Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.

Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and helping to make decisions. UAVs are a highly consistent technological platform for efficient and cost-effective data collection and event monitoring. The industrial Internet of things (IIoT) sends data from systems that monitor and control the physical world to data processing systems that cloud computing has shown to be important tools for meeting processing requirements. In fog computing, the IoT gateway links different objects to the internet. It can operate as a joint interface for different networks and support different communication protocols. A great deal of effort has been put into developing UAVs and multi-UAV systems. This paper introduces a smart IIoT monitoring and control system based on an unmanned aerial vehicle that uses cloud computing services and exploits fog computing as the bridge between IIoT layers. Its novelty lies in the fact that the UAV is automatically integrated into an industrial control system through an IoT gateway platform, while UAV photos are systematically and instantly computed and analyzed in the cloud. Visual supervision of the plant by drones and cloud services is integrated in real-time into the control loop of the industrial control system. As a proof of concept, the platform was used in a case study in an industrial concrete plant. The results obtained clearly illustrate the feasibility of the proposed platform in providing a reliable and efficient system for UAV remote control to improve product quality and reduce waste. For this, we studied the communication latency between the different IIoT layers in different IoT gateways.

One of the key challenges in wireless sensor networks is the efficient usage of restricted energy resources in battery operated sensor nodes. Clustering remains the most effective routing approach used in WSN. Low Adaptive Clustering Hierarchy (LEACH) protocol is an efficient routing approach which has been widely adopted and enhanced to improve the lifespan of deployed sensor networks. However, latterly, clustering algorithms have shown their limitation in extending the network lifespan. In this paper, we propose a new clustering approach based on a combination of LEACH and MTE protocols. The adoption of multi-hop communication instead of direct communication in cluster filed has optimized the communication in the network. The simulation results illustrate the energy efficiency of the multi-hop cluster based routing approach. The proposed method achieves significant improvement in term of network lifespan and provides enhanced energy performance for wireless sensor networks

This letter proposes an accurate parameter extraction method based on the Levenberg-Marquardt algorithm for a silicon carbide (SiC) power mosfet model. An improved compact model uses this method to study the static behavior of SiC power mosfets according to the temperature and the input voltage. The simulation results obtained with this proposed method fit perfectly the measurements and accurately describe the static behavior of 1200 V Gen 2 SiC mosfets. The extracted model parameters (threshold voltage, saturation region transconductance, and transverse electric field parameter) are evaluated to analyze the temperature impact and understand the physical behavior of 1200 V Gen 2 SiC power mosfet.

This paper proposes to study the design of a novel high-Q fully integrated switched capacitor bandpass filter. This circuit, implemented in CMOS technology, allows a tunable high selectivity over a broad frequency band. The proposed architecture is intended to replace passive surface acoustic wave (SAW) filters in low-cost wireless radio-communication applications. To show the feasibility of the proposed filter a prototype has been fabricated and tested. Measurements show quality factors up to 300, and a tunable center frequency range of 290 MHz [240 - 530 MHz] with a bandwidth tuning.

The quality of the gate-oxide and Oxide/SiC interfaces is one of the crucial issues in the implementation of silicon carbide (SiC) Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) in the industrial power electronic applications. The main goal of this work is to investigate the gate-oxide integrity and to understand the basic phenomena involved on 4H-SiC MOSFET by the mean of Capacitance-Voltage (C-V) characterizations. The paper presents HTRB (High Temperature Reverse Bias) test results on the second and third generations of SiC MOSFETs. The C-V measurements are compared to physical simulation results. The good agreement between 2D numerical simulations and measurements suggests failures related to acceptor interface traps and doping concentration variations.