] applied fuzzy inference to the CH selection, and designed two different clustering approaches with a threshold for the improved network lifetime. To reduce the load on CH, Ref.…”

Section: Introductionmentioning

confidence: 99%

Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization

Zheng

Zhang

Liu

et al. 2022

Sensors

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Energy conservation is one of the main problems in a wireless sensor network (WSN). Compared with a single cluster head (CH), a dual CH optimization was proposed for less energy consumption by the WSN and an acquisition delay by the mobile sink (MS). Firstly, a fuzzy c-means clustering algorithm and a multi-objective particle swarm optimization were utilized for the determinations of the first and second CHs. Following that, the ideal trajectory of MS was assessed using the improved ant colony algorithm. Finally, the lifetimes, the death rounds of the first node and the 50% node, and the number of packets received at the base station were compared among the proposed approach. Moreover, five algorithms were compared to validate the optimization, and the improved trajectory was compared with the original one as well. It was found that, for 100 nodes, the number of dead rounds from the proposal increased by 7.9%, 22.9%, 25.1%, 61%, and 74.4% for the first node, and that of the 50% nodes increased by 27.8%, 34.2%, 98.3%, 213.1%, and 211.2%, respectively. The base station packet reception increased by about 19.3%, 53.5%, 27%, 86.8%, and 181.2%, respectively. The trajectory of MS could also decrease by about 10%.

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Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

Cited by 19 publications

References 25 publications

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Efficient cluster-based routing protocol for wireless sensor networks by using collaborative-inspired Harris Hawk optimization and fuzzy logic

Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization

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