## 8 Recent Trends, Observations, and Future Research Challenges
### Research Trends and Observations  
IoRT presents unique challenges for connectivity and data gathering and processing, demanding innovative solutions that are cost-effective, scalable, and efficient. To address these challenges, we have identified key research trends that are shaping the future of IoRT. This subsection delves into promising technologies like CubeSats, NOMA, machine learning, and MEC. Examining these technologies provides valuable insights into how IoRT can overcome its hurdles and realize its full potential for revolutionizing remote IoT applications. * _CubeSats: A Cost-Effective and Scalable Solution for Remote IoT Connectivity_ Miniaturized satellites known as CubeSats are rapidly gaining traction as a cost-optimized solution for establishing communication links with geographically dispersed IoRT systems. Their miniature size and inherently low energy requirements make them ideal for constructing network constellations tailored to specific mission profiles and optimized for minimized energy expenditure. Furthermore, CubeSats exhibit exceptional scalability and adaptability, enabling the deployment of dense constellations with extensive coverage areas, particularly in regions beyond the reach of terrestrial infrastructure. This capability is further augmented by their proficiency in forming dynamic collaborative networks, which maximizes resource utilization and flexibility. Furthermore, CubeSats play a crucial role as excellent research platforms, promoting progress in compact electronics, energy-conserving techniques, and cost-efficient deployment strategies. These developments will have an immediate effect on the connection of IoRT systems because they lay the groundwork for future global connectivity that will be both more affordable and more accessible. * _Softwarization Driving Agile and Scalable Remote IoT Networks_ A key research path for NTN-based IoRT systems is centered around the softwarization of network infrastructure. The integration of SDN and NFV offers a flexible and dynamic architecture to achieve cost-efficient deployment in NTNs. Researchers are exploring the potential of software-defined radio for cost-effective gateway implementation, addressing interoperability challenges, and enhancing adaptability in diverse network environments. Additionally, the adoption of SLA-based automatic network slicing frameworks can also be noted as an emerging trend, enabling the efficient allocation of resources and customization of network slices tailored to the specific requirements of IoT devices. These trends collectively signify a shift towards more agile, cost-effective, and scalable solutions for the evolving landscape of remote IoT-based NTNs. * _NOMA: A Key Enabler for Scalable and Efficient IoRT Connectivity_ NOMA enhances spectral efficiency by enabling more devices to share the available frequency spectrum, thereby increasing capacity. This directly translates to increased \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline
[135] & HAP-UAV & A 6C clustered-NOMA HAP-UAV system architecture is proposed, and a UAV 3D location planning and resource allocation scheme is developed that enhances system throughput. \\ \hline
[136] & LAP UAV & A trajectory optimization and subslot allocation scheme is proposed for UAV-based clustered NOMA wireless-powered communication systems to improve the uplink average sum rate for IoT terminals. \\ \hline \hline \end{tabular}
\end{table} 
Table 15: IoRT systems based on aerial NTN networks: **enhanced throughput/transmission rate**. network capacity, facilitating enhanced connectivity for remote IoT devices communicating with satellites and UAVs. NOMA, by leveraging a dynamic scheduling scheme, can dynamically adjust resource allocation based on the varying needs of IoT devices. This allows the system to handle a mix of devices with different data rates, latency sensitivities, and QoS demands by dynamically adapting the transmission parameters for each device.