scholarly journals Evaluation of a wireless low-energy mote with fuzzy algorithms and neural networks for remote environmental monitoring

2021 ◽  
Vol 23 (2) ◽  
pp. 717
Author(s):  
Ricardo Yauri ◽  
Jinmi Lezama ◽  
Milton Rios
Keyword(s):  
Neural Networks ◽  
Cell Voltage ◽  
Life Time ◽  
Low Energy ◽  
Memory Space ◽  
Fuzzy Logic Algorithm ◽  
Fuzzy Algorithms ◽  
Neural Network Algorithm ◽  
Save Energy ◽  
The Internet Of Things

The devices developed for applications in the internet of things have evolved technologically in the improvement of hardware and software components, in the area of optimization of the life time and to increase the capacity to save energy. This paper shows the development of a fuzzy logic algorithm and a power propagation neural network algorithm in a wireless mote (IoT end device). The fuzzy algorithm changes the transmission frequency according to the battery voltage and solar cell voltage. Moreover,the implementation of algorithms based on neural networks, implied a challenge in the evaluation and study of the energy commitment for the implementation of the algorithm, memory space optimization and low energy consumption.

Research on Low Power Routing Protocol for WSNs

2014 ◽  
Vol 610 ◽  
pp. 927-932
Author(s):  
Ahmed Rouaba ◽  
Nouamane Soualmi ◽  
He Zun Wen
Keyword(s):  
Energy Source ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Communication Systems ◽  
Cluster Head ◽  
Life Time ◽  
Low Energy ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Save Energy

A wireless sensor network (WSN) consists of large number of autonomous sensors nodes; these nodes communicate with each other in dispersed manner to observe the environment. WSNs become one of the most important researches in modern communication systems. The energy source of nodes is limited and practically it is impossible to change or charge the battery. In order to save energy and increases the life time of battery in WSNs. Many energy routing protocols using the clustering were proposed in the literature. Low Energy Adaptive Clustering Hierarchy (LEACH) is the most famous routing protocol. In this paper we propose a new algorithm to choose the cluster head which has the highest energy. We shared the network to four regions, between them 90° for each part we find the powerful sensor between the sensors groups, and this last will be the cluster head of this round. Each sensor sends its data to the nearest cluster head and this last will send it to the sink. The same work for five and six clusters heads with sink in the center and in the corner (100, 0) is done.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

Design and development of an intelligent system based on the Internet of Things for the early detection of forest fires

2022 ◽  
Vol 12 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Forest Fires ◽  
Mobile Applications ◽  
Intelligent System ◽  
The Internet ◽  
Scientific Practices ◽  
Depth Analysis ◽  
Neural Network Algorithm ◽  
The Internet Of Things

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

scholarly journals Convolutional Neural Networks for Low Energy Gamma-Ray Air Shower Identification with HAWC

2021 ◽  
Author(s):  
Ian Watson ◽  
Anushka Udara Abeysekara ◽  
Andrea Albert ◽  
Ruben Alfaro ◽  
César Alvarez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gamma Ray ◽  
Low Energy ◽  
Air Shower ◽  
Energy Gamma

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

Bluetooth Low-Energy-Based Applications

2018 ◽  
pp. 95-112
Author(s):  
Smita Sanjay Ambarkar ◽  
Rakhi Dattatraya Akhare
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Smart Devices ◽  
The Internet ◽  
Bluetooth Low Energy ◽  
Iot Applications ◽  
Proximity Detection ◽  
Battery Power ◽  
Commercial Market ◽  
The Internet Of Things

This chapter focuses on the comprehensive contents of various applications and principles related to Bluetooth low energy (BLE). The internet of things (IoT) applications like indoor localization, proximity detection problem by using Bluetooth low energy, and enhancing the sales in the commercial market by using BLE have the same database requirement and common implementation idea. The real-world applications are complex and require intensive computation. These computations should take less time, cost, and battery power. The chapter mainly focuses on the usage of BLE beacons for indoor localization. The motive behind the study of BLE devices is that it is supported by mobile smart devices that augment its application exponentially.

A Review of Cryptographic Algorithms for the Internet of Things

2019 ◽  
pp. 67-93 ◽  
Author(s):  
Issmat Shah Masoodi ◽  
Bisma Javid
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
The Internet ◽  
Reference Architecture ◽  
Memory Space ◽  
Security Issues ◽  
Research Challenges ◽  
Short Period ◽  
Cryptographic Algorithms ◽  
The Internet Of Things

There are various emerging areas in which profoundly constrained interconnected devices connect to accomplish specific tasks. Nowadays, internet of things (IoT) enables many low-resource and constrained devices to communicate, do computations, and make smarter decisions within a short period. However, there are many challenges and issues in such devices like power consumption, limited battery, memory space, performance, cost, and security. This chapter presents the security issues in such a constrained environment, where the traditional cryptographic algorithms cannot be used and, thus, discusses various lightweight cryptographic algorithms in detail and present a comparison between these algorithms. Further, the chapter also discusses the power awakening scheme and reference architecture in IoT for constrained device environment with a focus on research challenges, issues, and their solutions.

scholarly journals Characterization and Differentiation between Olive Varieties through Electrical Impedance Spectroscopy, Neural Networks and IoT

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5932
Author(s):  
José Miguel Madueño Luna ◽  
Antonio Madueño Luna ◽  
Rafael E. Hidalgo Fernández
Keyword(s):  
Neural Networks ◽  
Electrical Impedance ◽  
Impedance Measurement ◽  
Food Products ◽  
Electrical Impedance Spectroscopy ◽  
Optimal Harvest ◽  
Working Together ◽  
On Chip ◽  
Olive Varieties ◽  
The Internet Of Things

Electrical impedance has shown itself to be useful in measuring the properties and characteristics of agri-food products: fruit quality, moisture content, the germination capacity in seeds or the frost-resistance of fruit. In the case of olives, it has been used to determine fat content and optimal harvest time. In this paper, a system based on the System on Chip (SoC) AD5933 running a 1024-point discrete Fourier transform (DFT) to return the impedance value as a magnitude and phase and which, working together with two ADG706 analog multiplexers and an external programmable clock based on a synthesized DDS in a FPGA XC3S250E-4VQG100C, allows for the impedance measurement in agri-food products with a frequency sweep from 1 Hz to 100 kHz. This paper demonstrates how electrical impedance is affected by the temperature both in freshly picked olives and in those processed in brine and provides a way to characterize cultivars by making use of only the electrical impedance, neural networks (NN) and the Internet of Things (IoT), allowing information to be collected from the olive samples analyzed both on farms and in factories.

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