Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wang, Jin; Gao, Yu; Zhou, Chang; Sherratt, R. Simon; Wang, Lei

doi:10.32604/cmc.2020.08674

Cited by 207 publications

(144 citation statements)

References 21 publications

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“…An energy-efficient routing algorithm based on information collected by a mobile agent in uneven clustering for wireless sensor networks (WSNs) is presented in Reference [18]. The PSO and GA adopt a schedule moving trajectory for the mobile sink for handling problems of hot spots in large-scale WSN, as implemented in Reference [19]. Authors in Reference [20] improve the bat algorithm in three ways by accelerating convergence processes of the bat algorithm via enhanced APF, enhancing the adaptive inertia weight and avoiding trapping in the local minimum.…”

Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

128

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Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

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Section: Introductionmentioning

confidence: 99%

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Ajeil

Ibraheem

Azar

et al. 2020

Sensors

128

View full text Add to dashboard Cite

show abstract

“…Besides, Wang et al 18 provided a new trajectory scheduling approach based on the coverage rate for large-scale wireless sensor networks with multiple mobile sinks. In this paper, particle swarm optimization algorithm is improved by mutation strategy and used to search the promising solution with keeping the convergence rate of the network.…”

Section: Related Workmentioning

confidence: 99%

“…17 "Contention occurs between different flows and different packets of a flow." 18 Interference happens when there are concurrent transmissions alongside multiple network paths amid the nearby nodes. Lower level congestion occurs as the result of packet drops from packet collision.…”

Section: Related Workmentioning

confidence: 99%

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Shahidinejad

Barshandeh

2020

Int J Communication

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Summary Applying multiple sink nodes in a large‐scale wireless sensor networks (WSN) can increase the scalability and lifetime of the network. The current sink selection mechanisms assume an unlimited amount of buffer and bandwidth for the sink nodes. This can be problematic in real‐world applications, especially when many cluster heads select a specific sink node and send their data to the sink at the same time. In this situation, the sink node may not have enough buffer to receive and process data; consequently, some packets are dropped. To mitigate these occasions, a fuzzy‐based controller with reduced rules is proposed for sink selection by considering the capacity of the sink nodes. The capacity of the sink nodes is estimated using the long short‐term memory (LSTM) technique. Then another fuzzy‐based controller with reduced rules is designed to select the cluster head. The fuzzy rules are reduced by employing R‐implications method. Reducing the number of fuzzy rules decreases the complexity of the fuzzy controllers. The results show the efficiency of the proposed sink selection and clustering techniques in terms of consumed energy, remaining energy, first node dead (FND), half nodes dead (HND), last node dead (LND), packet loss, and delay.

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“…Remote sensing images play an important role in military, civil engineering, agriculture, and other fields [2]. With the further development of remote sensing equipment and wireless sensor networks [3][4][5][6], the way of obtaining remote sensing images is more convenient, and the demand for remote sensing scene classification is getting increasingly more urgent. Therefore, many researchers pay attention to this field [1,2,[7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification

Zhang

Wang

et al. 2020

Sensors

Self Cite

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Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.

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Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Cited by 207 publications

References 21 publications

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification

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