Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.Comment: 19,

Optimizing Onion Crop Management: A Smart Agriculture Framework with IoT Sensors and Cloud Technology

Smart agriculture, fueled by the integration of Internet of Things (IoT) and cloud technology, has revolutionized modern farming practices. In this study, we propose a step-by-step framework for optimizing onion crop management using IoT sensors and cloud-based solutions. By deploying various IoT sensors, including soil moisture, temperature, humidity, and aerial drones, essential data about the onion crops is collected and transmitted to a central data hub. Optional edge computing devices enable real-time data processing, minimizing latency and bandwidth usage.The collected data is aggregated and stored securely on a cloud platform, which facilitates advanced data analysis and insights. Utilizing machine learning algorithms, the cloud platform can provide valuable information about the onion's growth patterns, health status, and growth trajectory. Farmers can easily access this information through a user-friendly dashboard, accessible via web or mobile applications.Automated alerts and notifications enable timely intervention, notifying farmers about any deviations from optimal conditions, such as low moisture levels or pest infestations. The system's predictive capabilities allow for precision irrigation and nutrient management, optimizing resource usage and improving crop health.The accumulated historical data offers a wealth of information, enabling the identification of trends and the prediction of growth patterns for future planting seasons. Throughout this process, data security and privacy measures are prioritized, with encrypted data transmission and storage to protect farmers' sensitive information.The integration of IoT and cloud technology provides an efficient and effective solution for monitoring onion crop growth. The proposed framework offers farmers valuable insights, improves productivity, and promotes sustainable agricultural practices

The Implications of IoT in the Modern Healthcare Industry post COVID-19

The healthcare industry has recently seen a massive surge in the use of the Internet of Things (IoT) during and after the COVID-19 pandemic. IoT’s main objective is to provide people with the necessities in these uncertain times. During the pandemic, the availability of IoT-based healthcare systems is crucial. Using IoT, healthcare systems are becoming more individualized, allowing for more precise patient diagnosis, treatment, and monitoring. Since the beginning of the epidemic, many researchers have worked tirelessly to find solutions to this global problem, and IoT technology has the potential to revamp the current system completely. Over 6 million people had lost their lives by the time this document was produced due to the ongoing COVID-19 epidemic. Many lives could have been saved. The problem today is that when people are too sick, they cannot call or contact an ambulance or get safely to the hospital. With new technology, perhaps a button or programming into a device, people in need can press a button on their phone or call out into a voice-enabled device to contact the ambulance or other emergency contacts that they might have. The research has found that if significant companies take this seriously, it could be a remarkable idea that could save many lives

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks

Risk driven models & security framework for drone operation in GNSS-denied environments

Flying machines in the air without human inhabitation has moved from abstracts to reality and the concept of unmanned aerial vehicles continues to evolve. Drones are popularly known to use GPS and other forms of GNSS for navigation, but this has unfortunately opened them up to spoofing and other forms of cybersecurity threats. The use of computer vision to find location through pre-stored satellite images has become a suggested solution but this gives rise to security challenges in the form of spoofing, tampering, denial of service and other forms of attacks. These security challenges are reviewed with appropriate requirements recommended. This research uses the STRIDE threat analysis model to analyse threats in drone operation in GNSS-denied environment. Other threat models were considered including DREAD and PASTA, but STRIDE is chosen because of its suitability and the complementary ability it serves to other analytical methods used in this work. Research work is taken further to divide the drone system into units based in similarities in functions and architecture. They are then subjected to Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA). The STRIDE threat model is used as base events for the FTA and an FMEA is conducted based on adaptations from IEC 62443-1-1, Network and System Security- Terminology, concepts, and models and IEC 62443-3-2, security risk assessment for system design. The FTA and FMEA are widely known for functional safety purposes but there is a divergent use for the tools where we consider cybersecurity vulnerabilities specifically, instead of faults. The IEC 62443 series has become synonymous with Industrial Automation and Control Systems. However, inspiration is drawn from that series for this work because, drones, as much as any technological gadget in play recently, falls under a growing umbrella of quickly evolving devices, known as Internet of Things (IoT). These IoT devices can be principally considered as part of Industrial Automation and Control Systems. Results from the analysis are used to recommend security standards & requirements that can be applied in drone operation in GNSS-denied environments. The framework recommended in this research is consistent with IEC 62443-3-3, System security requirements and security levels and has the following categorization from IEC 62443-1-1, identification, and authentication control, use control, system integrity, data confidentiality, restricted data flow, timely response to events and resource availability. The recommended framework is applicable and relevant to military, private and commercial drone deployment because the framework can be adapted and further tweaked to suit the context which it is intended for. Application of this framework in drone operation in GNSS denied environment will greatly improve upon the cyber resilience of the drone network system