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Currently, the internet of things (IoT) is being widely deployed in home automation systems. An analysis of bibliometrics is presented in this work that covers articles that were obtained from the Web of Science (WoS) databases and published between 1 January 2018, and 31 December 2022. With VOSviewer software, 3880 relevant research papers were analyzed for the study. Through VOSviewer, we analyzed how many articles were published about the home IoT in several databases and their relation to the topic area. In particular, it was pointed out that the chronological order of the research topics changed, and COVID-19 also attracted the attention of scholars in the IoT field, and it was emphasized in this topic that the impact of the epidemic was described. As a result of the clustering, this study was able to conclude the research statuses. In addition, this study examined and compared maps of yearly themes over 5 years. Taking into account the bibliometric nature of this review, the findings are valuable in terms of mapping processes and providing a reference point.

The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. This paper presents an Edge AI-based anomaly detection framework that combines Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) models to identify anomalies in IoT sensor data. The system is evaluated on both synthetic and real-world smart home datasets, including temperature, motion, and energy consumption signals. Experimental results show that LSTM-AE achieves higher detection accuracy (up to 93.6%) and recall but requires more computational resources. In contrast, IF offers faster inference and lower power consumption, making it suitable for constrained environments. A hybrid architecture integrating both models is proposed to balance accuracy and efficiency, achieving sub-50 ms inference latency on embedded platforms such as Raspberry Pi and NVIDEA Jetson Nano. Optimization strategies such as quantization reduced LSTM-AE inference time by 76% and power consumption by 35%. Adaptive learning mechanisms, including federated learning, are also explored to minimize cloud dependency and enhance data privacy. These findings demonstrate the feasibility of deploying real-time, privacy-preserving, and energy-efficient anomaly detection directly on edge devices. The proposed framework can be extended to other domains such as smart buildings and industrial IoT. Future work will investigate self-supervised learning, transformer-based detection, and deployment in real-world operational settings.

Federated Learning (FL) has emerged as a promising solution for privacy-preserving model training across distributed IoT devices. Despite its advantages, FL faces challenges such as inefficient client selection, data heterogeneity, security vulnerabilities, and exposure to Man-in-the-Middle (MITM) attacks. To address these issues, the Incentive-Aware Federated Bargaining (IAFB) framework is proposed, integrating Nash Bargaining for optimal client selection, Shapley-value-based incentives for fair reward distribution, and decentralized peer-to-peer (P2P) aggregation to eliminate single points of failure. To enhance security, IAFB employs AES-GCM encryption, ensuring data confidentiality, authenticity, and integrity during transmission, effectively mitigating MITM attacks. Experimental results demonstrate that IAFB improves participation fairness by 28%, boosts model accuracy by 6.5%, and reduces convergence time by 35% compared to FedAvg. Additionally, IAFB reduces communication overhead by 39.5% and enhances resilience against adversarial threats, making it highly suitable for secure and scalable FL deployment in resource-constrained IoT environments.

With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.

In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms.

PurposeThis article aims to understand the housing needs of older people and to ascertain the level of demand and supply of age-related housing in Scotland. It also explores interest in different types of retirement accommodation and tenure options.Design/methodology/approachA review of existing literature is undertaken on senior housing preferences and residential satisfaction. Primary data is collected from an online survey of people over 55 in Scotland to ascertain demand side requirements with secondary data on current supply obtained from the Elderly Accommodation Counsel and data on future pipeline collated from market reports.FindingsThe results from the survey confirm earlier research that seniors when looking for accommodation in their retirement years particularly focus on the local area, access to shops, social relations with neighbours and the design of the home interior. Current analysis of the level of supply at a county level reveals that there is significant undersupply with some particularly striking regional differences. Along with a desire for owner occupation there is interest, particularly among the 75 plus age group, to lease their accommodation, perhaps a consequence of volatile property markets, insufficient pension provision or a desire to pass wealth to their family prior to death. This shortfall in supply highlights development opportunities and raises the possibility of introducing a build-to-rent senior housing offering, which may be of interest to institutional investors.Practical implicationsThe Scottish Government is currently reviewing its strategy for Scotland's older people. The results are of practical benefit as they expose the gaps in supply of age-related stock at county level. This may require the government to introduce policy measures to encourage a mix of housing types suited for the ageing demographics of the population. This research highlights opportunities for developers and investors to fill that gap and explains why advancements in technology should be incorporated in the design process.Originality/valueThis paper brings together supply side data of senior housing in Scotland and provides insights into the housing preferences of seniors. It will be of direct value and interest to developers and institutional investors.

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.

With the popularity and affordability of ZigBee wireless sensor technology, IoT-based smart controlling system for home appliances becomes prevalent for smart home applications. From the data analytics point of view, one important objective from analyzing such IoT data is to gain insights from the energy consumption patterns, thereby trying to fine-tune the energy efficiency of the appliance usage. The data analytics usually functions at the back-end crunching over a large archive of big data accumulated over time for learning the overall pattern from the sensor data feeds. The other objective of the analytics, which may often be more crucial, is to predict and identify whether an abnormal consumption event is about to happen. For example, a sudden draw of energy that leads to hot spot in the power grid in a city, or black-out at home. This dynamic prediction is usually done at the operational level, with moving data stream, by data stream mining methods . In this paper, an improved version of very fast decision tree (VFDT) is proposed, which learns from misclassified results for the sake of filtering the noisy data from learning and maintaining sharp classification accuracy of the induced prediction model. Specifically, a new technique called misclassified recall (MR), which is a pre-processing step for self-rectifying misclassified instances, is formulated. In energy data prediction, most misclassified instances are due to data transmission errors or faulty devices. The former case happens intermittently, and the errors from the latter cause may persist for a long time. By caching up the data at the MR pre-processor, the one-pass online model learning can be effectively shielded in case of intermitting problems at the wireless sensor network; likewise the stored data could be investigated afterwards should the problem persist for long. Simulation experiments over a dataset about predicting exceptional appliances energy use in a low energy building are conducted. The reported results validate the efficacy of the new methodology VFDT + MR, in comparison to a collection of popular data stream mining algorithms from the literature.

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.

Internet of Things (IoT) is rapidly growing and contributing drastically to improve the quality of life. Immense technological innovations and growth is a key factor in IoT advancements. Readily available low cost IoT hardware is essential for continuous adaptation of IoT. Advancements in IoT Operating System (OS) to support these newly developed IoT hardware along with the recent standards and techniques for all the communication layers are the way forward. The variety of IoT OS availability demands to support interoperability that requires to follow standard set of rules for development and protocol functionalities to support heterogeneous deployment scenarios. IoT requires to be intelligent to self-adapt according to the network conditions. In this paper, we present brief overview of different IoT OSs, supported hardware, and future research directions. Therein, we provide overview of the accepted papers in our Special Issue on IoT OS management: opportunities, challenges, and solution. Finally, we conclude the manuscript.