Abstract Satellites have revolutionised the way that the planet’s environment is monitored via a unique perspective from above. Indeed, environmental monitoring is crucial for understanding and addressing the complex challenges facing the planet, which helps in decision-making and ensuring a sustainable future. Thus, this work aims to develop an intelligent model that includes artificial neural networks and deep learning approaches that are coupled with Blockchain capabilities for secure environmental monitoring using a CubeSat. The CubeSat, which is a small satellite platform, is equipped with a designed communication payload, including an adaptive Multiple-Input Multiple-Output antenna as well as an High Definition (HD) camera for better connectivity and precision aerial imaging. The proposed solution is simulated, tested, and validated from four scenarios, namely, water detection, tree counting and vegetation assessment, and oil spill detection. Ensuring the security and integrity of the data transmitted between the CubeSat and the ground station is of paramount importance; this is where Blockchain technology comes into play. The obtained results show high accuracy in monitoring environmental surfaces like water, trees, and coasts in an effective and rapid deployment fashion. Also, performance indicators of the Blockchain ensure data integrity and retrieval efficiency. Combining these technologies provides a valuable contribution to environmental monitoring.

Skin cancer is most likely to disseminate to other parts of the human body if it is not detected and treated in a timely manner. Consequently, early detection is crucial for prompt and effective treatment. The evident similarity between skin conditions has complicated medical diagnosis. Although melanoma is the most well-known form of skin cancer, other diseases have caused a significant number of deaths in recent years. Recent advances in computerized methods for mak- ing these diagnoses have made them more accurate and quicker, which is very encouraging. The absence of sizable datasets is one of the greatest obstacles to developing a reliable automatic classification system. For this purpose, the ISIC Skin Lesion Classification Challenge provided 25331 images from eight distinct classifications. This paper presents a CNN-based deep learning model for cuta- neous cancer detection. Its primary objective is to categorize skin lesions based on Dermoscope images. With a sensitivity of 55.32%, a specificity of 88.92%, an accuracy of 90.18%, a precision of 91.01%, a dice accuracy of 90.80%, and a jac- card accuracy of 83.37%, our method has yielded results that are significantly superior to those of existing methods. The proposed technique is significantly superior to the existing methods for recognizing and categorizing skin diseases.

Abstract Energy consumption remains an open challenge in aerial systems such as CubeSats and therefore optimization of its allocation is a top priority for maximizing operational capacity. Our research review reveals a plethora of approaches for optimization of energy allocation and all achieving varying degrees of success and not without any compromises. In this paper, we exploit the use of serious gaming in a novel energy allocation algorithm that aims at minimizing energy consumption to maximize the utilities of both CubeSats and terrestrial sensors. To demonstrate this, we use Stackelberg for serious gaming and standalone topology for CubeSat configuration. The experimental results show that the use of a Stackelberg game approach for optimization has led to reduction in the required transmission energy in sensors, an improved link performance between the CubeSat and ground sensors, and an increase in network lifetime and performance without resorting into sensor power enhancements or other external power sources. The overall average operational capacity improvement predictions range between 22 to 27% across all performance indicators of energy efficiency across RF chains of link budgets.

Abstract The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient$R^2$to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength.

The research work presented in this paper has been funded by a national research project whose aims are to enable an Unmanned Aerial Vehicle (UAV) to fly autonomously with the use of a Digital Elevation Model (DEM) of the target area and to detect terrain changes with the use of a 3D Structure Change Detection Model (3D SCDM). A Convolutional Neural Network (CNN) works with both models in training the UAV in autonomous flying and in detecting terrain changes. The usability of such an autonomous flying IoT is demonstrated through its deployment in the search for water resources in areas where a satellite would not normally be able to retrieve images, e.g., inside gorges, ravines, or caves. Our experiment results show that it can detect water flows by considering different surface shapes such as standing water polygons, watersheds, water channel incisions, and watershed delineations with a 99.6% level of accuracy.

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource‐constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy‐Preserving Data Aggregation Scheme with authentication for IoT‐Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

This paper develops a machine leaning framework that evolves an optimal propagation model for the last mile with Low Altitude Platforms from existing propagation models. Existing propagation models reviewed exhibit both advantages and shortcomings in relation to a set of factors that affect performance across different terrains, i.e. path loss, elevation angle, altitude, coverage, power consumption , operational frequency, interference, and antenna type. A comparison of the predictions between the optimized and the existing models in relation to above set of factors reveals significant improvements are achieved with the optimal model.