Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

Wu, Haixing; Jin, Shunfu; Yue, Wuyi

doi:10.1007/s11518-022-5521-0

Cited by 63 publications

(21 citation statements)

References 26 publications

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“…Figure 1 displays an MLP-NN where m, l, and s, respectively, stand for the number of input nodes, hidden nodes, and output nodes [51,52]. As observed, there is a one-sided junction betwixt the nodes of MLP-NN, which is among the group of neural networks (FNN) [53,54]. MLP-NN output is computed by…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 98%

Feature Selection and Training Multilayer Perceptron Neural Networks Using Grasshopper Optimization Algorithm for Design Optimal Classifier of Big Data Sonar

Kosarirad

Nejati

Saffari³

et al. 2022

Journal of Sensors

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The complexity and high dimensions of big data sonar, as well as the unavoidable presence of unwanted signals such as noise, clutter, and reverberation in the environment of sonar propagation, have made the classification of big data sonar one of the most interesting and applicable topics for active researchers in this field. This paper proposes the use of the Grasshopper Optimization Algorithm (GOA) to train Multilayer Perceptron Artificial Neural Network (MLP-NN) and also to select optimal features in big data sonar (called GMLP-GOA). GMLP-GOA hybrid classifier first extracts the features of experimental sonar data using MFCC. Then, the most optimal features are selected using GOA. In the last step, MLP-NN trained with GOA is used to classify big data sonar. To evaluate the performance of GMLP-GOA, this classifier is compared with MLP-GOA, MLP-GWO, MLP-PSO, MLP-ACO, and MLP-GSA classifiers in terms of classification rate, convergence rate, local optimization avoidance power, and processing time. The results indicated that GMLP-GOA achieved a classification rate of 98.12% in a processing time of 3.14 s.

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Section: Multilayer Perceptron Neural Networkmentioning

confidence: 98%

Feature Selection and Training Multilayer Perceptron Neural Networks Using Grasshopper Optimization Algorithm for Design Optimal Classifier of Big Data Sonar

Kosarirad

Nejati

Saffari³

et al. 2022

Journal of Sensors

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“…Nonetheless, due to connectedness issues, such as interference management or channel contention over a shared spectrum wireless network, an unsatisfactory network may sometimes cause devices to fall off. Intermittent connectivity and strategies for continuous service as major components should be included in IoT architecture designs 105 …”

Section: Backgroundsmentioning

confidence: 99%

The importance of nature‐inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

Dong‐liang

Bei

Wang

2023

Int J Communication

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Summary Over the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT‐based networks due to increased communication power consumption. It has become crucial to propose solutions for network‐based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP‐hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies.

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“…Tis scheme is expressed as an optimization formulation. Tis formulation includes objective functions [29][30][31][32][33] and constraints [34][35][36][37]. Tis scheme is an attempt to minimize the diference between the operating costs of nonrenewable DGs (NRDGs) and MG with the revenue from the sale of energy/reactive power in the mentioned markets by the mentioned elements.…”

Section: A Deterministic Model Of the Proposed Schemementioning

confidence: 99%

“…So, this problem obtains the highest possible uncertainty level, and the values of uncertainty variables, (30), are determined. Te values obtained for the uncertainty variables correspond to the worst-case scenario because, according to (37) and (38), with increasing the value of the uncertainty level, the solution space of the proposed problem decreases compared to the deterministic model; hence, the value of the objective function or cost in these conditions is more than the deterministic model. Tus, the scenario obtained from problem (36)-( 40) represents the worst-case scenario.…”

Section: Igdt-based Uncertaintymentioning

confidence: 99%

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IGDT-Based Robust Operation of Thermal and Electricity Energy-Based Microgrid with Distributed Sources, Storages, and Responsive Loads

Sarlak

Samimi

Nikzad

et al. 2022

International Transactions on Electrical Energy Systems

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In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties.

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Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

Cited by 63 publications

References 26 publications

Feature Selection and Training Multilayer Perceptron Neural Networks Using Grasshopper Optimization Algorithm for Design Optimal Classifier of Big Data Sonar

Feature Selection and Training Multilayer Perceptron Neural Networks Using Grasshopper Optimization Algorithm for Design Optimal Classifier of Big Data Sonar

The importance of nature‐inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

IGDT-Based Robust Operation of Thermal and Electricity Energy-Based Microgrid with Distributed Sources, Storages, and Responsive Loads

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