An Enhanced Sparrow Search Based Adaptive and Robust Data Gathering Scheme for WSNs

Khedr, Ahmed M.; Aghbari, Zaher Al; Raj, P. V. Pravija

doi:10.1109/jsen.2022.3167515

Cited by 19 publications

(2 citation statements)

References 31 publications

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“…A mobile sink is deployed in the network and the network parameters such as residual energy, network lifetime, data collection delay and tour cost are compared with HACDC, 9 ESRP, 36 QLMS 28 and QWRP 37 . Figure 7 shows a comparison of these approaches to the proposed algorithm with FND, LND and HND conditions.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Rajagopal

Bhanumathi

Sakthivel

2023

Int J Communication

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This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

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Section: Numerical Simulationsmentioning

confidence: 99%

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Rajagopal

Bhanumathi

Sakthivel

2023

Int J Communication

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“…Literature [14] improved the sparrow search algorithm by introducing inertial weights in order to improve the search speed, and Literature [15] further enhanced node localization performance by using the Doppler effect. Khedr, Ahmed M et al [16] proposed an adaptive enhanced sparrow search algorithm combined with the ant colony algorithm, and experimental results showed that the adaptive enhanced sparrow search algorithm performed better. In the same year, Arulkumar, T et al [17] proposed an adaptive update sparrow search algorithm (AU-SSA) combined with a proportionalintegral (PI) controller and obtained good control results.…”

Section: Introductionmentioning

confidence: 99%

ALSSA-PID control-based wheel-side motor control system for four-wheel-drive electric vehicles

XU,

DING,

XIAO

et al. 2024

Preprint

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To enhance the response speed and accuracy of the motor in a four-wheel side motor-driven electric vehicle for improved vehicle handling stability, this paper proposes a chaotic adaptive sparrow-optimized PID control algorithm. Firstly, the structural principles of the four-wheel side motor-driven electric vehicle are analyzed. A mathematical model and a simulation model of the electric vehicle are established, and the accuracy of the model is verified through simulation experiments. Subsequently, the traditional sparrow algorithm is improved using Logistic-Tent chaotic mapping, adaptive search parameters, and the Levy flight strategy. These modifications enable the algorithm to escape local optima and achieve better convergence speed and accuracy. Based on these improvements, the ALSSA-PID control algorithm is developed, and the motor model is simulated and tested. Finally, a physical control platform for the wheel-edge motor is constructed to test the control algorithm. The results indicate that the rise time of the ALSSA-PID control algorithm is reduced by an average of 40.59% and 5.66% compared to open-loop control and SSA-PID control at various control speeds, respectively, while the stabilization time is reduced by an average of 27.72% and 15.59%. These findings demonstrate that the proposed control algorithm significantly improves the control performance of the wheel-edge motor control system.

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Discrete Reptile Search Algorithm-Based Clustering Technique for Flying Ad Hoc Networks

Pravija Raj,

Khedr,

Mostafa

2024

Studies in Computational Intelligence

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An Enhanced Sparrow Search Based Adaptive and Robust Data Gathering Scheme for WSNs

Cited by 19 publications

References 31 publications

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

ALSSA-PID control-based wheel-side motor control system for four-wheel-drive electric vehicles

Discrete Reptile Search Algorithm-Based Clustering Technique for Flying Ad Hoc Networks

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