The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people\'s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices\' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to support older adults (e.g., smart homes, assistive robots, wheelchairs) are increasingly applying artificial intelligence (AI), and thereby creating ethical challenges to technology development and use. The international debate on AI ethics focuses on implications to society (e.g., bias, equity) and to individuals (e.g., privacy, consent). The relational nature of care, however, warrants a humanistic lens to examine how \"AI AgeTech\" will shape, and be shaped by, social networks or care ecosystems in terms of their care actors (i.e., older adults, care partners, service providers); inter-actor relations (e.g., care decision-making) and relationships (e.g., social, professional); and evolving care arrangements. For instance, if an older adult\'s reduced functioning leads actors to renegotiate their risk tolerances and care routines, smart homes or robots become more than tools that actors configure; they become semi-autonomous actors, in themselves, with the potential to influence functioning and interpersonal relationships. As an experientially-diverse, transdisciplinary working group of older adults, care partners, researchers, clinicians, and entrepreneurs, we co-constructed intersectional care experiences, to guide technology research, development, and use. Our synthesis contributes a preliminary guiding model for AI AgeTech innovation that delineates humanistic attributes, values, and design orientations, and captures the ethical, sociological, and technological nuances of dynamic care ecosystems. Our visual probes and recommended tools and techniques offer researchers, developers/innovators, and care actors concrete ways of using this model to promote successful aging in AI-enabled futures.

The use of voice assistants (e.g., Amazon Alexa, Google Home) is being widely advocated as part of supporting people living with dementia at home. The development of this technology is largely driven by industry, and there is little research to determine how family carers and professionals use voice assistants with people with dementia. This paper presents the findings from further analysis of data from two studies: Study 1-a qualitative study that aimed to explore the views and expectations of family carers and professionals who use voice assistants to support people with a cognitive impairment at home, and Study 2-a qualitative enquiry aiming to identify the views and barriers on using voice assistants by family carers of people with dementia and professionals, together with a pilot case study evaluating a prototype that addresses barriers identified during the enquiry, entitled IntraVox. Based on processing of smart home sensor data, IntraVox uses a personalised human voice to send prompts and reminders to end-users to conduct daily life activities and to activate smart home processes using voice assistants. The results of the qualitative studies indicate that family carers and professionals use voice assistants in their caring role for home automation, skills maintenance and development, prompts and reminders, behaviour and environment monitoring, and for leisure and social interaction support. The findings also show that family carers and professionals have specific challenges that need to be overcome for them to realise the benefits that may be gained through the use of voice assistants within technology enabled care. The pilot case study also provided a useful demonstration that interoperability can be achieved to enable exchanges between IntraVox and voice assistants, with the aim of providing customised and personalised technological solutions that address some of the barriers that people with dementia and their carers face in the use of this technology.

Chiroptical sensing with real-time colorimetrical detection has been emerged as quantifiable properties, enantioselective responsiveness, and optical manipulation in environmental monitoring, food safety and other trace identification fields. However, the sensitivity of chiroptical sensing materials remains an immense challenge. Here, we report a dynamically crosslinking strategy to facilitate highly sensitive chiroptical sensing material. Chiral nematic cellulose nanocrystals (CNC) were co-assembled with amino acid by a two-step esterification, of which a precisely tunable helical pitch, a unique spiral conformation with hierarchical and numerous active sites in sensing performance could be trigged by dynamic covalent bond on amines. Such a CNC/amino acid chiral optics features an ultra-trace amount of 0.08 mg/m3 and a high sensitivity of 60 nm/(mg/m3) for formaldehyde gas at a molecule level detection, which is due to the three synergistic adsorption enhancement of dynamic covalent bonded interaction, hydrogen bonded interaction and van der Waals interaction. Meanwhile, an enhancement hierarchical adsorption of CNC/amino acid chiral materials can be readily representative to the precise helical pitch and colorimetrical switch for sensitive visualization reorganization.

Traditionally, China has been more reliant on a model of care that ensures older adults are cared for by family members. Whilst promoting the idea of older adults ageing in their own homes is essential, the provision of in-home care must shift from primarily relying on family caregivers to a model that places greater emphasis on gerontechnologies and enhanced healthcare service delivery. In this perspective article we argue for the adoption of a \'smart home\' model in aged care in China. The smart home model argues for innovative technologies to older adult care, such as virtual support groups, video-conferencing, and electronic health records; assistive technologies that can safely maintain independence and assist with daily living such as sensors, wearables, telehealth, smart home technologies as well as interactive robotic technologies for mobility and cognitive support such as humanoid robots, rehabilitation robots, service/companion robots. The adoption and implementation of gerontechnologies have been slow, with only a handful of solutions demonstrating proven effectiveness in supporting home care. The utilisation of such digital technologies to support and enable older adults in China to age-in-place can bring a significant contribution to healthy ageing. Nonetheless, it\'s crucial to focus on co-creating with end-users, incorporating their values and preferences, and enhancing training to boost the adoption of these gerontechnologies. Through a smart home model of care, China can age-in-place more effectively, leading to significant contributions to healthy ageing.

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult\'s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD\'s performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system\'s robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

-The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics\' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.

UNASSIGNED: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.
UNASSIGNED: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.
UNASSIGNED: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature \"Activity Duration\" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.
UNASSIGNED: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.