In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

Introduction: Carers support programmes are commonly delivered in person, limiting attendance opportunities for rural carers and others who have access barriers. Studies using technology typically use text-based forums rather than real-time technology such as videoconferencing (VC). Delivering home-based carers support programmes by VC may mitigate barriers for accessing support. We report implementation findings for delivering a telehealth peer-support programme for isolated carers of people with dementia. Methods: Participants were recruited through aged care providers, peak bodies and media activities. Inclusion criteria were primary dementia caregiver with Internet access and being socially or geographically isolated. The study design was a staggered randomised waitlist design. Measures included the UCLA Loneliness Scale (ULS-6) and selected scales from the e-Health Literacy Questionnaire. Recruitment activities utilised digital processes. Participants completed a six-week programme delivered by VC. Qualitative data comprised logs detailing administration and IT procedures and difficulties. Post programme, 28 participants undertook semi-structured interviews. Data were analysed using descriptive statistics and thematic analysis. Results: There were 16 groups comprising 69 participants located throughout Australia, with 87% using their own devices. Technical issues were few but included connection problems, which were compounded by low digital literacy skills. Qualitative data themes included changing perceptions in using technology, differences in communicating by VC and technical support required. Recruitment activities were time-consuming and would benefit from IT tailored for group-based work. Eight groups continued to meet on a self-organised basis. Discussion: Providing peer-support groups using telehealth may have the potential to develop self-sustaining peer networks for isolated caregivers of people with dementia.

With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for farmers to understand, trust and use it. The use of fuzzy logic makes the system customisable in terms of types/number of sensors, type of crop, and adaptable for any soil types and weather conditions. The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms. To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type, soil moisture, soil temperature, plant life cycle, irrigation requirement and water application timing for Maize as our target crop. The experimental results show that our proposed system is interpretable, can detect anomalous data, and triggers actions accurately based on crop requirements.

Blockchain technology is increasingly recognised for its potential to enhance the security, reliability, and resilience of distributed systems. By ensuring data integrity and protecting against unauthorised access, blockchain creates immutable and transparent records, making it an appealing option for various applications. In the current healthcare landscape, secure data sharing is essential for improving patient outcomes, streamlining healthcare operations, and ensuring compliance with legal standards. This paper conducts a systematic literature review to explore how blockchain can be utilised as a security solution for health data sharing. We assess the strengths and weaknesses of blockchain-based solutions in areas such as secure data management, data sharing, access control, trust and transparency, and authentication and authorisation. Our findings indicate that blockchain has significant potential to secure health data sharing, although we identify certain limitations in blockchain-based access control systems and propose potential solutions. Additionally, this paper provides detailed insights into future research directions in this domain.