Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks

Dhinnesh, A. D. C. Navin; Sabapathi, T.

doi:10.1007/s12652-021-02961-z

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Each CH must wait until all the packets are processed, which includes propagation and queuing delays. In general, network latency is measured in terms of time delay associated with the distance the packet travels at a determined transmission rate to the size of the packet at the specified transmission rate [41]. The formula for network latency over the transmission rate T r is determined based on Equation (7).…”

Section: Network Latencymentioning

confidence: 99%

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

Ahmed

2023

Sensors

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A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model’s performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

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Section: Network Latencymentioning

confidence: 99%

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

Ahmed

2023

Sensors

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“…With the continuous improvement of network technology, human interaction has changed significantly, in which Wireless Sensor Network (WSN) plays a significant role [1]- [3]. WSN can collect and process data information, such as the environment of the monitored area, in real time and deliver the processed information to the relevant users.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization

Chen

2023

EAI Endorsed Trans Energy Web

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WSN localization occupies an important position in the practical application of WSN. To complete WSN localization efficiently and accurately, the article constructs the objective function based on the target node location constraints and maximum likelihood function. It avoids premature convergence through the PSO algorithm based on chaos search and backward learning. Based on linear fitting, the node-flipping fuzzy detection method is proposed to perform the judgment of node flipping fuzzy phenomenon. And the detection method is combined with the localization algorithm, and the final WSN localization algorithm is obtained after multi-threshold processing. After analysis, it is found that compared with other PSO algorithms, the MTLFPSO algorithm used in the paper has better performance with the highest accuracy of 83.1%. Different threshold values will affect the favorable and error detection rates of different WSNs. For type 1 WSNs, the positive detection rate of the 3-node network is the highest under the same threshold value, followed by the 4-node network; when the threshold value is 7.5 (3 ), the positive detection rate of the 3-node network is 97.8%. Different numbers of anchor nodes and communication radius will have specific effects on the number of definable nodes and relative localization error, in which the lowest relative localization error of the MTLFPSO algorithm is 3.4% under different numbers of anchor nodes; the lowest relative localization error of MTLFPSO algorithm is 2.5% under different communication radius. The article adopts the method to achieve accurate and efficient localization of WSNs.

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Multi-objective Grey Wolf Optimization based self configuring wireless sensor network

Navin Dhinnesh,

Sabapathi

2024

Wireless Netw

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Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks

Cited by 8 publications

References 32 publications

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization

Multi-objective Grey Wolf Optimization based self configuring wireless sensor network

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