Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms

AI-based modeling and data-driven evaluation for smart farming-oriented big data architecture using IoT with energy harvesting capabilities

Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.

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Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

Optimization of Spectrum Utilization Parameters in Cognitive Radio Using Genetic Algorithm

Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks

A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type).  RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED.

https://www.ijcnis.org/index.php/ijcnis/article/download/3718/329

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Energy-efficient and self-organizing Internet of Things networks for soil monitoring in smart farming

The cognitive radio (CR) is proposed as an optimal solution to solve the spectrum underutilization problem. Spectrum sensing is the first enabling stage in cognitive radio. The objective is to obtain the state of the band in interest (free/occupied), then use it temporarily without causing any interference to the primary user (PU). It has been shown that the energy detection is the most chosen method to detect the free bands, in the case when there is no prior information about the primary user. In this paper, we are interested in the implementation of the spectrum sensing (SS) operation based on a real FM signal generated by Raspberry pi 3 card using the FHSS method, and captured under MATLAB-Simulink software by an RTL-SDR hardware. The performance of this approach is evaluated in terms of its ability to detect the transmitted signal.

Raspberry Pi and RTL-SDR for Spectrum Sensing based on FM Real Signals

Cognitive radio (CR) is a form of wireless communication, that provides solution for the spectrum underutilization problems via dynamic spectrum sharing among primary users and secondary users. In cognitive radio network, interference and power consumption must be carefully controlled to maximize the channel usage. Game theory has emerged in the last fifteen years as an effective framework for communications problems, which is used to analyze those problems and help to derive solutions. In this paper, we propose a sufficiently opportunistic utilization for spectrum resources by solving two challenges: interference and power consumption. We analyze the spectrum allocation problem under game theoretical framework and we propose an efficient algorithm to examine the design specification issues regarding the choice of optimal power, optimal speed, and optimal amount of information in a wireless network. Our objectives are to regulate the opportunistic spectrum access, by the secondary users in order to guarantee the protection on licensed users from harmful interference. More especially, to optimize the quality of communication link, transmission levels, and battery life of the wireless devices.

Transmit-Power and Interference Control Algorithm in Cognitive Radio Network Based on Non-cooperative Game Theory

Over the years, several generations of mobile radio have followed one another every decade. The transition from one generation to another is dictated by a concern about performance in terms of mobility, speed, delay, multiplicity of services etc. Nowadays, unlike previous generations, 5G technology is not only interested in the needs of mobile operators (telephony, video download, mobile applications, etc.) but also, and above all, in different applications and diverse uses such as Smart City, safety, transportation, energy, health, industry and security. All these services are unified by 5G technology. This technology manages an Ultra Dense Network (UDN) and therefore consumes a lot of energy. This chapter deals with the issue of Call Admission Control (CAC) in 5G networks, energy efficiency and state of the art in this area. It suggests a CAC modeling algorithm in the case of a New Radio Access (NR 5G). It also addresses the issue of handover and more generally mobility management, power control and interference. All of this is done by considering and ensuring acceptable QoS and QoE, as well as energy efficiency. In this chapter, we focused on the development of an algorithm for improving call admission control essentially based on minimal energy consumption, knowing that there are other parameters on which we can act for better energy efficiency.

Hatim Kharraz Aroussi

Papers

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Energy-efficient and self-organizing Internet of Things networks for soil monitoring in smart farming

Raspberry Pi and RTL-SDR for Spectrum Sensing based on FM Real Signals

Transmit-Power and Interference Control Algorithm in Cognitive Radio Network Based on Non-cooperative Game Theory

5G Energy Efficiency for Smart Cities: A Call Admission Control Proposition