In space communications, authentication plays an important role as a security technique that verifies and validates satellite identity. Single authentication can be weak and compromised. This project will investigate a novel two-layered multifactor authentication (2L-MFA) accompanied by a decentralized blockchain-based Satellite and IoT environment. The first-level authentication is for IoT devices and considers the secret key, geographical location, and the physically unclonable function (PUF). For lightweight and low latency support, proof-of authentication (PoAh) and elliptic curve Diffie-Hellman are used. Second-level authentication is for Satellite users and is subcategorized into four factor levels, namely identity, password, and the Nonce code (Blockchain). Matrix-based password enrolment in level 1, Elliptic Curve Digital Signature (ECDSA) in level 2, ensure Satellite-level authentication. Fuzzy logic will be deployed to validate the authentication and make the system stronger. The proposed 2L-MFA is evaluated in terms of registration time, login time, authentication time and authentication success rate.

The aim of this project is to identify areas of growth in satellite spectrum monitoring. It will provide advice on what technology capabilities can be developed within Australia and assess the market potential. The main outcome will be a report describing: potential users, the user requirements and a plan to meet those requirements.

The final report will also quantify the expected social and economic benefit to Australia of developing a sovereign space-based spectrum monitoring system.

The report will include a survey of relevant technologies that either already exist or can be developed in Australia. By matching the user requirements with available technologies, the report will provide advice on what research areas need to be focused on.

The aim of the project is to develop enhanced system requirements and resilient architectural designs, waveforms and protocols for selected concepts of operation and use cases, that will improve the efficacy and operation of the current system.

Existing Search and Rescue (SAR) systems suffer from operational and performance limitations that limit their effectiveness.  This project will initially focus on the current internationally used Cospas-Sarsat SAR system, a system constrained by a number of existing specifications and requirements, and then extend to additional applications.  The first phase of this work will develop enhanced system requirements and resilient architectural designs, waveforms and protocols for selected concepts of operation and use cases, that will improve the efficacy and operation of the current system.

This phase will not only look at the Cospas-Sarsat system, but also extend to investigating how emergency management can be impacted and enhanced.  Further, it will study initial architecture options for a prospective new environment being developed by NASA to support the safety of astronauts (called LunaSAR) as part of the Moon to Mars ARTEMIS program.

The objectives of this project are as follows:

1. Define Quantum Enhanced Secure Communications (QComms) system architecture(s) able to provide enhanced, quantum strength security to communication between resource-limited space segment (small satellites) and resource-limited ground users that could be used either as a standalone system for IoT served by small satellites or as a part of larger quantum communication infrastructure, and
2. Define a preliminary technology plan to turn the currently available implementations of selected technologies into space-worthy devices for the LEO environment.

The communications capacities of current satellite technologies are expected to be lagging behind the ever-increasing demands for broadband services. It is foreseen by industry professionals that a rising number of high-throughput satellites must be able to handle a vast amount of data collectively for different purposes such as 5G+, the Internet of Things (IoT), and smart homes and cities as well as defence applications.

Recently, the IEEE 802.15.3d™-2017 Standard for point-to-point (P2P) wireless terahertz links was released to support a wide range of data transmission rates, namely from 1 Gbps to 300 Gbps using channel bandwidths as high as 69 GHz, in the terahertz band. To put into perspective, the nominal data rates are sufficient to support the streaming of tens of uncompressed 4K and 8K videos simultaneously. As such, utilising terahertz communications for satellite networks implies high-quality services for a number of applications.

Therefore, in this project, we plan to investigate the potential opportunities and challenges of applying the terahertz technology to inter/intra-satellite communications within the technical specifications framework defined by the IEEE 802.15.3d™-2017 Standard.

This includes Software Defined Networking (SDN) solutions to centralise the orchestration of the whole network, the control and sharing of the network resources, implementations of intelligent Software Defined Radio (SDR) transponders and user terminals with machine learning technologies, advanced digital multibeam precoding, dynamic control of multi-spot beam patterns and distributed computing tasks based on traffic conditions and QoS requirements. The project will engage relevant researchers from industry and academia to establish which elements are the key factors and bottlenecks in delivering gains from next generation satellites.

IoT is perceived as Internet of Threats by many businesses and consumers. With cyber-attacks becoming more sophisticated day by day, IoT data security, privacy and confidentiality is one of the biggest challenges. Security becomes a greater concern when wireless networks are used for IoT connectivity as they are easily vulnerable to security threats such as eavesdropping, jamming, data modification etc.

Another important issue is to power these IoT edge devices which in some scenarios will be located in extremely remote areas. From the consumer’s perspective, post-rollout maintenance of sensor nodes such as replacing batteries in a short amount of time is extremely undesirable. Hence, there is a need to use machine learning techniques particularly deep reinforcement learning so that the IoT devices/agents can become energy self-sufficient by making optimized decisions based on the learnt policies.

This project will address the these issues jointly by developing novel energy management and data transmissions techniques for energy self-sufficient and secure IoT systems using deep reinforcement learning and a robust PLS approach. Furthermore, we plan to test our designed model on mission critical Smart Grid sensor and monitoring scenario to ensure that the developed solutions are efficient, scalable, and implementable for massive IoT systems.