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Satellite communication networks are seeing a significant surge in traffic requirements. Nevertheless, the rise in traffic requirements is inconsistent across the service region because of the unequal distribution of consumers and fluctuations in traffic requirements during the day. Variable payload designs solve this issue by enabling the uneven allocation of payload resources to match the traffic requirement of each beam. Optimization-based Radio Resource Management (ORRM) has its high substantial efficiency demonstrated computational difficulty hinders its real-world deployment. This work explores the structure, execution, and uses of Machine Learning (ML) for resource allocation in satellite systems. The primary emphasis is on two systems: one that offers power, capacity, and beamwidth adaptability and provides temporal flexibility via beam hopping. The research examines and contrasts several ML methods suggested for these structures. The research determines whether training must be done online or offline depending on the features and needs of each ML method. The study analyzes the most suitable system structure and the pros and cons of each strategy.

The construction of smart cities has been progressing quickly due to advancements in 5G and Internet of Things (IoT) technology. Smart transportation systems and smart medical ideas are also being explored alongside smart city initiatives. Integrating the two mentioned technologies, a smart navigational method for ambulances is being developed. When severe patients arrive via ambulance, significant time is wasted in relaying data, and the hospital needs to gain prior knowledge of the patient's condition. An efficient system called the IoT-based Wireless Patient Tracking Method (IoT-WPTM) is proposed to diagnose patients quickly by recording vital metrics such as heartbeat, temperature, and breathing rate using detectors. These variables are transmitted wirelessly to the healthcare before the ambulance is dispatched, allowing doctors to begin medical care promptly. If necessary, the patient monitors the ambulance's position to anticipate arrival and get immediate directions to the closest hospital. The user carries the gadget and is also placed in the ambulance. The gadget allows IoT devices and Android apps to enhance user interaction and streamline information delivery. IoT-WPTM can be surpassed with ease and in a more effective manner for smart medical surveillance systems.

The Internet of Things (IoT) expansion has introduced a new era of interconnectedness and creativity inside households. Various independent gadgets are now controlled from a distance, enhancing efficiency and organization. This results in increased security risks. Competing vendors rapidly develop and release novel connected devices, often paying attention to security concerns. As a result, there is a growing number of assaults against smart gadgets, posing risks to users' privacy and physical safety. The many technologies used in IoT complicate efforts to provide security measures for smart devices. Most intrusion detection methods created for such platforms rely on monitoring network activities. On multiple platforms, intrusions are challenging to detect accurately and consistently via network traces. This research provides a Multi-Stage Intrusion Detection System (MS-IDS) for intrusion detection that operates on the host level. The study employs personal space and kernel space data and Machine Learning (ML) methods to identify different types of intrusions in electronic devices. The proposed MS-IDS utilizes tracing methods that automatically record device activity, convert this data into numerical arrays to train multiple ML methods, and trigger warnings upon detecting an incursion. The research used several ML methods to enhance the ability to see with little impact on the monitoring devices. The study evaluated the MS-IDS approach in a practical home automation system under genuine security risks.