POWER: probabilistic weight-based energy-efficient cluster routing for large-scale wireless sensor networks

Farooq, Muhammad Umar; Wang, Xingfu; Hawbani, Ammar; Khan, Asad; Ahmed, Adeel; Alsamhi, Saeed Hamood; Qureshi, Bushra

doi:10.1007/s11227-022-04372-z

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Figures 2, 3, and 4 show the number of alive nodes, a comparison of the residual energy, and the energy consumption per round for 100 sensor nodes when the base station is in location (50,50). In this case approximation, the proposed algorithm gives a more number of alive nodes and higher available energy as compared to the base algorithm [39], Node Density-based Clustering (NDC) [40], and LEACH [11]. Figures 5, 6, and 7 display, for a total of 200 sensor nodes, the number of active nodes, a comparison of their residual energy, and their respective energy consumption per round (50,150).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This paper deals with the optimization of clustering algorithms for agriculture purposes. The optimal cluster numbers are xed using optimal cluster number selection in ad-hoc wireless sensor networks [39]. It rst analyses the entire network to give the optimal number of clusters.…”

Section: Related Workmentioning

confidence: 99%

“…LEACH [11] Algo2 Node Density based Clustering (NDC) [40] Algo3 base algorithm [39] Proposed Proposed Algorithm…”

Section: Algo1mentioning

confidence: 99%

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Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Rajpoot,

Avtar,

Pandey

et al. 2023

Preprint

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Wireless Sensor Networks have become a vast field of study. In the past few years, it has been seen that the use of wireless sensor networks in various fields, such as health monitoring, landslide detection, agriculture field, etc., is increased. In such real-life applications, many sensor nodes are required for monitoring and collecting data from the environment, and these sensor nodes work autonomously. The network of complete sensor nodes that works on an application is termed WSN. In a WSN, clustering refers to dividing the sensor nodes into several groups called clusters. A cluster head is selected among all clusters, temporarily acting as the particular cluster's base station. This cluster head is chosen based on several factors, including the node's residual energy, distance from the base station, and distance from other sensor nodes. Mainly the cluster head is responsible for increasing the efficiency and scalability of the entire network. Utilizing the Ordered Weighted Averaging (OWA) operator, we have suggested an algorithm for cluster head selection that works better and uses less energy during data transmission. Simulation results showed that our suggested algorithm outperforms others and extends network lifetime by 5–12%.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Rajpoot,

Avtar,

Pandey

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [30], the author surveys recent routing protocols for UWSN to inspire researchers to design efficient routing protocols. In [31], a probabilistic weight-based energy-efficient cluster routing is presented for large-scale wireless sensor networks protocol. Simulation results show that this protocol has better performance in many aspects, such as delay, throughput and packet delivery rate.…”

Section: Introductionmentioning

confidence: 99%

Formation Control of Multiple Autonomous Underwater Vehicles under Communication Delay, Packet Discreteness and Dropout

Zhang

et al. 2022

JMSE

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Effective communication between multiple autonomous underwater vehicles (AUVs) is necessary for formation control. As the most reliable underwater communication method, acoustic communication still has many constraints compared with radio communication, which affects the effectiveness of formation control. Therefore, this paper proposes a formation control scheme for multiple AUVs under communication delay, packet discreteness and dropout. Firstly, the communication delay is estimated based on the kernel density estimation method. To solve the problem of packet discreteness and dropout, the curve fitting method is used to predict the states of the AUV. Secondly, a follower controller is designed based on the leader–follower approach using input–output feedback linearization, which is proven to be stable with Lyapunov stability theory. Then, some simulation results are presented to demonstrate the stability and accuracy of the formation control in different communication environments. Finally, the field tests on the lake show that the scheme introduced in this paper is valid and practical.

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“…Aegilops usually deciduous broad-leaved plants, others are evergreen or semi-evergreen broad-leaved plants. Most of evergreen species are called sclerophylla oak because their leaves are leathery, hard and spiny ( Chaudhri et al., 2022 ; Farooq et al., 2022 ; Ismail et al., 2022 ). There has been a great controversy about the relationship between species within the sclerophylla oak.…”

Section: Introductionmentioning

confidence: 99%

Electrochemical fingerprinting sensor for plant phylogenetic investigation: A case of sclerophyllous oak

Yin²,

Zheng³

et al. 2022

Front. Plant Sci.

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Electrochemical fingerprinting can collect the electrochemical behavior of electrochemically active molecules in plant tissues, so it is regarded as a new plant analysis technology. Because the signal of electrochemical fingerprinting is positively correlated with the amount and type of electrochemically active molecules in plant tissues, it can also be used to reflect genetic differences between different species. Previous electrochemical fingerprinting techniques have been frequently used in phylogenetic studies of herbaceous plants. In this work, 19 Quercus species (17 evergreen or semi evergreen species and 2 deciduous species) were selected for investigation. The results indicated the electrochemical fingerprint of some species share similar features but can be distinguished after changing the recording condition (extraction solvent and electrolyte). The two sets of electrochemical fingerprint data can be used to construct different pattern recognition technology, which further speeds up the recognition efficiency. These electrochemical fingerprints were further used in phylogenetic investigations. The phylogenetic results deduced from electrochemical fingerprinting were divided mainly into three clusters. These can provide evidence for some of these arguments as well as new results.

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POWER: probabilistic weight-based energy-efficient cluster routing for large-scale wireless sensor networks

Cited by 11 publications

References 32 publications

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Formation Control of Multiple Autonomous Underwater Vehicles under Communication Delay, Packet Discreteness and Dropout

Electrochemical fingerprinting sensor for plant phylogenetic investigation: A case of sclerophyllous oak

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