Smart Household Research Articles

Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Data-Driven Technology Applications in Planning, Demand-Side Management, and Cybersecurity for Smart Household Community

Data-Driven Technology Applications in Planning, Demand-Side Management, and Cybersecurity for Smart Household Community

Pengungkapan Kinerja Lingkungan Instansi Pemerintah Daerah Melalui Laporan Keberlanjutan Berbasis Global Reporting Initiative

Laundry washing is a crucial process in daily life. Although automatic washing machines have simplified the washing process, utilizing advanced technology like fuzzy logic control can enhance the performance of smart washing machines to achieve optimal washing results. This research aims to implement fuzzy logic control using the Mamdani method on smart washing machines to improve efficiency and washing quality. The research methodology consists of several stages. Firstly, analyzing variables that affect the washing process, such as laundry load, fabric thickness, dirt level, and water temperature. Next, designing a Mamdani fuzzy logic control system by determining membership functions for each variable and creating fuzzy rules to link input and output variables. Lastly, evaluating washing results based on predefined input variables. The research findings demonstrate that implementing Mamdani fuzzy logic control on smart washing machines can significantly enhance washing quality and adaptively determine washing parameters to achieve cleaner washing outcomes. Therefore, integrating Mamdani fuzzy logic control into smart washing machines has the potential to improve overall washing performance. In conclusion, utilizing Mamdani fuzzy logic control on smart washing machines is an effective step towards enhancing efficiency and effectiveness in laundry washing. This research contributes to the development of more adaptive and environmentally-friendly smart washing machine technology and plays a crucial role in advancing fuzzy logic control technology for other smart household applications.

A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-informed decision-making, ultimately leading to decreased energy consumption. Implementing eco-friendly architectural designs is paramount in mitigating energy consumption, particularly in recently constructed structures. This study utilizes clustering analysis on the original dataset to capture complex consumption patterns over various periods. The analysis yields two distinct subsets that represent low and high consumption patterns and an additional subset that exclusively encompasses weekends, attributed to the specific behavior of occupants. Ensemble models have become increasingly popular due to advancements in machine learning techniques. This research utilizes three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), and Decision Trees (DT). In addition, the application employs three more machine learning algorithms bagging and boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Trees (GBT). To augment the accuracy of predictions, a stacking ensemble methodology is employed, wherein the forecasts generated by many algorithms are combined. Given the obtained outcomes, a thorough examination is undertaken, encompassing the techniques of stacking, bagging, and boosting, to conduct a comprehensive comparative study. It is pertinent to highlight that the stacking technique consistently exhibits superior performance relative to alternative ensemble methodologies across a spectrum of heterogeneous datasets. Furthermore, using a genetic algorithm enables the optimization of the combination of base learners, resulting in a notable enhancement in prediction accuracy. After implementing this optimization technique, GA-Stacking demonstrated remarkable performance in Mean Absolute Percentage Error (MAPE) scores. The improvement observed was substantial, surpassing 90 percent for all datasets. In addition, in subset-1, subset-2, and subset-3, the achieved R2 scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction.

Using IoT and AI to replenish household food supplies: A systematic review

Food wastage because of the lack or incompletion of a household replenishment system is an essential topic to be addressed. An appropriate utilization of Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart household replenishment system to reduce food wastage. Therefore, this systematic review is dedicated to survey papers utilizing IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This study was conducted by following the PRISMA search strategy. It examined 70 papers in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system. The analysis resulted in 43 approaches using IoT technology, 27 using AI, and recently the use of AIoT has been trending in the past two years. This systematic review provides future directions for researchers acquired from the limitations of the reviewed papers to enhance the household replenishment system by developing and adding required features in smart food storage compartments. Further investigation into smart home appliances would lead to extensive approaches like smart shops, industries, and eventually smart cities.

Modeling techno-economic multi-objectives of smart homes considering energy optimization and demand management

The research suggests an approach that prioritizes customer needs and aims to reduce energy expenses while safeguarding customer privacy. Furthermore, it is recommended that smart homes incorporate a home energy management system to optimize appliance energy consumption. Conversely, the introduction of demand-side management addresses the energy management challenges faced by smart households. The main goal of this approach is to decrease energy usage and electricity costs for customers. Moreover, it enhances user satisfaction while waiting at common intervals. The primary emphasis of this study is on a smart residence furnished with energy management technology and smart home gadgets capable of supplying electricity to the grid. These objectives are considered distinct aspects in the multi-objective optimization issue stemming from this approach. The study utilizes the grasshopper optimization algorithm (GOA) to optimize battery and home appliance scheduling in smart homes with flexible devices. The goal is to reduce the overall cost of microgrid systems through demand-side management implementation. This comparison highlights the superiority of the proposed method in optimizing energy consumption and reducing carbon emissions in a variety of scenarios. By achieving lower energy costs and carbon emissions while maintaining a comfortable indoor environment, the proposed method proves to be a highly effective and sustainable solution for energy management in buildings. These simulation results provide strong evidence of the method’s potential to significantly impact energy efficiency and environmental sustainability in real-world applications. Furthermore, the consistent minimization of the discomfort index showcases the method’s ability to prioritize occupant comfort while still achieving significant energy savings and emissions reductions. Overall, the comparison with other algorithms solidifies the effectiveness and practicality of the proposed method in addressing the complex challenges of energy management and sustainability in smart homes.

Design and Implementation of a Smart Energy Meter System using the Internet of Things

Recent advances in the Internet of Things (IoT) and cloud computing have led to the development of various smart home applications and services. In addition, with the increase in the number of home appliances, the daily electricity consumption is also increasing, which increases the need for smart energy meters and monitoring systems. The proposed smart energy meter system measures current and voltage, calculates energy consumption and other information. The system is realized using ESP32s board (combination of microcontroller and Wi-Fi module), which connects to the internet and uploads data to the cloud. ThingSpeak is used as a cloud web server, which allows any user to remotely view individual consumption figures. This paper presents the design and implementation of a household smart energy meter that can collect and store energy consumption data of household appliances and major loads. The system measures three-phase currents and voltages, calculates power consumption, transmits the data to the cloud and displays it on an OLED screen.

Mobile Application Interface for Device Management in the Smart Household System

The article analyzes the features of the development of a complex information system called Smart Household, aimed at automating and improving the implementation of a wide variety of functions in a multi-disciplinary modern household, in particular, such as care for domestic animals, a garden, home territory, etc. The use of an effective mobile interface in this kind of system will allow household owners to monitor and control remotely a lot of useful functions using a mobile phone or a tablet. The Smart Household information system could help with the tasks of taking care of the home territory and a garden, automatic watering of the garden and lawn. The system can be configured for certain time moments according to weather conditions (temperature, humidity), which will allow economical use of water and provide optimal conditions for plant growth.

The Role of Performance in Smart Meter’s Acceptance: A Survey in Joinville, Brazil

The incorporation of renewable energy sources necessitates the upgrade of the electrical grid to a smart grid, which involves the implementation of smart meters. Although smart meters provide benefits to users, many smart meter implementation projects have failed to be accepted by users. This article assesses the factors influencing the acceptance of household smart meters in Joinville, a city in the south of Brazil. Based on the Unified and Extended Theory of Acceptance and Use of Technology (UTAUT2), a structural equation model was estimated using data from a sample of 136 respondents in the city of Joinville. The results indicate that Performance Expectancy, Hedonic Motivation, and Social Influence constructs have a more substantial effect on the Intention to Use smart meters. The results provide evidence for planning the upgrade of the electrical grid by implementing smart meters in southern Brazil.

Government digital policy breaks the mystery of “limited participation” in China's home finance market

This study uses a two-step approach to construct a multi-period double-difference model and introduces a quasi-natural experiment of the Broadband China pilot policy to investigate whether household financial market participation at the urban level is affected by the digital economy, which is significant for promoting Chinese households' shift from savings to investment and alleviating the long-standing problem of insufficient household financial market participation in China. In terms of direct impact, the digital economy increases the household financial market participation rate of urban residents by 3.26%, and increases the financial market participation rate of highly financially literate households by 2.14%; in terms of indirect impact, the development of the digital economy increases the total number of household smart Internet devices by 8.27%, and similarly increases the attention to household financial information by a significant 4.22%, which further positively influences the household financial market participation rate. This paper also evaluates the individual and regional differences of the digital economy on household financial market participation, and the estimated causal effect of the digital economy on household financial market participation is purer, which expands the scope of research on the digital economy and household financial market participation, and provides a certain reference basis and policy inspiration for the government to promote the construction of the digital economy.

Power Quality Improved Grid-Interfaced PV-Assisted Onboard EV Charging Infrastructure for Smart Households Consumers

Power Quality Improved Grid-Interfaced PV-Assisted Onboard EV Charging Infrastructure for Smart Households Consumers

Interpretable transformer-based model for probabilistic short-term forecasting of residential net load

Short-term residential load forecasting is of great significance for the demand-side energy management of the grid and the home energy management of resident customers. The massive penetration of distributed renewable energy, especially small-scale solar photovoltaic (PV), in the residential sector urgently requires us to move from traditional load forecasting to net load forecasting. In recent years, deep learning techniques that can improve model forecasting performance have developed rapidly in the field of residential load forecasting. However, the opaque nature of deep learning makes its practical application very difficult. This paper proposes a Transformer-based probabilistic residential net load forecasting method that utilizes quantile regression to quantify uncertainty in future load demand. Meanwhile, to improve the interpretability of deep learning model, local variable selection network is developed to automatically select relevant features and provide feature-level explanations. Additionally, interpretable sparse self-attention mechanism is proposed to extract long-term temporal dependencies. Numerical experiments are carried out with data from real household smart meters. The results show that the proposed model outperforms other state-of-the-art forecasting models. In terms of point forecasting, compared with the most common deep time series forecasting model LSTM, the proposed model decreases by 21.3%, 27.3% and 20.9% On three point forecasting performance metrics. Compared with Vanilla Transformer, the proposed InterFormer decreases by 9.9%, 10.5% and 9.0% On three point forecasting performance metrics. In terms of probabilistic forecasting, compared with LSTM and Vanilla Transformer, the average pinball loss of the proposed model decreases by 26.2% and 17.0%, respectively. Additionally, and most importantly, our model provides users with explanations in terms of feature importance and temporal patterns.

DEVICE FOR AUTOMATED CONTROL OF EXPIRATION DATES OF FOODSTUFFS, STORED IN REFRIGERATOR

One of the main problems of storing food (especially perishable) in refrigerators is compliance with their expiration dates. To solve it, it is necessary to constantly monitor and promptly notify food consumers about the approaching expiration of these deadlines. At present, this problem is solved mainly manually by checking the expiration date information printed on food packaging visually or using a hand-held scanner. At the same time, time is spent on checking, there is a high probability of untimely consumption of the food product or even its spoilage, which leads to certain financial losses. Today, given the development of Internet of Things technologies, it is possible to simply and cheaply automate the process of monitoring the expiration dates of food products stored in refrigerators and having a corresponding label on the packaging. Since 2021, Russia has introduced mandatory labeling of dairy products using the 2D Data Matrix code, which allows you to obtain data on expiration dates from the Chestny Znak state labeling and tracking system. Currently, the industry is already producing smart household refrigerators that can be connected to the Internet to transmit various information to the user. However, the common disadvantages of the known designs of smart refrigerators are their high cost, design complexity and the impossibility of working with 2D Data Matrix code labels applied to food packaging. A solution is proposed in the form of a stand-alone wireless smart device that allows you to simply and economically implement automatic notification of users about the approaching expiration date of food stored in the refrigerator, thereby eliminating the possibility of untimely consumption and spoilage. The proposed device does not require structural changes to the equipment of the refrigerator, has a low cost and ease of installation and use, and allows you to turn an ordinary refrigerator into a smart one.

The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection.

Demand planning of a nearly zero energy building in a PV/grid-connected system

The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %.

Day-ahead aggregated load forecasting based on household smart meter data

Smart meters provide much energy consumption information at the residential level, making it possible to improve short-time load forecasting accuracy by identifying more specific load patterns for each consumer type. This paper proposes a day-ahead load forecasting approach that uses the smart meter data aggregated by residential customers’ power consumption characteristics. First, the long-term trend information and daily fluctuation information are extracted from the residential load time series. According to the load characteristics reflected by the daily load fluctuation information, the residential consumers are clustered into several groups using the K-means algorithm. The non-linear autoregressive neural network is used to forecast each cluster of consumers to capture their specific load patterns. Finally, the aggregated load at the system level is obtained by combining each cluster’s forecasting results. The proposed method’s forecasting performance is evaluated on the data from Irish household customers’ actual smart meters. The outcomes show that the suggested approach can improve forecasting performance under a specific clustering scale.

Highly sensitive, ultra-reliable flexible piezoelectret sensor for non-contact sitting motion tracking and physiological signal monitoring

Non-contact, cost-effective, multifunctional and robust monitoring platform without mechanical wearing burden is highly desired to enhance the immersive experience for users during the long-term health management. However, most reported intelligent electronics in human life need to be adhered conformably to key human body parts for accurate signal detection, giving rising to activity interference and wearing burden in the long-term usage. Herein, a highly sensitive flexible film pressure sensor with a folded structure configuration assembled from irradiation-cross linked polypropylene (IXPP) piezoelectret film featured with piezoelectric-like dynamic electrical output attributes are reported. Such simple folded structure incorporated specially into the device design endows the piezoelectric film sensor with merits such as enhanced sensitivity, high signal-to-noise ratio and excellent anti-interference capacity. Consequently, the sensor can achieve a linear pressure sensitivity of 92 pC/kPa over a wide pressure range of 0.6–62.4 kPa, a pressure resolution of 0.98%, and fast response/recovery time of 40 ms. The flexible piezoelectric film sensors are embedded into cushions and attached to the back of chairs for application demonstrations of non-contact sensing. Remarkably, low-frequency human motion and weak physiological signal detection are achieved in a non-contact mode by a highly sensitive piezoelectric sensing principle in an all-in-one configuration, simplifying the signal processing and monitoring system design. A vital sign monitoring system based on the intelligent cushion is further developed to unlock the health status of sedentary crowd. This is the first work that ever use commercially available irradiated cross-linked polypropylene (IXPP) film to design pressure sensor and such sensors are successfully connected to the commercial “C-life” smart home ecosystem, presenting strong practicability and application prospects of such films in smart households and remote healthcare beyond the laboratory.

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

Research on user-side flexible load scheduling method based on greedy algorithm

In the context of increasing flexible load represented by smart household appliances, this study aims to establish a demand response model of residential flexible load to minimize electricity cost and reduce grid load variance. In this study, the flexible loads of residents are classified considering different load demand response modes, and demand response models are established for all kinds of loads. To make full use of residential electricity data, this paper established a user side flexible load multi-objective optimization scheduling model, the model for electricity cost and power grid load variance minimizing the objective function, and the safe operation of the meter and the adjustable resources as constraint, load and energy storage battery time-sharing electricity price for resident’s flexible optimization scheduling. Finally, the greedy algorithm is use to calculate and analyze the model. The result show that the model is effective and feasible.

A Hierarchical Deep Reinforcement Learning-Based Community Energy Trading Scheme for a Neighborhood of Smart Households

The uncertainties from the distributed energy resources (DERs) and the prosumer’s heterogeneous characteristics bring great challenges to the operation of the community energy market. In this paper, the real-time energy sharing and management in the community market are decomposed into two-layered sub-problems: the household appliance scheduling problem and the internal energy trading pricing problem. A hierarchical deep reinforcement learning (HDRL) based scheme is proposed for the community energy trading with multiple households, containing a two-stage learning process. In the inner stage, a multi-agent deep reinforcement learning (MADRL) based approach is developed to learn the real-time appliances scheduling policy based on the local observations and given internal electricity price in a decentralized way. In the outer stage, a deep reinforcement learning (DRL) based pricing approach is proposed to determine the real-time internal electricity prices based on the participants’ historical net power and external energy supplier’s electricity prices. The scheduling policy and households’ state are not required in the pricing process. Finally, the simulations with real-world datasets demonstrate that the internal energy trading can significantly reduce the prosumers’ daily cost and the control performance of the proposed scheme is superior to the existing studies.