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.

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions.

In almost-majority of IoT (Internet of Things) networks; the wireless sensor nodes are small and low-cost portable devices. Besides, they are typically battery-powered with a finite lifespan duration. In most emerged applications, these nodes are required to remain for the maximum time possible with a standalone performance for large-scale coverage. Therefore, it is important to understand the node's processing before real implementation of any massive long-range IoT networks to assign accurate settings for each node on its positions along with estimating the approximate mean duration of all networks. LoRa (Long Range) is one of the recently emerged Low Power Wide Area Networks IoT networks that is knowing high interest in various applications. In this paper, we propose a Markov Decision Process-based model that aims to estimate the lifespan of LoRa class A end nodes regarding the efficiency in resources allocation and transmission parameter settings. The proposed model has been tested and showed interesting results on the accuracy of lifetime estimation and prediction under different scenarios.

Recently, various communication technologies have emerged to enable potential wireless connectivity between objects and allow data access for both short and wide-area communications. Among those, the low-power wide-area network (LPWAN) technologies that offer new types of services by enabling wireless objects to communicate efficiently through large scale networks and over long-range distances. Although LPWAN technologies are rising in the market, LoRa has shown tremendous attention. Lora is a long-range, low-power, wireless communication solution featured as an affordable infrastructure solution to tune the rapid growth of the Internet of Things (IoT) market. In this work, we have investigated the LoRa promising performances under channel medium impairments different physical layer parameters selection that affect the data rate, reliability, and range of communication among Coding Rate (CR), Bandwidth (BW), Spreading Factors (SF). The results proved that higher spreading factors offer better resistance to interference at the cost of increased airtime, and short coding rates offer significant performance at the cost of reduced packets size.

Robot vision, enabled by deep learning breakthroughs, is gaining momentum in the industry 4.0 digitization process. The present investigation describes a robotic grasp detection application that makes use of a two-finger gripper and an RGB-D camera linked to a collaborative robot. The visual recognition system, which is integrated with edge computing units, conducts image recognition for faulty items and calculates the position of the robot arm. Identifying deformities in object photos, training and testing the images with a modified version of the You Only Look Once (YOLO) method, and establishing defect borders are all part of the process. Signals are subsequently sent to the robotic manipulator to remove the faulty components. The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy.