Energy efficiency is a critical concern in IEEE 802.11ah (Wi-Fi HaLow) networks, particularly in Internet of Things (IoT) scenarios where both stations (STAs) and the access point (AP) operate under stringent power constraints. While most existing research has focused on optimizing energy usage at the STA side, the energy consumption of the AP has received comparatively little attention. To address this gap, we propose a model-driven MAC-layer optimization approach based on an absorbing Markov chain that characterizes the transmission dynamic and contention behavior of STAs in non-saturated networks. Leveraging analytical insights derived from this model, we optimize contention window settings to reduce the time the AP spends in energy-intensive states, such as idle listening and collision handling. Real-device experiments conducted with 16 commercial IEEE 802.11ah STAs demonstrate that the proposed approach achieves up to a 20.4% reduction in AP energy consumption compared to the default configuration. Overall, the proposed approach provides a simple and effective way to reduce AP energy consumption in energy-constrained IoT deployments.

As the number of IoT (Internet of Things) devices continues to grow at an exceptional rate, so does the variety of use cases and operating environments. IoT now plays a crucial role in areas including smart cities, medicine and smart agriculture, where environments vary to include built environments, forest, paddocks and many more. This research examines how Wi-Fi HaLow can be optimised to support the varying environments and a wide variety of applications. Through examining data from performance evaluation testing conducted in varying environments, a framework has been developed. The framework takes inputs relating to the operating environment and application to produce configuration recommendations relating to ideal channel width, MCS (Modulation and Coding Scheme), GI (Guard Interval), antenna selection and distance between communicating devices to provide the optimal performance to support the given use case. The application of the framework is then demonstrated when applied to three various scenarios. This research demonstrates that through the configuration of a number of parameters, Wi-Fi HaLow is a versatile network technology able to support a broad range of IoT use cases.

Agriculture Technology (AgTech) integrates various technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, the Internet of Things (IoT), cloud and edge computing are applied to provide capabilities for collecting, transmitting, storing, processing, and analyzing agricultural data. Large amounts of data are gathered from multiple sources and processed in real time, enabling more effective decision-making. Data can be used to automate processes, resulting in savings on farm labor. To obtain the benefits of AgTech, data trustworthiness is essential and cyber resilience is required. Security flaws may result in farming equipment and processes being interrupted or inoperable, with significant revenue and capital losses. It is important for farmers to identify and respond to cyber incidents. Several existing works have attempted to classify AgTech technologies. However, most existing classifications are based on a limited set of characteristics and only focus on communication technologies in use or provide generic adversarial scenarios. There are real risks to data trustworthiness that impact on digital agriculture, and many of these risks are yet to be addressed. It is critical to understand the building blocks and possible components to be considered in a future security framework and their capabilities for satisfying the data confidentiality, quality, authenticity, and integrity requirements. This paper proposes a taxonomy to enable a consistent means of classifying the technologies that form the backbone of AgTech and outline the building blocks of data trustworthiness. This taxonomy consists of seven main criteria that recognize the technologies and systems (used to enable the movement of data at different stages) in terms of how it operates, its features, its benefits, and its limitations. Understanding the taxonomy enables effective implementation of anomaly detection and cryptographic methods to ensure data trustworthiness. Efficient AgTech with no security represents a serious risk to long-term sustainable smart farming and food security. Further, this paper outlines the integration of post-quantum cryptography with AgTech. The usefulness of the proposed taxonomy is demonstrated using two different types of case study related to the design and implementation of a modular security framework in real-world scenarios. The paper also identifies and discusses other important factors that impact data trustworthiness.

Mitigating cyberattacks on IoT networks is critical and remains a significant challenge, as such attacks can cause severe damage to the network systems and services. Moreover, the large volume of devices in IoT networks presents another challenge in managing security to reduce the risk of attacks. The Manufacturer Usage Description (MUD) is a standard for limiting attack risks on IoT networks. However, MUD has limitations, as it relies solely on pre-defined access control list (ACL) rules to allow permitted traffic and block unknown traffic. This can lead to false-negative filtering, where malicious traffic may still be allowed by MUD, compromising an entire IoT network. This study presents the implementation of a network behaviour analysis (NBA) system for DoS/DDoS attack detection in MUD-based IoT networks. We designed a set of algorithms to enhance the effectiveness of malicious traffic detection compared to using MUD alone. The NBA system groups related traffic and detects a variety of DoS/DDoS attacks that utilise TCP and MQTT protocols. Our evaluation demonstrates that the NBA system achieves high detection accuracy, effectively identifying attacks that MUD alone would not be able to detect, thereby enhancing the effectiveness of attack detection in MUD-based IoT networks.

Wi-Fi HaLow (IEEE 802.11ah) has emerged as a promising solution which can support Internet of Things (IoT) applications where energy efficiency and extended coverage are important. A key feature of Wi-Fi HaLow is the Target Wake Time (TWT) mechanism, which allows devices to schedule wake-up times, significantly reducing IDLE listening and energy consumption. However, there is currently no energy consumption model, leaving a gap in calculating how much energy a device actually consumes in a real network. This study aims to bridge this gap by developing a forecast model to accurately predict the energy consumption of devices with TWT enabled. The proposed model is then validated through experimental measurements using real Wi-Fi HaLow-compatible devices, ensuring an accurate representation of practical energy consumption. This research provides empirical insights and recommendations for optimizing network configurations in battery-constrained environments. In particular, the proposed energy consumption model can assist businesses in accurately estimating and managing energy usage, which is essential for cost-effective planning and improving operational efficiency in real-world IoT deployments.

Attacks launched from IoT networks can cause significant damage to critical network systems and services. IoT networks may contain a large volume of devices. Protecting these devices from being abused to launch traffic amplification attacks is critical. The manufacturer usage description (MUD) architecture uses pre-defined stateless access control rules to allow or block specific network traffic without stateful communication inspection. This can lead to false negative filtering of malicious traffic, as the MUD architecture does not include the monitoring of communication states to determine which connections to allow through. This study presents a novel solution, the enhanced profiling assurance (EPA) architecture. It incorporates both stateless and stateful communication inspection, a unique approach that enhances the detection effectiveness of the MUD architecture. EPA contains layered intrusion detection and prevention systems to monitor stateful and stateless communication. It adopts three-way decision theory with three outcomes: allow, deny, and uncertain. Packets that are marked as uncertain must be continuously monitored to determine access permission. Our analysis, conducted with two network scenarios, demonstrates the superiority of the EPA over the MUD architecture in detecting malicious activities.

Radio frequency fingerprint identification (RFFI) is becoming increasingly popular, especially in applications with constrained power, such as the Internet of Things (IoT). Due to subtle manufacturing variations, wireless devices have unique radio frequency fingerprints (RFFs). These RFFs can be used with pattern recognition algorithms to classify wireless devices. However, Implementing reliable RFFI in time-varying channels is challenging because RFFs are often distorted by channel effects, reducing the classification accuracy. This paper introduces a new channel-robust RFF, and leverages transfer learning to enhance RFFI in the time-varying channels. Experimental results show that the proposed RFFI system achieved an average classification accuracy improvement of 33.3 environments. This paper also analyzes the security of the proposed RFFI system to address the security flaw in formalized impersonation attacks. Since RFF collection is being carried out in uncontrolled deployment environments, RFFI systems can be targeted with false RFFs sent by rogue devices. The resulting classifiers may classify the rogue devices as legitimate, effectively replacing their true identities. To defend against impersonation attacks, a novel keyless countermeasure is proposed, which exploits the intrinsic output of the softmax function after classifier training without sacrificing the lightweight nature of RFFI. Experimental results demonstrate an average increase of 0.3 in the area under the receiver operating characteristic curve (AUC), with a 40.0 improvement in attack detection rate in indoor and outdoor environments.

With the deepening of knowledge base research and application, question answering over knowledge base, also called KBQA, has recently received more and more attention from researchers. Most previous KBQA models focus on mapping the input query and the fact in KBs into an embedding format. Then the similarity between the query vector and the fact vector is computed eventually. Based on the similarity, each query can obtain an answer representing a tuple (subject, predicate, object) from the KBs. However, the information about each word in the input question will lose inevitably during the process. To retain as much original information as possible, we introduce an attention-based recurrent neural network model with interactive similarity matrixes. It can extract more comprehensive information from the hierarchical structure of words among queries and tuples stored in the knowledge base. This work makes three main contributions: (1) A neural network-based question-answering model for the knowledge base is proposed to handle single relation questions. (2) An attentive module is designed to obtain information from multiple aspects to represent queries and data, which contributes to avoiding losing potentially valuable information. (3) Similarity matrixes are introduced to obtain the interaction information between queries and data from the knowledge base. Experimental results show that our proposed model performs better on simple questions than state-of-the-art in several effectiveness measures.

IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology.

Radio frequency fingerprint identification (RFFI) is a lightweight device authentication technique particularly desirable for power-constrained devices, e.g., the Internet of things (IoT) devices. Similar to biometric fingerprinting, RFFI exploits the intrinsic and unique hardware impairments resulting from manufacturing, such as power amplifier (PA) nonlinearity, to develop methods for device detection and classification. Due to the nature of wireless transmission, received signals are volatile when communication environments change. The resulting radio frequency fingerprints (RFFs) are distorted, leading to low device detection and classification accuracy. We propose a PA nonlinearity quotient and transfer learning classifier to design the environment-robust RFFI method. Firstly, we formalized and demonstrated that the PA nonlinearity quotient is independent of environmental changes. Secondly, we implemented transfer learning on a base classifier generated by data collected in an anechoic chamber, further improving device authentication and reducing disk and memory storage requirements. Extensive experiments, including indoor and outdoor settings, were carried out using LoRa devices. It is corroborated that the proposed PA nonlinearity quotient and transfer learning classifier significantly improved device detection and device classification accuracy. For example, the classification accuracy was improved by 33.3% and 34.5% under indoor and outdoor settings, respectively, compared to conventional deep learning and spectrogram-based classifiers.

The Internet of Things (IoT) smart grid enables many benefits to both customers and energy generators, such as improved outage visibility, billing, and cost reduction. It allows more efficient energy use through improved access to real-time data that supports customers in reducing their energy usage and improving environmental outcomes. Integrating IoT-based data networks into the grid brings these benefits and many more. However, security and performance challenges are introduced. With the plethora of current and emerging technologies, suitable technologies must be used in each network segment that provide a sufficient level of network performance. With the introduction of data networks to the grid, we must also consider what additional threats are introduced regarding network security. This work provides essential background information on the residential smart grid. Security risks and attacks that threaten the IoT smart grid are then identified. A discussion on security challenges and future research directions are presented. A review and discussion of relevant modern IoT transmission technologies covering their benefits, key performance metrics, and their appropriate place within the IoT-based smart grid is then presented.

The IoT-based smart grid provides many benefits to both energy consumers and energy producers, such as advanced metering functions, improved reliability, and management. Increasingly with the rise of smart homes and smart cities, security is a concern, as data networks increasingly run parallel to power networks. Ensuring good security practices are implemented in the smart home is critical. This study proposes a Home Area Network architecture design, and secure ChaCha20-Poly1305 Authenticated Encryption with Associated Data (AEAD) based authentication scheme, based on the recent LoRa 2.4 GHz technology; a robust and highly tunable transmission technology. This results in a network that balances performance considerations, whilst providing confidentiality, integrity and authenticity through the use of symmetric key-based authentication and encryption scheme. A performance analysis is conducted using a practical test bench to determine the impact that the proposed security mechanisms have on the LoRa network. The secure architecture proposed by this study has a minimal impact on the transmission time of a packet compared to a network with no security measures. This additional latency does not negatively impact on the smart home user in terms of network performance.

The auditability of telephone call records plays an essential governance role in the electricity industry in Australia as non-compliance with the Australian National Electricity Rules can lead to financial charges and result in developing a poor reputation. The existing telephone call recording processes using manual logbook entries or a recording system without verification and auditing capabilities are labour-intensive and prone to human error. This study is motivated to address this real-world problem by designing a system that streamlines telephone call audit processes. This can be verified with digital technologies to meet security requirements as well as legal requirements stipulated by the Australian National Electricity Rules. In meeting security and legal compliance requirements of the Australian National Electricity Rules, this study develops a novel approach using the Clark-Wilson Integrity Model and blockchain technology for an automatic telephone call audit system with security provisions to prevent unauthorized access to and manipulation of telephone call records nationally. Although the application of blockchain has generated great interest in other areas, few studies have been conducted on its application to auditing. This study uses the Clark-Wilson Integrity Model to verify metadata records' integrity at the systems where metadata are generated. The proposed architectural design not only enhances data integrity and confidentiality but also enables the automatic execution of telephone call audit processes for auditors. The auditing system we propose presents a higher level of security compared to the existing system.

Energy market trading systems are undergoing rapid transformation due to an increasing demand for renewable energy sources to be integrated into the power grid, coupled with the dynamic and evolving needs of future energy customers. In the current energy trading system, which is based on mega power generation, energy is traded by insecure means of communication based on mutual trust. In addition, electricity from both renewable and non-renewable sources is mixed in the grid, impeding customers' ability to definitively track the source of energy dispatched to their premises. Although blockchain technology has been studied for energy trading on a peer-to-peer microgrid trading, to our knowledge none of the previous work focused on using blockchain for trading energy in a national wholesale energy market in macrogrid. In this paper, we address security architectures required of the energy market trading system in an Australian context, we propose a cryptocurrency token-based structure and a smart contract that provides data confidentiality that verifies and audits transactional records. The proposed trading system architecture not only enhances overall system security but provides additional capabilities in the operation of the scheme so that sources of energy dispatched to customer premises are known. The energy market trading system we propose also presents higher security compared to existing trading systems.

With predictions suggesting there will be 18 billion Internet of Things (IoT) devices live by 2022, performance of these low powered devices, as well as security is of utmost importance.Managing security and performance is a balancing act.Achieving this balance will always continue to be a challenge.This research presents two main contributions to this area.The first contribution is a framework to measure cryptographic performance of IoT devices.The areas of measurement are power consumption, time cost, energy cost, random access memory (RAM) usage and flash usage.The second contribution is an insightful comparison of the performance of the ATmega328, STM32F103C8T6 and ESP8266 low powered microcontroller devices.Experiments were conducted on these devices running various cryptographic operations.The measured operations are from three encryption algorithms: Advanced Encryption Standard (AES), ChaCha and Acorn.The proposed methods from this research are real-world in nature rather than simulated, and can be used by others wishing to conduct their own IoT performance testing.The results show that the ATmega328 has the lowest overall power consumption.The ESP8266 was generally the fastest performing device.ChaCha outperformed AES in both time cost and energy cost.Both algorithms outperformed Acorn in these metrics.The STM32F103C8T6 device displayed the best overall energy cost, while still performing well in terms of time.The results from the experiments conducted in this study can be used by network designers, developers and others to make appropriate decisions in IoT deployments with regards to balancing performance and security.

As moving towards cloud-based real-time services, we are witnessing the shift from a technology-driven services to service provisioning paradigms, that is, from Quality of Service (QoS) to Quality of Experience (QoE). User experience and satisfaction are placed at the epicenter of the system design. QoE is a measurement of user experience on the provided service by a system. Often QoE is measured by subjective mechanisms, such as user experience surveys and mean opinion scores (MOS) methods, which can be a costly and time-consuming process. Using an adequate QoE model to measure user experience of perceived quality is cost-effective, compared to using time-consuming subjective surveys. Applying an adequate QoE model to assess user experience is advantageous for cloud-based real-time services such as voice and video. This study uses a formula-based QoE estimation model to estimate and predict QoE prior to the deployment or during the planning stage of the system service. This study investigates a real-world scenario of a company that recently moved to its premises-based real-time trading communication system (TCS) to a public cloud. A simulation system using OPNET is also implemented to illustrate the usefulness of the model. Our result shows that the effect of delay on the users experience of the service provided by the cloud-based TCS is minimum comparing to packet loss rate (PLR) and Jitter. However, it has been observed that the overhead of the different security settings of the TCS system had no major negative impact to the user experience. The proposed model can be used as a QoE control mechanism and network optimization for cloud-based TCS services.

The purpose of this study is twofold. First, this study proposes a cost-effective LoRa gateway to improve bandwidth utilisation to achieve optimal network throughput for LoRa networks from a hardware design perspective. Secondly, this study creates a design for adaptive and autonomous algorithm to allocate bandwidth while meeting dynamic throughput demands from a network management perspective. This study conducted actual experiments to evaluate the network throughput capacity of a LoRa gateway based on the SemTech™ SX1301 transceiver chipset (SX1301 gateway). This study also addresses the limitations of the SX1301 gateway by proposing a gateway to improve bandwidth utilisation to increase network throughput. Knowing different packet sizes have an impact on network throughput, this study also set up a series of actual experiments related to the network throughput of the proposed gateway with 40 test combinations based on four different packet sizes and ten configurable bandwidth options. These test results were gathered and analysed to establish the throughput threshold for four different packet size ranges, and then to design an adaptive and autonomous algorithm for dynamic bandwidth allocation without human intervention. Based on the theoretical throughput capacity, the proposed gateway has an average improvement of throughput capacity of 57.73%, compared to the SX1301 gateway. The significance of the proposed adaptive algorithm is its capability for monitoring of the network usage constantly and then allocating the bandwidth on demand in an autonomous, agile and scalable fashion.

Cybersecurity studies at undergraduate/postgraduate level are offered at numerous universities in Australia. The level offered varies from a specifically named undergraduate/postgraduate coursework degree to usual IT or relevant degrees offering cybersecurity as a minor or major theme. A minority of universities do not offer any specific cybersecurity specific course while others offer such courses in association with industry organisations. Based upon an extensive analysis of published course/program data from university websites, chosen as the best data repository that would normally be examined by prospective students, this study submits that in Australia available courses are few and are acknowledged as not meeting market demands for skilled cybersecurity professionals. This has been recently recognised by Australia’s Federal Government which has implemented the “Academic Centres of Cyber Security Excellence (ACCSE)” program in its 2016/2017 budget to promote the discipline and university support for it. In summary, courses currently available appear quite limited in scope.