Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.

The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user’s personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users’ privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.

This paper addresses the control problem of unicycle mobile robot evolving in unknown environments using reactive behaviours. The deformable Virtual Zone (DVZ) principle is used to resolve this problem through defining a safety zone around the vehicle. This approach presents fast control laws that react depending on the presence of an obstacle in the safety zone. Experimental tests are carried out on a wheelchair mobile robot and proved the effectiveness of the studied method.

This paper presents an implementation of robot localization algorithm using FPGA technology. The adopted localization method uses webcam tracking images. This technique has been developed and implemented for the motion of the robot from an initial position towards another desired position. Firstly, we have validated our approach on PC platform using C language and OpenCV library. Secondly, for the autonomous navigation, a mixed HW/SW implementation was been developed using a high performance version of the NIOS processor coupled with a hardware accelerator. Experimental tests on Altera Cyclone III FPGA Starter Kit proved the effectiveness of the proposed architecture.

This paper describes teleoperation systems for remote control of robot and the use of 3D virtual reality. It presents the adopted architecture and describes the experimental implementation through simulation results for mobile robot Khepera.

The paper presents a hardware architecture for implementing a mobile robot localization approach through encoders' measurements and absolute localization using camera tracking images. This technique has been developed and implemented for the motion of the robot from an initial position towards another desired position, taking into account the tradeoff between complexity (reducing computation time) and performance (reducing traveling time). The proposed method is implemented on FPGA (Field Programmable Gate Array) board through mixed hardware / software implementation in order to ensure fluid robot navigation. Experimental tests on Xilinx Virtex 5 ML507 proved the effectiveness of the proposed architecture since we have obtained a reduction of 85 % in execution time compared to initial MATLAB version developed on a PC platform.