Energy efficient fuzzy clustering and routing using BAT algorithm

Lipare, Amruta; Edla, Damodar Reddy; Ramesh, Dharavath

doi:10.1007/s11276-021-02615-0

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…According to the different data distribution densities in the WSN, the node combination and the forwarding path of data fusion are selected to reduce the network's energy consumption and control congestion. Lipare et al [22] proposed a load balancing clustering algorithm based on a fuzzy C-means (FCM) algorithm for cluster head selection and applied the bat-inspired routing algorithm to multi-hop between cluster heads to determine the best low-energy routing path. PSO, bat, and other swarm intelligence algorithms and machine learning classification algorithms can better solve nonlinear classification problems in cluster head and path selection, and are very suitable for dynamic network applications in probabilistic channels.…”

Section: Multi-hop Algorithm Between Cluster Headsmentioning

confidence: 99%

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A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

Wang

Miao

et al. 2022

Electronics

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Clustering of sensor nodes is a prominent method applied to wireless sensor networks (WSNs). In a cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes also have limited battery power. Therefore, energy efficiency in WSNs is crucial. The load on the sensor node and its distance from the base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSNs. In this paper, we propose a hybrid routing algorithm based on Naïve Bayes and improved particle swarm optimization algorithms (HRA-NP). The cluster heads (CHs) are selected according to the CH conditional probability, which is estimated by the Naïve Bayes classifier. After the selection of the CHs, the multi-hop routing algorithm is applied to the CHs. The best routing path from each CH to the BS is obtained from an improved particle swarm optimization (PSO) algorithm. Simulations were conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network, and the standard deviation of a load on the sensor node. It was observed that HRA-NP outperforms comparable algorithms, namely DUCF, ECRRS, and FC-RBAT, based on the evaluation factors.

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Section: Multi-hop Algorithm Between Cluster Headsmentioning

confidence: 99%

“…The simulations were carried out in MATLAB R2019. The proposed HRA-NP algorithm was compared with ECRRS [17], FC-RBAT [22], and DUCF [25]. All other parameters used in the simulations are presented in Table 1 [1,5,17,[39][40][41].…”

Section: Experiments Settingmentioning

confidence: 99%

A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

Wang

Miao

et al. 2022

Electronics

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“…In formula (6), N ′ represents the amount of noise added for the first division, and u 2 represents a constant.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Big data text clustering is a more complex basic work. Only by completing big data analysis from multiple perspectives and discovering potential data in time can we stably support the operation of computer system [5,6].…”

Section: Introductionmentioning

confidence: 99%

Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

Shu

2022

Wireless Communications and Mobile Computing

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In order to break through the limitations of current clustering algorithms and avoid the direct impact of disturbance on the clustering effect of abnormal big data texts, a big data text clustering algorithm based on swarm intelligence is proposed. According to the characteristics of swarm intelligence, a differential privacy protection model is constructed; At the same time, the data location information operation is carried out to complete the location information preprocessing. Based on KD tree division, the allocation of the differential privacy budget is completed, and the location information is clustered by dimension reduction. Based on the result of the location information dimension reduction clustering, a differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. Select the fuzzy confidence and support thresholds to extract the associated features, use the features to determine the big data text targets to be clustered, establish a target decision matrix, determine the clustering targets, and obtain the target weights, the obtained membership, and the decision results. Realize the effective clustering of big data text based on swarm intelligence. The experimental results show that the algorithm can effectively realize the high-efficiency clustering of big data texts, and the clustering time is short, and it has a good clustering effect.

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“…In this way, to prolong the network lifetime as much as possible with energy limitation, researchers have conducted a lot of research on ways which can effectively enhance the energy utilization. Finding that the design of network topology and the selection of routing have a significant impact on the energy dissipation of nodes [5][6][7], researchers improve the energy utilization in WAMRN by designing a reasonable clustering model and using efficient optimization algorithm [8,9]. Therefore, the focus of this paper is on the optimization of network clustering to effectively extend the lifetime of WAMRN.…”

Section: Introductionmentioning

confidence: 99%

A Novel Niche Quantum Ant Colony-Based Clustering Optimization Approach for Wireless Auto Meter Reading Network

Qin

Liu

et al. 2022

Journal of Sensors

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Since the low cost and high flexibility, wireless automatic meter reading network (WAMRN) is widely used by utility companies to realize automatic collection and transmission of remote energy consumption information. Considering that WAMRN is composed of several wireless communication nodes, the lifetime of the network will be affected by factors such as the changeable deployment environment and the limited energy of nodes. Thus, a novel niche quantum ant colony-based WAMRN clustering optimization method is proposed in this paper to address the problem of how to make full use of the limited energy to extend network lifetime and improve data transmission efficiency. In the proposed approach, a clustering model of WAMRN is defined; moreover, an improved Niche Quantum Ant Colony Optimization (NQACO) is proposed to optimize the model therefore to obtain an optimal clustering scheme, which can help WAMRN reduce unnecessary energy loss to achieve the purpose of extending the lifetime of the entire network. To verify the performance of the proposed method, NQACO is compared with some popular clustering methods, i.e., GA and SA, under different scenarios. The results show that under the premise of ensuring network communication, NQACO is superior to the other two methods in reducing the total energy consumption and prolonging the network lifetime.

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Energy efficient fuzzy clustering and routing using BAT algorithm

Cited by 17 publications

References 28 publications

A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

A Novel Niche Quantum Ant Colony-Based Clustering Optimization Approach for Wireless Auto Meter Reading Network

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