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In this work, we present a resource allocation scheme for managing trade-offs between total throughput maximisation and system fairness in a non-orthogonal multiple access (NOMA) system for 5G networks. Our proposed approach is designed to improve throughput and fairness as performance metrics of NOMA in 5G networks. We apply integer linear programming for user pairing and adopt particle swarm optimisation as the power allocation scheme for reducing resource allocation complexity. To formulate the multi-objective problem, we use scalarisation of multi-objective optimisation, which exhibits flexibility in assigning different weights to a single objective—in the case of this study, either sum rate or fairness. Moreover, the problem is formulated with a penalty function to prevent optimisation violating the constraints of the optimisation function. Simulation results show that the proposed model outperformed the conventional approach by at least 17% in terms of throughput maximisation and fairness rate.

Even after two years since the declaration of the new virus Coronavirus Disease 19 , the reported cases are still considerably high in many countries, including Malaysia. The health authorities cannot monitor the health condition and track the location of every homemonitored patient at once due to many confirmed cases in a day. In order to overcome the shortage of manpower, an Internet of Things (IoT)-based self-quarantine system with Radio Frequency Identification (RFID) and Global Positioning System (GPS) tracking is proposed in this paper to monitor the health conditions of the Covid-19 patients and track their real-time location via mobile application. Biomedical sensors are used to measure health conditions such as temperature, pulse oximetry, and heart-rate monitor. In addition, the RFID readers are used to detect patients that intend to leave the quarantine area, and the GPS modules are used to track their actual geometrical location so that the authorities can take further action. The real-time data is automatically pushed to the cloud server for the authorities to remotely view the patient's health condition and location on the Google map using smart devices. Finally, a hardware prototype and a mobile application have been successfully developed in this project. The system is able to display the temperature, heartbeats, and blood oxygen saturation properly on a liquid crystal display (LCD) screen. All these measured values, together with the information from RFID detection and GPS location tracking, can be viewed on a smartphone.

The recent surge in adoption of the Internet of Things (IoT) has accelerated integration and Internet access beyond smart devices, which in turn has made the Internet more and more pervasive in our daily lives and IoT devices open up endless new possibilities and simplify lives. Unfortunately, the current system able to spy on users of unprotected IoT systems. Thus, the predictive models taught by machine learning algorithms is demanded and have great potential to alleviate some of these problems as the looming crisis deepens. In this work, a lightweight encryption technique (SIT) to secure an IoT is proposed. It is a 64bit block cypher that encrypts data with a 64-bit key. The proposed algorithm's architecture is a hybrid type and delivers significant security in just five encryption cycles in simulations. The technique is implemented in hardware on a low-cost 8bit microcontroller. The impact of an intense attack and buffer size are discussed in this work to analyze the Distributed Denial-of-Service (DDoS) attacks on a server. Finally, the proposed mitigation approached shows a better performance according to the energy consumption level during the attacks and the mitigation applied. Thus, the DDoS attacks successfully being reduced.

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