Secured node detection technique based on artificial neural network for wireless sensor network

Hasan, Bassam; Alani, Sameer; Saad, Mohammed Ayad

doi:10.11591/ijece.v11i1.pp536-544

Cited by 17 publications

(14 citation statements)

References 17 publications

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“…Artificial neural networks (ANN) is one of many important features of artificial intellegent [25,26]. The neuron is the basic element of an artificial neural network which has a summer and an activation function [26] as shown in Figure 3 The model of a neuron is given by: For ensure best result and fast convergence it must to use the algorithm equation [27]. The main algorithm equations of the neural network as ( 13), (14).…”

Section: Neural Network Principlementioning

confidence: 99%

Impact of sensorless neural direct torque control in a fuel cell traction system

Aissa¹,

Hamza²,

Guettaf

et al. 2021

IJECE

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<span>Due to the reliability and relatively low cost and modest maintenance requirement of the induction machine make it one of the most widely used machines in industrial applications. The speed control is one of many problems in the traction system, researchers went to new paths instead the classical controllers as PI controller, they integrated the artificial intelligent for its yield. The classical DTC is a method of speed control by using speed sensor and PI controller, it achieves a decoupled control of the electromagnetic torque and the stator flux in the stationary frame, besides, the use of speed sensors has several drawbacks such as the fragility and the high cost, for this reason, the specialists went to propose an estimators as Kalman filter. The fuel cell is a new renewable energy, it has many applications in the traction systems as train, bus. This paper presents an improved control using DTC by integrate the neural network strategy without use speed sensor (sensorless control) to reduce overtaking and current ripple and static error in the system because the PI controller has some problems like this; and reduce the cost with use a renewable energy as fuel cell.</span>

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Section: Neural Network Principlementioning

confidence: 99%

Impact of sensorless neural direct torque control in a fuel cell traction system

Aissa¹,

Hamza²,

Guettaf

et al. 2021

IJECE

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“…) 𝐸 𝐷𝐴 + 𝐸 𝑇𝑥𝐶𝐻𝐴 (10) where (𝐸 𝑅𝑥𝐶𝐻𝐴 = 𝑙 𝑚 𝐸 𝑒𝑙𝑒𝑐 ) is the dissipating energy to receive the lm-bits message from the active cluster member nodes. During the aggregation of data from active CM nodes, the energy dissipated to aggregate data 𝐸 𝐷𝐴 can be expressed as ( 𝐸 𝐷𝐴 = 𝑙 𝑚 𝐸 𝑑𝑎 ).…”

Section: − Energy Consumption Analysis For Clustering Functionmentioning

confidence: 99%

Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs

Saleh

Abdulrahman

Salman

2021

IJEECS

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Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the energy levels of the node battery by utilizing various energy harvesting techniques in order to extend the network lifetime. In this paper, a new energy-aware clustering algorithm (EHEARA) has been proposed. The proposed algorithm is based on a dynamic clustering function and adopts a solar energy harvesting scheme in order to improve network lifetime. Furthermore, the active-sleep mechanism was used to distribute node activity and balance communication among nodes within clusters and cluster heads with the base station. The proposed algorithm is simulated using matrix laboratory (MATLAB), and the results show that it outperforms the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC), and stable election protocol (SEP) algorithms in terms of network lifetime, energy consumption, and network throughput.

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“…where W is the stress concentration factor; B is the buried depth of coal seam, n; and x is the bulk density of the overlying strata, WB/n. In the above formula, assuming that the internal friction angle θ [23] is a fixed value, it is concluded that y 1 only increases with the increase of the thickness of the coal seam under the same coal seam condition. erefore, the peak point of the support pressure in front of the working face [24] increases with the height of the mining.…”

Section: Calculation Of Bearing Pressurementioning

confidence: 99%

Research and Control of Underground Pressure Law of Coal Mining Face in Soft Rock Strata Based on Neural Network under the Background of Wireless Network Communication

Rong

Tong

2021

Mobile Information Systems

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Our country has a vast territory and rich resources, but it is a country with more coal, less oil, and poor gas. With the increase of our population, the development of society, and the more severe international situation, coal has become more important for our country’s economic development and energy. Security plays an irreplaceable role. Based on the neural network, this paper studies and controls the underground pressure law of the coal mine’s soft rock heading face, aiming at the safe and efficient mining of the first face and providing an experience for the next face. This paper mainly uses BP neural network learning algorithm and support pressure algorithm to measure and study the ground pressure law of coal mine soft rock heading face and establishes the ground pressure online monitoring system, which is used to analyze and summarize the ground pressure abnormal area during the mining of the working face, so as to provide the basis for safe mining of the working face. Through the field measured data, the initial pressure step and periodic pressure step at the upper, middle, and lower parts of the working face, the average working resistance of the support at the working face during pressure, and the dynamic load coefficient of the support are obtained. It is analyzed that the support in the middle of the working face has a large load and the pressure is obvious. The experimental results show that the initial support force of the whole working face is approximately normally distributed, the proportion of the initial support force in the range of 10–30 MPa accounts for more than 85% of the total statistics, and the frequency of the initial support force in the upper, middle, and lower stations at 10–25 MPa is 55%–65%.

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Secured node detection technique based on artificial neural network for wireless sensor network

Cited by 17 publications

References 17 publications

Impact of sensorless neural direct torque control in a fuel cell traction system

Impact of sensorless neural direct torque control in a fuel cell traction system

Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs

Research and Control of Underground Pressure Law of Coal Mining Face in Soft Rock Strata Based on Neural Network under the Background of Wireless Network Communication

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