Smart home - The Sustainability Management Wiki

Authors: Elnaz Afshar, Elif Gönen
Edited by: Sura Hashim, Elixane Bruet
Last updated: June 3, 2026

Executive summary

Smart homes combine connected devices, sensors, and automation to improve comfort, safety, accessibility, and energy management. In organizational sustainability work, they matter because they show how digital systems can support lower resource use, healthier indoor environments, and more inclusive living conditions. At the same time, adoption does not automatically produce positive outcomes. Effective programs must address affordability, accessibility, training, and user support so that benefits reach low-income households, older adults, and people with disabilities rather than reinforcing existing inequities.

Economically, smart homes can reduce household costs through home energy management systems, smart thermostats, smart meters, demand response, and feedback nudges that help users adjust behavior. However, results depend on sustained user engagement, clear onboarding, and interoperable systems. Fragmented protocols, vendor lock-in, and limited data portability can increase costs, reduce flexibility, and weaken long-term value. From a management perspective, organizations should therefore focus not only on technical performance, but also on usability, maintenance, and open standards.

Ecologically, smart homes can lower energy use, improve load shifting, support renewable integration, and reduce emissions when control systems are well designed. Yet these gains can be offset by rebound effects, standby consumption, short device lifetimes, repair constraints, and e-waste. A credible sustainability assessment must therefore consider the full life cycle of devices, including manufacturing, software support, repairability, and end-of-life treatment. Socially and legally, smart homes raise important issues around indoor environmental quality, privacy, cybersecurity, and regulatory compliance. Secure-by-design and privacy-by-design approaches, local resilience during connectivity failures, and compliance with laws such as the GDPR are essential for responsible deployment.

1 Description and history

Computer science has advanced significantly over the last few decades, especially in internet access and use. Most modern activities now rely on internet-based services. In recent years, Internet of Things (IoT) services have become increasingly common in daily life.¹

People often describe any device with some degree of artificial intelligence as a “smart.” To respond appropriately, smart technology must collect information from its surroundings. Many current innovations aim to improve human well-being, and smart technology drives much of that progress.²

The term “smart” has been used in a number of contexts, including smart homes, smart TVs, smartphones, and smart learning. It generally implies “intelligent,” yet each concept has a slightly different meaning.³

The term “smart home” was first defined by Lutolf. Lutolf claims that the integration of multiple amenities within a house through a shared communication system is the “smart home concept.” This approach offers a high level of intelligent functionality and flexibility.⁴

Berlo and Allen (1999) define a smart home as an environment in which systems such as appliances, lighting, climate control, and security operate automatically. Balta-Ozkan et al. (2013) describe it as a tech-enabled residence with sensors and remote monitoring that adapts to its inhabitants’ needs.⁵

Smart homes enhance comfort, security, and energy efficiency through context awareness, remote control, and ambient intelligence. They allow users to manage appliances remotely and optimize electricity use while improving security with advanced monitoring and access management.⁴

The category of smart computing known as “smart home systems” (SHSs) deals with incorporating smart technology into homes to provide convenience, healthcare, safety, security, comfort, and energy efficiency. Smart homes improve quality of life by enabling remote and automated control of appliances and services.²

People have long relied on their homes for shelter. When electricity entered homes in the late nineteenth century, it expanded the range of services that homes could provide. The introduction of electric distribution networks in 1883 enabled households to use electric heat and light. The electric iron (1909), clothes washers (1910), air conditioners (1911), vacuum cleaners (1908) … quickly followed. Each of these appliances expanded what homes could offer their residents.⁶

The smart home concept began to develop in the 1970s with the introduction of X.⁷, a platform for home automation that uses radio frequency bursts to transmit digital data onto an existing electrical wiring system in a house. However, few fully developed examples of smart house technologies existed before 1984. United Technology Corporation (UTC) was the first company in the United States to use the term “intelligent building” in the 1980s. This idea was used by UTC subsidiary “building systems” to complete a partial renovation project at the CityPlace Building in Hartford, Connecticut. The CityPlace Building is often cited as one of the earliest architectural projects to integrate information technology into a building. Its computer system monitored and controlled lighting, elevators, air conditioning, and other equipment while also supporting services such as voice communication and email.⁸ The term “smart building” is generally associated with its emergence in the 1980s. It refers to structures integrating automated systems for technical management, energy control, and information networks. Some sources indicate that industrial actors, such as United Technologies Corporation (UTC), contributed to the use and dissemination of the term in the United States, particularly through projects like the CityPlace building in Hartford. However, scientific literature does not definitively identify a single inventor of the term nor designate a specific first smart building. Instead, a gradual adoption of the concept is observed, developing alongside technological advancements and building management needs.⁹

Figure 1 shows different smart areas in a house.

Figure 1: Different smart areas in a home

According to Furszyfer Del Rio and associates, in order for a SHT(smart home technology) to be considered “smart,” it must have four of the following characteristics: 1. have a digital connection; 2. give users more control; 3. allow automated procedures; and.⁴ contain the capacity for learning.¹⁰

Smart Home Environments (SHE) offer versatile applications, primarily in home care for the elderly, energy efficiency, entertainment, and security. These areas often overlap, enhancing overall convenience, sustainability, and safety.¹¹, as shown in figure.²

For example, in the elderly care context, 20% of the world’s population will be over age 60 by 2050, and many people in this group will face long-term illness or difficulty living independently. The WHO reports 650 million people with disabilities globally, making long-term hospital or assisted living care impractical. Integrating assistive technology and healthcare services into homes offers a more sustainable response.⁴

Figure 2: Accessibility & user benefit matrix¹² (plotted in a Python environment)

Smart-home adoption must remain accessible and beneficial for low-income families, older adults, and people with disabilities. Evidence from recent studies highlights persistent inequities that must be addressed.

Liu et al. report that families in subsidized housing frequently encounter “multiple home environmental asthma triggers, including pests, mold, secondhand smoke, leaks, poor ventilation, and aging infrastructure”.¹² They further note that “healthy housing services were under-implemented… due to unresponsive landlords, inadequate inspections, and poor maintenance”.¹² Smart-home environmental monitoring will reduce inequities only if systems are affordable and paired with enforceable housing standards.

Choi and Soave show that subsidized housing reduces unaffordability but may still expose families to inadequate or overcrowded conditions.¹³ They also describe “Matthew Effect” dynamics, where better-resourced households benefit more from social programs.¹³ Procurement decisions should therefore prioritize households with the greatest unmet needs.

Moon et al. demonstrate the importance of environmental modification and individualized support for people with disabilities, noting that interventions included “home environmental modification… to lower environmental barriers and increase convenience”.¹⁴ Inclusive interfaces—such as voice control, tactile buttons, and simplified navigation—are therefore essential for smart-home accessibility.

Training and support are also critical. Liu et al. highlight that caregivers often lack information and feel powerless¹², while Moon et al. emphasize the importance of education and hands-on guidance.¹⁴ Smart-home programs should therefore include training, onboarding, and community-based support.

Equity, inclusion, and affordability must be embedded into smart-home design and policy. Subsidized bundles, accessible interfaces, and user-centered training—combined with accountability mechanisms—are essential to ensure that smart-home benefits reach the populations most affected by environmental and housing inequities.

Smart home energy savings depend on sustained user engagement, as households often underestimate their consumption and benefit from structured feedback that can reduce energy use by 8–12%.¹⁵ Satisfaction and continued use hinge on expectation management, with effective onboarding helping users interpret feedback and adjust device settings. Engagement is also shaped by the roles of occupants, providers, and designers within the broader socio-technical environment.¹⁵ Because satisfaction strongly influences long-term use, digital nudges must match user needs and be supported by accessible assistance channels.¹⁵ Effective change management therefore requires clear onboarding, regular feedback, defined responsibilities, and reliable support to sustain engagement.

2 Economic performance

Market penetration reached 14.2% in 2022, with AHEMS potentially reducing electricity bills by 53.2%.¹⁵⁴³ Eco-feedback nudges can change behavior and typically reduce energy consumption by 8% to 12%.¹⁵ Assistive modifications significantly improve quality of life, increasing occupational performance scores by 5.0 points.¹⁶.Specific figures for 2025 were not available when this article was written. Among the leading markets, the United States and China dominate the industry, driving global adoption and technological innovation.⁷

Smart homes integrate interconnected devices, sensors, and systems that can digitally communicate, monitor, and exchange data while allowing remote access and control.¹⁷ These interconnected systems enable seamless automation of various household functions, such as climate control, lighting, and security, enhancing both comfort and energy efficiency.¹⁸ One of the most significant impacts of smart home technology is its role in energy management, addressing concerns about rising energy consumption.¹⁹

The U.S. Energy Information Administration (EIA) reports that the residential sector consumes nearly one-third of the nation’s total energy. This consumption is gradually rising, creating challenges for both energy regulators and suppliers. To address the increasing demand, it is essential to focus on optimizing energy systems, particularly from the demand-side perspective.²⁰ According to data from the Energy Information Administration (EIA), in 2023, the final energy consumption of households, including purchased electricity, amounted to 11.3 quadrillion BTU (quads), representing approximately 15% of total consumption by sector. When focusing on primary energy consumption, the household contribution accounts for about 7% of the total energy consumption in the United States in 2023.²¹

Government policies are playing a significant role in the broader deployment of smart energy meters across the European Union (EU). As energy consumption continues to rise, smart meters have become a key tool in improving energy efficiency and enabling more precise monitoring of usage patterns. Installing these meters will allow hundreds of millions of households to add a critical element of domestic smart energy systems while also establishing a direct connection to the smart grid.²²

A key advantage of smart meter adoption is its potential to improve cost savings and energy efficiency for consumers, suppliers, and distribution system operators alike. Additionally, pilot projects have demonstrated that smart meters can lead to energy reductions ranging from 2% to as much as 10%, highlighting their role in optimizing energy consumption and reducing overall demand.²² Feedback nudges drive behavioral change, typically reducing residential energy consumption by 8% to 12%.¹⁵ Residential building end-use accounts for ~12% of global CO₂ emissions, with appliance use-phases responsible for 72% to 77% of their climate impact. These benchmarks replace unsupported estimates to provide a verified baseline for evaluating smart home ecological and economic performance.²³

Smart meters can also play a crucial role in demand-side management, particularly in regions where heating accounts for a significant portion of household energy consumption. In Finland, for instance, home heating and hot water usage make up 83% of total residential energy consumption, with nearly half of all detached houses relying on electric heating. Managing heating demand during winter peak consumption periods presents a valuable opportunity for optimizing energy use and reducing strain on the power grid. By integrating smart metering systems, households can better regulate heating schedules, shift consumption to off-peak hours, and improve overall energy efficiency and cost-effectiveness.²⁴ ²⁵ ²⁶

Smart homes rely on fragmented protocols like Zigbee, Z-Wave, and Wi-Fi, necessitating hubs that often serve merely as message passers to specific clouds.²⁷ This architectural dependency creates “islands of knowledge” and risks vendor lock-in, increasing complexity while limiting long-term scalability and innovation.¹⁶ The inability to integrate diverse programs restricts data interchange and significantly raises system maintenance and development costs.¹⁶ To unify communication, the industry is moving toward open frameworks like IoTivity, sponsored by the Open Connectivity Foundation.²⁷ These standards enable seamless interoperability regardless of manufacturer or chipset, ensuring a more competitive and future-proof marketplace.²⁷ These system-level challenges are further illustrated in Figure 3, which highlights device lifecycles, repairability constraints, and circularity considerations.

Figure 3: Device lifecycle, repairability & circularity graph¹⁵ ²³ (plotted in a Python environment)

In the Official Statistics of Finland, lighting represents a small share of household electricity consumption: in 2023, it constituted only about 1.9% of the total household energy consumption. Within the share dedicated to home appliances (approximately 13% of the total), lighting accounts for about 15%, which is a significantly more modest proportion than the “over 30%” sometimes claimed. These data show that, although lighting remains a non-negligible consumption item, it is less significant than other uses such as space heating, domestic hot water, or other electrical equipment.²⁸ ²⁹

Demand Side Management (DSM) balances electricity use by shifting some consumption from peak hours to off-peak periods. This reduces peak consumption, stabilizes the grid, and lowers carbon emissions. In price-based DSM systems, energy providers encourage consumers to change their habits through variable tariffs. Several types of pricing exist, including All-Inclusive tariffs (TTC), Time-of-Use (TOU) rates, Critical Peak Pricing (TPC), and Real-Time Pricing (TTR).³⁰

With technological progress such as the “smart home,” DSM has evolved significantly through technical intervention. Artificial Intelligence and dynamic pricing systems have enabled numerous advancements, including Automated Demand Response (ADR), Direct Load Control (DLC), smart grid DR systems, blockchain applied to energy, decentralized approaches, and Virtual Power Plants (VPP). These new technologies allow for greater integration of renewable energies and improve grid stability while helping to reduce costs and environmental impact.

The Internet of Things (IoT) plays a key role in this evolution. Through connected sensors, it becomes possible to automate appliance operation, monitor consumption in real time, and make intelligent decisions based on peak or off-peak periods. Embedded devices provide the necessary connectivity and processing power for these systems to operate smoothly and efficiently.³¹

Demand management has social consequences, both by incentivizing individuals to change their lifestyle habits and by redistributing electricity costs among different social groups. A qualitative study was conducted with users participating in a technological pilot project. The same TEI box, allowing for the control of energy-intensive appliances via a mobile app, was installed in approximately two hundred households in a Norwegian city. This box manages electricity consumption according to various temperature programs, automatic adjustments based on hourly energy prices, and the total energy consumption of the home. Through this demand management technology, the goal was to reduce total energy consumption and grid load peaks.

Norway made the installation of smart meters mandatory, which have been deployed by grid managers since 2019. This specific study focused on indoor residential heating. The targeted appliances were, in order of priority: water heaters, air-to-air heat pumps, underfloor heating (direct electric heating), and radiators. Basic SET kits included three devices to be connected to the hub, though some users added others. The SET hub, a home energy management system, allows for remote control and automation of indoor heating via a mobile application, as well as the control of previously isolated devices through a single interface. This SET hub synchronizes these devices to save electricity and balance the load while meeting the needs of all household members. However, efficiency depends on user involvement, often making Centralized Control (DLC) a simpler alternative.³²

When looking at household appliances, residents usually use each one based on their comfort and lifestyle. Consequently, the trade-off between optimal use and the peak-to-average ratio (PAR) can lead to high electricity costs. Cost-optimized scheduling typically moves energy-intensive tasks to off-peak pricing periods, which may not align with the user’s preferred times. Appliances are classified by flexibility: Interruptible Appliances (IA), like electric vehicles, can adapt easily; Non-Interruptible Appliances (NIA), like dishwashers, must complete their cycles; and Thermostatically Controlled Appliances (TCA) rely on weather. (1) While smart charging for electric vehicles can greatly reduce costs, it may cause minor secondary peaks during off-peak hours.³³

By integrating such a system, a smart home becomes an active grid participant: it shifts its consumption, optimizes photovoltaic self-consumption, smooths demand peaks, and reduces costs, all while maintaining occupant comfort. This clearly shows how home automation, dynamic pricing, and flexible services can change domestic consumption.

3 Ecological performance

Ecology examines interactions between living populations and their environment. To understand the ecology of the smart home, we must examine the relationship between the habitat and its inhabitants.⁶

Climate change is a global concern. As a direct result of human activity, the global climate system is changing, leading to the greatest emissions of greenhouse gases (GHGs) in human history. Studies show that greenhouse gases (GHGs) drive extreme weather and changes in both human and ecological systems.³⁴

Human-generated CO² and other greenhouse gas emissions. Recent analyses estimate that residential buildings account for approximately 12–13% of global energy related CO² emissions.¹⁵ ²³ Households account for a substantial share of final energy consumption—for example, more than a quarter in Germany—while in the EU, energy-related products are responsible for roughly 6.8% of total greenhouse gas emissions.¹⁵ ²³ The estimate of 52% found in the sources refers specifically to the share of total primary energy savings derived from the EU residential sector rather than China’s total emissions.¹⁵ It has been suggested that using digital technology to lower carbon emissions without compromising wellness could give consumers access to new and better smart homes.³⁴

Smart homes can save the most energy by improving control over household consumption. Residential energy use occurs across single-family and multi-family housing typologies, which serve as the structural baseline for modeling smart energy systems.²³ While the sources do not provide a specific 80% figure for the United States, they emphasize the significant scale of this sector, noting that households in regions like Germany account for more than a quarter of final energy consumption.²³ With smart energy efficiency improvements, these homes have tremendous possibilities for gas and electricity savings. Most household end uses depend on electricity, including cooling and some space- and water-heating needs.³⁵

Artificial Intelligence of Things (AIoT) is an emerging digital technology that smart homes have adopted more widely in recent years. Home security, entertainment, health care, energy management, and temperature control are just a few of the features that Smart Homes may provide to meet everyday demands. These functions improve occupants’ quality of life and can also support environmental goals.³⁴

For example, smart homes often use solar energy to reduce reliance on natural gas and grid electricity. In addition to reducing pollution, solar energy can address some of the limitations of conventional gas and electricity supply. In this way, solar power can supply energy to different Internet of Things applications and support more environmentally responsible energy use.³⁶

The residential sector must play a crucial role in lowering carbon emissions to build the future smart grid. The most significant effort should be put into upgrading buildings in order to reduce CO² emissions in the residential sector. It is important to understand how technology affects CO² emissions. Therefore, improvements in technology have a significant impact on the residential sector’s overall CO² emissions. In Finland, lighting accounts for more than 30% of all household appliance electricity use.³⁷

According to a US Department of Energy report (DOE, 2002), broad use of wireless sensors is expected to lower emissions by 25% and increase manufacturing production and energy efficiency by 10%.³⁸ Wireless sensors were identified in the early 2000s as a promising technology for improving energy efficiency and the performance of industrial systems. Studies conducted by the U.S. Department of Energy (DOE) notably estimated that in the manufacturing sector, the adoption of wireless sensors could lead to significant gains, such as improved energy efficiency and a reduction in emissions associated with industrial processes. These applications are centered on industrial contexts and cannot be directly transposed to residential uses or households, as the technical and optimization frameworks differ.³⁹ ⁴⁰

These findings assessed how much energy different smart appliances used in both standard and energy-efficient settings. The results showed that energy-efficient settings led to significant reductions in energy use:24

Smart refrigerators: As in energy-efficient mode, smart refrigerators displayed an average 10% reduction in energy consumption when compared to normal settings. Better insulation and the application of advanced compressor algorithms were major factors in this decrease.⁴¹

Smart washing machines: Smart washing machines with load optimization features showed a 15% reduction in energy consumption while in energy-efficient mode. These devices’ smart changes to water levels and wash cycles conserved energy.⁴¹

Smart ovens: Smart ovens with improved insulation and a warmup period had an average 12% lower energy consumption while in energy-efficient mode. These ovens produced tasty cuisine with low energy usage.⁴¹

Smart thermostats: Smart thermostats maintain desired comfort levels by effectively controlling interior temperature. When combined with occupancy patterns and meteorological information, these thermostats were able to reduce the average temperature by 1°C during periods when nobody was using them, saving energy without sacrificing comfort.⁴¹

Smart refrigerators, washing machines, and ovens all demonstrated notable energy savings when operated in energy-efficient settings. These reductions, typically ranging from 10% to 20%, demonstrate potential energy savings, although real world results may vary due to rebound effects, as highlighted by recent studies.²³ ¹⁵

Additionally, By transmitting information, internet use can encourage more environmentally friendly behavior. Finally, AI-powered smart gadgets usually have great energy-saving features that allow intelligent “hibernation” of household devices, eliminate human errors, and optimize energy efficiency.³⁴

To prove energy and emissions impact, research frameworks define baselines such as a model village’s daily electrical demand of 1.545 kWh per household.⁴² Key performance indicators (KPIs) track efficiency through metrics like an annual yield of 145 kWh per for solar installations and the monitoring of standby loads, which are measured at roughly 0.5 W for energy-efficient televisions.²³ Economic performance is assessed by the amount of demand shifted away from high-price regions, while ecological success is quantified by avoided emissions, with combined technical scenarios showing a potential 34% reduction in climate change impact.⁴³ ²³ These systems integrate occupant comfort metrics to balance energy savings against user dissatisfaction, ensuring that temperature variations and load rescheduling remain within preferred user bounds.⁴³ Credible quantification relies on Measurement & Verification (M&V) modeling using tools like HOMER to verify lifetime operation, as well as the integration of utility interval data.⁴² ⁴³ These approaches utilize 15, 30, or 60-minute non-overlapping windows to calculate average peak power and optimize load scheduling without affecting grid reliability.⁴³

The energy flow and management system in a smart house is shown in figure.⁴

Figure 4: Energy flow and management system in a smart house

Although energy efficiency in theory can result in significant savings, it also has a negative impact on energy usage and frequently has rebound effects. These days, the phrase “rebound effect” is used more frequently. When discussing rebound effects in the original energy context, several kinds of processes are used to negate the potential energy savings of energy efficiency measures. The time that is saved by a technology can be used for energy-intensive activities. The balance could remain beneficial even when the rebound effect is significant, resulting in an overall decrease in energy or resources. In the specific case where the rebound effect overcomes the early benefits and causes an increase in total energy usage,, is often referred to as ‘Jevons’ Paradox’or ‘Khazzoom-Brookes postulate.⁴⁴ This dynamic is illustrated in Figure 5, which visualizes how rebound effects can offset or even surpass the expected energy savings.

Figure 5: Rebound effect visualization¹⁵ ²³ (plotted in a Python environment)

Undoubtedly, smart houses have both positive and negative aspects when it comes to energy conservation. For instance, consumers can use the Internet of Things (IoT) to operate household equipment remotely. Allowing users to remotely turn off devices such as televisions and lights can prevent wasted electricity. In addition, certain smart gadgets have “monitoring” and “sensing” capabilities that allow them to measure power consumption, sending real time data to a smartphone to help users break patterns of energy waste.³⁴

First, the integration of DERs (Distributed Energy Resources) transforms the home into an active energy system. At the heart of this shift, the HEMS (Home Energy Management System) plays a central role; it is an intelligent residential energy management system that allows homeowners to monitor energy production, storage, and consumption. It relies on real time monitoring and communication between various smart household appliances, thereby contributing to improved energy efficiency and active participation in smart grids (see in Figure 6).⁴⁵ HEMS systems ensure the management of all household loads, including time-shiftable appliances (washing machines, dishwashers), thermostatic loads (heating, air conditioning, water heaters), as well as domestic storage systems and electric vehicle charging, while taking into account technical constraints such as battery degradation.

Recent research demonstrates that the effectiveness of a HEMS is considerably enhanced when coupled with a smart thermostat, which strengthens a home’s capacity to participate in demand response (DR) while optimizing energy consumption and maintaining acceptable thermal comfort. The smart thermostat adjusts the setpoint temperature by analyzing dynamic variables such as electricity tariffs, solar radiation, and the actual presence of occupants. A sophisticated model, tested among various types of households in Istanbul, integrates advanced features: photovoltaic self-consumption control, vehicle-to-infrastructure (V2X) communication management, solar tilt modeling, and consideration of battery degradation. Simulation results indicate that such synergy can reduce the daily electricity bill by 53.2% (under TOU pricing and PV feed-in tariffs) and decrease cooling costs by 24% compared to a conventional thermostat.⁴⁶ However, the literature highlights a persistent challenge: shifting the load to off-peak hours risks generating new consumption peaks in the evening, which sometimes limits the achievement of peak load reduction objectives.⁴

When focusing on energy management that integrates and coordinates multiple energy resources to optimize the energy system, we refer to IEMC (Integrated Energy Management and Coordination), a system frequently used for microgrids. IEMC designates an integrated and coordinated energy management, often on a larger scale. As illustrated in Figure 7, the study “An effective IoT-based demand response for energy-efficient smart homes” (2025) proposed an IEMC model capable of automatically optimizing the use of appliances, domestic storage, and photovoltaic production based on grid signals. The primary objective is to design a new connected energy management controller (IEMC) for a smart building. This controller will resolve the issue of manual appliance scheduling across different time slots. To meet this demand, this autonomous system will manage household appliances based on pricing, hours of use, and available energy resources, including photovoltaic systems and energy storage. The proposed controller is programmed to optimize each appliance’s parameters so that it operates intelligently within the desired time interval. Furthermore, this IEMC automation improves energy efficiency. For example, a study considered a residential household powered by the main grid, a photovoltaic system, and an energy storage system (ESS). The integrated energy management system (IEMC) automatically schedules appliances during off-peak hours and seamlessly manages non-programmable appliances during peak hours. The study implemented Time-of-Use (ToU) pricing to reduce energy costs. Additionally, weather forecasts are considered essential data and are publicly accessible. Finally, a hybrid energy management system (HGPO) is introduced to facilitate proactivity in price-based demand response (DR) programs, aiming for optimal reduction of electricity costs.³¹

Figure 6: Coordination between smart homes and the electricity grid

Figure 7: HEMS controller and system-wide optimization

When it comes to energy efficiency in smart homes, rebound effects can cause energy consumption to either rise or fall less than expected. Importantly, this phenomenon results from the interaction of technological features with cultural, social, psychological, and economic factors.⁴⁴

Despite this, smart homes increase electricity consumption and offer convenience, comfort, and security by equipping homes with an excessive number of smart devices. When non-electronic things are made “electronic” by the trend of intelligence, home devices are forced to operate in standby mode for longer periods of time, consuming more energy.³⁴

When a smart home device (thermostat, bulb, sensor, etc.) has a limited lifespan, non-replaceable batteries, or discontinued firmware, the equipment’s sustainability is compromised. It is essential to plan for repairability (spare parts, replaceable batteries, software updates) and an end-of-life strategy, including recycling according to regulations such as the WEEE Directive in Europe. Ideally, a life cycle registry would allow for tracking each device from manufacturing to recycling to minimize electronic waste and carbon footprint; however, this remains a rare practice. Therefore, considering the entire life cycle—from design to disposal—is crucial for a comprehensive environmental impact analysis.⁴⁷

The manufacturing of electronic devices involves the mining of rare metals and requires significant energy for assembly and transport between production chains. The end-of-life stage also poses major challenges: a vast amount of e-waste is disposed of informally or mixed with other metal scrap, leading to soil and water contamination, greenhouse gas emissions, and health risks for workers. Some studies estimate that up to 80% of electronic waste is improperly treated.⁴⁸

Furthermore, it is vital to understand the environmental impact by examining the device’s status once it is out of service, specifically by looking at different e-waste circuits. This involves implementing a circular economy, which may have an environmental footprint due to the use of chemicals during processing. However, this circular approach offers numerous advantages, such as reducing dependency on raw materials, lowering the consumption of essential resources, creating jobs, and mitigating the negative effects of resource extraction and processing. Various circuits exist, such as retail stores and collection points, where owners bring their e-waste to be logged and treated according to EU standards. Recycling is a strategic step in the life cycle assessment of electronics, fostering a circular economy through repair and upcycling. Upon collection, equipment is sent to pretreatment centers for evaluation: functional products are redirected toward reuse, while others undergo selective dismantling. This process includes extracting sensitive components, such as batteries (lead and Li-ion), and a decontamination phase to isolate hazardous substances. Finally, the remaining materials are processed in specialized plants to recover critical raw materials (gold, silver, palladium), enabling their reintegration into the manufacturing of new devices.

However, a significant portion of electronic equipment escapes specialized channels, ending up either in household waste or mixed with scrap metal destined for export. This improper management leads to major environmental and health risks: incineration releases greenhouse gases, while landfilling contaminates soil and water with hazardous substances. This mixing with conventional metal waste makes traceability difficult and exposes workers, often in third-party countries, to toxic pollutants under the guise of “reuse.”

The recent integration of end-of-life stages into life cycle assessments (LCA) highlights a critical lack of data. According to the “Urban Mine Platform” project, which studied various EU countries, approximately one-third of small IT equipment is unaccounted for; the study estimates that 43% end up in the trash and up to 25% are treated indifferently as scrap metal.²⁴ Given these findings, experts predict that global e-waste production will reach 80 million tonnes per year by 2030.⁴⁹

4 Social impact

One of the key advantages of IoT in residential settings is the ease of use it provides. Smart home systems, such as automated temperature control, lighting, and virtual assistants, can be efficiently operated through voice activation or mobile applications. This type of interaction allows residents to manage household functions, such as indoor climate and lighting, without using physical switches. The integration of voice-activated controls particularly benefits individuals with limited mobility, offering greater accessibility and convenience in daily tasks.³⁵

Elderly individuals and people with disabilities often face greater challenges in daily life compared to others. As the share of older adults in the global population rises, demand for accessible living environments will also grow. According to a United Nations report, the share of individuals aged 60 and above is expected to double between 2007 and 2050, reaching 2 billion by mid-century.⁵⁰

Smart home technology can help create safer and more supportive living environments by offering enhanced security, automation, and monitoring capabilities. These systems enable users to control various household functions or set them to operate automatically. Additionally, smart home technology can detect potential hazards and send alerts to prevent accidents. For instance, individuals with hearing impairments may struggle to notice a doorbell, while those with Alzheimer’s disease might forget to turn off the stove. By integrating smart home solutions, these difficulties can be effectively managed, improving safety and independence.⁴¹

While smart home technologies offer significant benefits, accessibility barriers remain a challenge, particularly for individuals with disabilities. Although these innovations have the potential to improve the quality of life and independence of people with disabilities, research indicates that their engagement with new technologies remains limited.⁵¹

One of the main obstacles is the socio-economic constraints that many individuals with disabilities face, making it difficult to afford and adopt smart home solutions. Additionally, the design of user interfaces and interaction methods in digital technologies often lacks inclusivity, posing challenges for individuals with physical and cognitive impairments. As a result, disability status is a key factor that influences individuals’ ability to effectively utilize and benefit from smart home technology.⁵²

Indoor Environmental Quality (IEQ) represents a major challenge for smart buildings, extending beyond simple energy savings. It encompasses indoor conditions that influence occupant health and well-being, such as temperature, humidity, ventilation, and the presence of pollutants. As illustrated in Figure 8, occupants are directly exposed to these environmental parameters, and intelligent measurement and control systems are required to continuously regulate them while maintaining overall energy performance. Recent European regulations impose minimum IEQ requirements and encourage the use of measuring and control devices, notably integrated into energy performance certificates. Member States must respect and enforce minimum IEQ levels, particularly regarding thermal comfort and Indoor Air Quality (IAQ). These requirements can be incorporated into the recommendations of Energy Performance Certificates (EPCs) aimed at improving indoor environmental quality.

Inadequate indoor conditions can have negative health effects. Excessive temperatures can cause thermal stress, while high humidity promotes condensation and mold growth. High concentrations of pollutants, particularly carbon dioxide (CO₂), are often a sign of insufficient ventilation and can adversely affect occupant comfort and cognitive abilities. Furthermore, for non-residential Zero-Emission Buildings (ZEB), the installation of measuring and control devices to monitor and regulate indoor air quality is required under Article 13, paragraph 5.⁵³ This will apply starting in 2028 to new non-residential buildings owned by public bodies and, from 2030, to all new non-residential buildings as well as buildings renovated to ZEB standards. Member States may also require the installation of such devices in residential buildings. These requirements are notably based on the EN 16798-1 standard, which defines different IEQ categories according to occupant expectations, as well as the European framework for sustainable buildings.

To establish this air quality, the implementation of control devices is necessary. Carbon dioxide (CO₂)-based Demand-Controlled Ventilation (DCV) plays a vital role in the Heating, Ventilation, and Air Conditioning (HVAC) sector. CO₂-based DCV is a significant technology because indoor CO₂ concentration serves as an effective biological indicator of Indoor Air Quality (IAQ) (2). When properly designed and operated, demand-controlled ventilation can achieve up to 30% energy savings compared to conventional Variable Air Volume (VAV) HVAC systems while maintaining acceptable comfort.⁵⁴ Thus, a balance must be found between maximizing air exchange to reduce indoor-sourced air pollutant concentrations and minimizing the penetration of outdoor pollutants, such as fine particulate matter (PM), especially in urban environments.

This requires proper operation of ventilation systems as well as the installation of air purification devices. Moreover, various passive or low-energy solutions exist to control temperature and improve air quality, such as Personal Comfort Systems (PCS) and mixed-mode ventilation (MMV), which reduce energy consumption while maintaining comfort. Issues can arise from incorrect commissioning, faulty performance, or a lack of maintenance, all of which can compromise indoor environmental.⁵⁵ Finally, sensors and platforms must interoperate effectively to realize the full value of these solutions. Protocols like Matter facilitate the integration of IEQ sensors into smart homes, making data accessible to occupants via dedicated applications and simplifying communication with other devices within a home automation ecosystem.⁵⁶

Figure 8: Intelligent IEQ monitoring and control in smart buildings

5 Political and legal aspects

Cloud-dependent platforms risk losing core safety and security functions during connectivity outages caused by natural events or targeted attacks. Local processing, such as Python-based neural networks predicting levels with >95% accuracy, ensures that essential environmental monitoring remains operational regardless of external connectivity. To address these risks, the proposed RES-Hub provides a resilient fallback by caching device states and maintaining critical services via an IoTivity-based architecture. This framework ensures secure local control and cross-vendor interoperability through OAuth 2.0, establishing offline resilience as a fundamental safety requirement for smart homes.

Smart home appliances control two types of data. The first type of data is administrative data, which is used to describe user information in general. Additionally, the second data consists of highly private information and/or user privacy that the device will generate in order to function effectively. For instance, this information includes sound, behavior, and photos, and it is gathered from the regular use of smart home appliances.⁵⁷

Although IoT is becoming more useful and efficient, it also creates major challenges, especially for data protection and compliance. Each IoT device is a possible point of entry for security risks and contains sensitive data, such as private information, confidential company data or extremely sensitive data like medical records.In the Internet of Things, data privacy refers to the ethical and legal management of personal data. IoT devices raise the danger of violations of privacy since they gather enormous amounts of data, sometimes without the users’ knowledge or agreement.⁵⁸

Table 1: Types of data collected by smart home devices

Today, cybersecurity and privacy protection remain two of the main challenges in deploying connected devices and smart homes. IoT devices collect, process, and transmit significant volumes of often sensitive data, which increases the risks of information leaks, cyberattacks, and privacy breaches for occupants. It is therefore essential to integrate these issues from the very first stages of system design.

Security by Design (SbD) is a general systems engineering principle that aims to integrate security requirements directly into the architecture of hardware, software, and services, rather than adding them a posteriori as patches or external controls. This approach reduces structural vulnerabilities and limits exposure to attacks. Today, Security by Design is encouraged, or even required, by many governments and companies, particularly in the field of consumer connected objects.⁵⁹

In this context, several best practices are recommended for IoT devices, such as the use of secure boot, regular software updates, rapid vulnerability patching, responsible disclosure of security flaws, and the absence of default passwords. In Australia and the United Kingdom, regulatory frameworks define a set of security principles to be integrated from the design stage of connected objects, including identity management, personal data protection, and the ability for users to delete their data.⁵⁹ ⁶⁰

However, integrating Security by Design represents an additional and complex step in the development process. Developers must identify and apply the security and privacy protection practices best suited to the system being designed. In this regard, the concept of Privacy by Design (PbD) complements Security by Design by focusing specifically on the protection of personal data. It is an engineering principle that aims to minimize data collection, limit its use to necessary purposes, and integrate privacy control mechanisms from the design phase. Recent work proposes the use of Semantic Web technologies to facilitate the application of Privacy by Design in IoT systems. For example, the PARROT ontology allows for modeling the functional needs of IoT systems and associating them with appropriate privacy protection measures. This approach aims to guide software engineers in the concrete integration of PbD principles, based on competency questions defined from several real-world IoT systems.⁶¹

Furthermore, numerous studies show that basic IoT architectures often lack internal security mechanisms. Requirements such as strong authentication, communication confidentiality, secure cryptographic key management, and fault tolerance must be integrated before any real-world deployment. Various solutions have been proposed to strengthen the security of IoT systems, including lightweight authentication schemes based on cryptographic hash functions, two-factor authentication mechanisms, or solutions aimed at masking device identities to preserve anonymity and confidentiality.⁶²

Securing communications is also a central challenge, particularly in cloud-based architectures used in smart homes. Secure protocols such as SSL/TLS, symmetric and asymmetric encryption techniques, as well as robust authentication mechanisms are necessary to ensure the confidentiality, integrity, and authenticity of data exchanged between devices, platforms, and users. Specific approaches have also been developed to secure IPv.⁶ communications in IoT environments, particularly against denial-of-service (DoS) attacks during address configuration.⁶²

Finally, the integration of smart homes into smart energy grids raises additional challenges regarding data security and system reliability. Recent anomaly detection techniques, based for example on auto-encoders, allow for the automatic identification of abnormal behavior in sensor data, thereby contributing to strengthening system security and trust in the collected data.⁶³

Legal frameworks for IoT differ across countries, and each jurisdiction applies its own standards and regulations. These rules are intended to solve the common issues that the Internet of Things presents on a global scale, including data security, privacy protection, and cross-border data flows. Without a single global standard for IoT security and privacy, businesses and consumers must navigate a complex patchwork of regional rules.⁵⁸

In the field of IoT, several important rules and regulations have become standards:

In Europe, the General Data Protection Regulation (GDPR): One of the strictest privacy and security rules in the world is GDPR. It places duties on businesses worldwide as long as they target or gather information on EU citizens. With its strict consent requirements, rights for data subjects, and sanctions for non-compliance, GDPR has set a high standard and impacted IoT operations globally.⁵⁸ The General Data Protection Regulation (GDPR) does not apply solely to companies established within the European Union. It has extraterritorial scope and also concerns organizations located outside the EU whenever they process the personal data of individuals located within the Union—for example, by offering goods or services or monitoring their online behavior. This regulation is crucial in the context of smart homes and the Internet of Things (IoT), where many services are provided by international actors processing data from European users.⁶⁴ ⁶⁵

In California, the California Consumer Privacy Act (CCPA) gives residents the right to know what personal data a company collects about them and why. Additionally, even in cases where there is no breach, this law permits customers to bring claims against businesses for violating their privacy policies.⁵⁸ It was previously suggested that consumers could initiate legal action under the California Consumer Privacy Act (CCPA) even in the absence of a data breach. However, according to the California Office of the Attorney General, the private right of action provided by the CCPA is strictly limited. It applies only in the event of a security breach involving unauthorized access, disclosure, or exfiltration of specific personal information, resulting from a failure to maintain reasonable security measures. Other violations of the CCPA, such as non-compliance with access or deletion rights, fall exclusively under the authority of the California regulatory body.⁶⁶ ⁶⁷

In Canada, the Personal Information Protection and Electronic Documents Act (PIPEDA) regulates the collection, use, and disclosure of personal data by private sector entities engaged in commercial activities. It also offers recommendations for IoT device security.⁵⁸

An international overview of smart home privacy laws is shown in figure.³

Figure 9: Overview of smart home privacy laws.

Government regulations are essential for promoting the use of smart technology and sustainable building techniques. The green construction industry has grown in industrialized countries because of financial support systems, tax incentives, and regulatory frameworks. However, policy support is frequently missing or absent in developing countries. There is a major gap between the need for sustainable construction regulations and how they are actually implemented in developing nations.⁶⁸

When such policies do exist, they are often outdated, poorly implemented, or underfunded. There are, however, a few examples of progress. South Africa, for example, has established a Green Building Council that certifies green buildings and encourages sustainable building methods. Governments in countries such Kenya and Nigeria are also starting to implement construction rules that take sustainability concepts into

account. Although these efforts are encouraging, governments must expand them through stronger enforcement, financial incentives, and public awareness campaigns to achieve widespread adoption.⁶⁸

References

1
Vardakis, G., Hatzivasilis, G., Koutsaki, E. & Papadakis, N. Review of Smart-Home Security Using the Internet of Things. Electronics 13, 3343 (2024).
2
Chakraborty, A. et al. Smart Home System: A Comprehensive Review. Journal of Electrical and Computer Engineering 2023, 7616683 (2023). https://doi.org:https://doi.org/10.1155/2023/7616683
3
Yang, H., Lee, W. & Lee, H. IoT Smart Home Adoption: The Importance of Proper Level Automation. Journal of Sensors 2018, 6464036 (2018). https://doi.org:https://doi.org/10.1155/2018/6464036
4
Ali, M. R. A. M. B. I. R. M. A. M. A Review of Smart Homes—Past, Present, and Future. IEEE Transactions on Systems, Man, and Cybernetics, Part C03 42, 1190 – 1203 (03 April 2012). https://doi.org:10.1109/TSMCC.2012.2189204
5
Kim, M. J., Cho, M. E. & Jun, H. J. Developing Design Solutions for Smart Homes Through User-Centered Scenarios. Frontiers in Psychology 11 (2020). https://doi.org:10.3389/fpsyg.2020.00335
6
Crowley, J. L. & Coutaz, J. in Ambient Intelligence. (eds Boris De Ruyter, Achilles Kameas, Periklis Chatzimisios, & Irene Mavrommati) 1-16 (Springer International Publishing).
7
Alexandra Catalina Lazaroiu a, M. R. b., *, Vasile Sebastian Dancu c,Georgiana Balaban. Social impact of decarbonization objectives through smart homes: Survey and analysis,. 230 (September 2024).
8
Li, W., Yigitcanlar, T., Liu, A. & Erol, I. Mapping two decades of smart home research: A systematic scientometric analysis. Technological Forecasting and Social Change 179, 121676 (2022). https://doi.org:https://doi.org/10.1016/j.techfore.2022.121676
9
Intelligent Buildings – an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/engineering/intelligent-buildings.
10
Furszyfer Del Rio, D. D., Sovacool, B. K. & Griffiths, S. Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries. Energy and Climate Change 2, 100035 (2021). https://doi.org:https://doi.org/10.1016/j.egycc.2021.100035
11
Costin Badica, M. B., Amelia Badica. An Overview of Smart Home Environments: Architectures, Technologies and Applications. (2013).
12
Liu, M., et al. (2026). Mitigating Home Environmental Asthma Triggers in Subsidized Housing. Healthcare.
13
Choi, K. H., & Soave, A. (2025). Subsidized Housing: The Panacea to Canada’s Housing Affordability Crisis? Canadian Review of Sociology.
14
Moon, K., et al. (2022). The Effects of Occupation Based Community Rehabilitation… Occupational Therapy International.
15
M. Berger, H. Gimpel, F. Schnaak, and L. Wolf, “Can feedback nudges enhance user satisfaction? Kano analysis for different eco-feedback nudge features in a smart home app,” Electronic Markets, vol. 35, p. 29, 2025.
43
Gonçalves, I., Gomes, Á., & Henggeler Antunes, C. (2019). Optimizing the management of smart home energy resources under different power cost scenarios. Applied Energy, 242, 351–363. https://doi.org/10.1016/j.apenergy.2019.03.108 (doi.org in Bing)
16
Kim, K., et al. (Year). Effectiveness of a Korean Smart Home Modification Program for Older Adults. Journal/Conference Name, Volume(Issue), pages.
17
A. Shuhaiber, W. A., S. Almansoori. Trust in smart homes: the power of social influences and perceived risks, in: Intelligent Sustainable Systems: Selected Papers of WorldS4 2022. Vol. 1 305–315 (2023).
18
Absalom E. Ezugwu, O. T., Ojonukpe S. Egwuche,Laith Abualigah, Annette Van Der Merwe, & Jayanta Pal, A. K. S., Ahmed Ibrahim Alzahrani,Fahad Alblehai, Japie Greeff, Micheal O. Olusanya. Smart Homes of the Future. (2025).
19
Karduri, R. G., Anurag. The Impact of Smart Homes on Energy Conservation and Demand Management. (2018).
20
Rajesh Subbiah, A. P., Eric K. Nordberg, Achla Marathe, Madhav V. Marathe. Energy Demand Model for Residential Sector: A First Principles Approach. (2017).
21
U.S. energy facts explained – consumption and production – U.S. Energy Information Administration (EIA). https://www.eia.gov/energyexplained/us-energy-facts/.
22
Germany’s delayed electricity smart meter rollout and its implications on innovation, infrastructure, integration, and social acceptance. (31 March 2023).
23
Reale, F., Castellani, V., Hischier, R., Corrado, S., & Sala, S. (2019). Consumer Footprint: Basket of Products indicators on household appliances. JRC Technical Report, European Commission, Joint Research Centre.
24
Tilastokeskus. Consumption of energy in living and housing in 2010-2017. (2018).
25
LUKE, T. a. Pientalojen polttopuun k¨aytt¨o 2016/2017. (2018).
26
Lund PD, L. J., Mikkola J, Salpakari J. . Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy (2015).
27
Doan, T. T., Safavi Naini, R., Li, S., Avizheh, S., Venkateswarlu, M. K., & Fong, P. W. L. (2018). Towards a Resilient Smart Home. In IoT S&P ’18: Proceedings of the 2018 Workshop on IoT Security and Privacy (pp. 1–7). ACM. https://doi.org/10.1145/3229565.3229570 (doi.org in Bing)
28
Statistics Finland – Statistics by topic – Energy consumption in households. https://stat.fi/til/asen/kas_en.html.
29
Final Consumption of Energy in Households – Motiva. https://www.motiva.fi/en/databank/energy-use-in-finland/final-consumption-of-energy/final-consumption-of-energy-in-households/.
30
Akbari-Dibavar, A., Nojavan, S., Mohammadi-Ivatloo, B. & Zare, K. Home energy management system considering effective demand response strategies and uncertainties. Energy Reports 8, 5256–5271 (2022).
31
Habibu, M. A., Sivakumar, S., Kanagachidambaresan, G. R. & Mwanandiye, E. S. An effective IoT-based demand response for energy-efficient smart homes. Energy Informatics 2025 8:1 8, 125- (2025).
32
Vindegg, M. & Julsrud, T. E. Digitised demand response in practice: The role of digital housekeeping for smart energy technologies. Energy Efficiency 2024 18:1 18, 1- (2024).
33
Hofmann, M. & Lindberg, K. B. Residential demand response and dynamic electricity contracts with hourly prices: A study of Norwegian households during the 2021/22 energy crisis. Smart Energy 13, 100126 (2024).
34
Han, Y., Du, X., Zhang, H., Ni, J. & Fan, F. Does smart home adoption reduce household electricity-related CO2 emissions? ——Evidence from Hangzhou city, China. Energy 289, 129890 (2024). https://doi.org:https://doi.org/10.1016/j.energy.2023.129890
35
King, J. Energy Impacts of Smart Home Technologies (2018).
36
Jo, H. & Yoon, Y. I. Intelligent smart home energy efficiency model using artificial TensorFlow engine. Human-centric Computing and Information Sciences 8, 9 (2018). https://doi.org:10.1186/s13673-018-0132-y
37
Jean-Nicolas Louis, A. C., Eva Pongrácz. Smart Houses for Energy Efficiency and Carbon Dioxide Emission Reduction. The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (2014).
38
Elkhorchani, H. & Grayaa, K. Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. Journal of Cleaner Production 135, 950-962 (2016). https://doi.org:https://doi.org/10.1016/j.jclepro.2016.06.179
39
Elkhorchani, H. & Grayaa, K. Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. J. Clean. Prod. 135, 950–962 (2016).
40
Hardy, J. E. & Manges, W. W. Wireless Sensors and Networks for Advanced Energy Management.
41
Anna A. Malysheva, , B. R., , N. S., , P. C. J. & , K. Energy Efficiency Assessment in Smart Homes: A
42
Bilich, A., Langham, K., Geyer, R., Goyal, L., Hansen, J., Krishnan, A., Bergesen, J., & Sinha, P. (2017). Life Cycle Assessment of Solar Photovoltaic Microgrid Systems in Off Grid Communities. Environmental Science & Technology.
44
Bremer, C. et al. How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI. ACM J. Comput. Sustain. Soc. 1, Article 4 (2023). https://doi.org:10.1145/3608115
45
Raza, A., Jingzhao, L., Ghadi, Y., Adnan, M. & Ali, M. Smart home energy management systems: Research challenges and survey. Alexandria Engineering Journal 92, 117–170 (2024).
46
Duman, A. C., Erden, H. S., Gönül, Ö. & Güler, Ö. A home energy management system with an integrated smart thermostat for demand response in smart grids. Sustain. Cities Soc. 65, (2021).
47
Pohl, J., Frick, V., Hoefner, A., Santarius, T. & Finkbeiner, M. Environmental saving potentials of a smart home system from a life cycle perspective: How green is the smart home? J. Clean. Prod. 312, 127845 (2021).
48
He, Y. et al. Driving sustainable circular economy in electronics: A comprehensive review on environmental life cycle assessment of e-waste recycling. Environmental Pollution 342, 123081 (2024).
49
Benqassem, S. et al. AU-DELÀ DES CHIFFRES : Comprendre les impacts environnementaux du numérique et agir.
50
UN, “UN Global Issue: Ageing, ¨. (20 May 2014 ).
51
Dobransky, K. H., E. The disability divide in internet access and use. Inf. Commun. 313–334 (2006).
52
Vicente, M. R. L., A.J. A multidimensional analysis of the disability digital divide: Some evidence for Internet use. Inf. Soc., 48–64 (2010).
53
Commission Notice providing guidance on new or substantially modified provisions of the recast Energy Performance of Buildings Directive (EU) 2024/1275 (Technical building systems, indoor environmental quality and inspections (Articles 13, 23 and 24)). https://energy.ec.europa.eu/document/download/77a9516d-8579-4c5b-af65-236f0029e7f1_en?filename=Technical+building+systems%2C+indoor+environmental+quality+and+inspections+%28Articles+13%2C+23+and+24%29+-+annex+10.pdf.
54
Lu, X., Pang, Z., Fu, Y. & O’Neill, Z. Advances in research and applications of CO2-based demand-controlled ventilation in commercial buildings: A critical review of control strategies and performance evaluation. Build. Environ. 223, 109455 (2022).
55
Dimitroulopoulou, S. et al. Indoor air quality guidelines from across the world: An appraisal considering energy saving, health, productivity, and comfort. Environ. Int. 178, 108127 (2023).
56
Mota, A., Serôdio, C., Briga-Sá, A. & Valente, A. Next-generation smart homes: CO2 monitoring with Matter protocol to support indoor air quality. Internet of Things 32, 101649 (2025).
57
Pratama, B. & , R. P. J. Smart Home Appliances Regulation and Principles. (2021). https://doi.org: 10.4108/eai.8-6-2021.2314344
58
Ammar Odeh, A. A. T., Tareq Alhajahjeh,Francisco Aparicio,Sara Hamed,Nizar Al Daradkeh,Nasser Ali Al-Jarallah. Smart and Agile Cybersecurity for IoT and IIoT Environments. (2024).
59
Secure by design – Wikipedia. https://en.wikipedia.org/wiki/Secure_by_design.
60
IoT Secure by Design guidance for manufacturers | Cyber.gov.au. https://www.cyber.gov.au/business-government/secure-design/secure-by-design/iot-secure-by-design-guidance-for-manufacturers.
61
Code of Practice for consumer IoT security – GOV.UK. https://www.gov.uk/government/publications/code-of-practice-for-consumer-iot-security/code-of-practice-for-consumer-iot-security.
62
Sadek, I., Codjo, J., Rehman, S. U. & Abdulrazak, B. Security and privacy in the internet of things healthcare systems: Toward a robust solution in real-life deployment. Computer Methods and Programs in Biomedicine Update 2, 100071 (2022).
63
Chanal, P. M. & Kakkasageri, M. S. Security and Privacy in IoT: A Survey. Wirel. Pers. Commun. 115, 1667–1693 (2020).
64
Bhardwaj, A., Bharany, S., Abulfaraj, A. W., Osman Ibrahim, A. & Nagmeldin, W. Fortifying home IoT security: A framework for comprehensive examination of vulnerabilities and intrusion detection strategies for smart cities. Egyptian Informatics Journal 25, 100443 (2024).
65
Art. 3 GDPR – Territorial scope – General Data Protection Regulation (GDPR). https://gdpr-info.eu/art-3-gdpr/.
66
Alshaleel, M. K. & Alshaleel, M. K. The Extraterritoriality of the gdpr and Its Effect on gcc Businesses. Global Journal of Comparative Law 13, 201–226 (2024).
67
Understanding the California Consumer Privacy Act (CCPA). https://legal.thomsonreuters.com/blog/the-california-consumer-privacy-act/.
68
Boye, H. I. M. E. a. A. Adapting Green Building Practices and Smart Technology inDeveloping Countries: A Review. (2024). https://doi.org:https://www.researchgate.net/deref/https%3A%2F%2Fdoi.org%2F10.62154%2Fajesre.2024.016.010407?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIiwicG9zaXRpb24iOiJwYWdlQ29udGVudCJ9fQ

1
Vardakis, G., Hatzivasilis, G., Koutsaki, E. & Papadakis, N. Review of Smart-Home Security Using the Internet of Things. Electronics 13, 3343 (2024).
2
Chakraborty, A. et al. Smart Home System: A Comprehensive Review. Journal of Electrical and Computer Engineering 2023, 7616683 (2023). https://doi.org:https://doi.org/10.1155/2023/7616683
3
Yang, H., Lee, W. & Lee, H. IoT Smart Home Adoption: The Importance of Proper Level Automation. Journal of Sensors 2018, 6464036 (2018). https://doi.org:https://doi.org/10.1155/2018/6464036
4
Ali, M. R. A. M. B. I. R. M. A. M. A Review of Smart Homes—Past, Present, and Future. IEEE Transactions on Systems, Man, and Cybernetics, Part C03 42, 1190 – 1203 (03 April 2012). https://doi.org:10.1109/TSMCC.2012.2189204
5
Kim, M. J., Cho, M. E. & Jun, H. J. Developing Design Solutions for Smart Homes Through User-Centered Scenarios. Frontiers in Psychology 11 (2020). https://doi.org:10.3389/fpsyg.2020.00335
6
Crowley, J. L. & Coutaz, J. in Ambient Intelligence. (eds Boris De Ruyter, Achilles Kameas, Periklis Chatzimisios, & Irene Mavrommati) 1-16 (Springer International Publishing).
7
Alexandra Catalina Lazaroiu a, M. R. b., *, Vasile Sebastian Dancu c,Georgiana Balaban. Social impact of decarbonization objectives through smart homes: Survey and analysis,. 230 (September 2024).
8
Li, W., Yigitcanlar, T., Liu, A. & Erol, I. Mapping two decades of smart home research: A systematic scientometric analysis. Technological Forecasting and Social Change 179, 121676 (2022). https://doi.org:https://doi.org/10.1016/j.techfore.2022.121676
9
Intelligent Buildings – an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/engineering/intelligent-buildings.
10
Furszyfer Del Rio, D. D., Sovacool, B. K. & Griffiths, S. Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries. Energy and Climate Change 2, 100035 (2021). https://doi.org:https://doi.org/10.1016/j.egycc.2021.100035
11
Costin Badica, M. B., Amelia Badica. An Overview of Smart Home Environments: Architectures, Technologies and Applications. (2013).
12
Liu, M., et al. (2026). Mitigating Home Environmental Asthma Triggers in Subsidized Housing. Healthcare.
13
Choi, K. H., & Soave, A. (2025). Subsidized Housing: The Panacea to Canada’s Housing Affordability Crisis? Canadian Review of Sociology.
14
Moon, K., et al. (2022). The Effects of Occupation Based Community Rehabilitation… Occupational Therapy International.
15
M. Berger, H. Gimpel, F. Schnaak, and L. Wolf, “Can feedback nudges enhance user satisfaction? Kano analysis for different eco-feedback nudge features in a smart home app,” Electronic Markets, vol. 35, p. 29, 2025.
43
Gonçalves, I., Gomes, Á., & Henggeler Antunes, C. (2019). Optimizing the management of smart home energy resources under different power cost scenarios. Applied Energy, 242, 351–363. https://doi.org/10.1016/j.apenergy.2019.03.108 (doi.org in Bing)
16
Kim, K., et al. (Year). Effectiveness of a Korean Smart Home Modification Program for Older Adults. Journal/Conference Name, Volume(Issue), pages.
17
A. Shuhaiber, W. A., S. Almansoori. Trust in smart homes: the power of social influences and perceived risks, in: Intelligent Sustainable Systems: Selected Papers of WorldS4 2022. Vol. 1 305–315 (2023).
18
Absalom E. Ezugwu, O. T., Ojonukpe S. Egwuche,Laith Abualigah, Annette Van Der Merwe, & Jayanta Pal, A. K. S., Ahmed Ibrahim Alzahrani,Fahad Alblehai, Japie Greeff, Micheal O. Olusanya. Smart Homes of the Future. (2025).
19
Karduri, R. G., Anurag. The Impact of Smart Homes on Energy Conservation and Demand Management. (2018).
20
Rajesh Subbiah, A. P., Eric K. Nordberg, Achla Marathe, Madhav V. Marathe. Energy Demand Model for Residential Sector: A First Principles Approach. (2017).
21
U.S. energy facts explained – consumption and production – U.S. Energy Information Administration (EIA). https://www.eia.gov/energyexplained/us-energy-facts/.
22
Germany’s delayed electricity smart meter rollout and its implications on innovation, infrastructure, integration, and social acceptance. (31 March 2023).
23
Reale, F., Castellani, V., Hischier, R., Corrado, S., & Sala, S. (2019). Consumer Footprint: Basket of Products indicators on household appliances. JRC Technical Report, European Commission, Joint Research Centre.
24
Tilastokeskus. Consumption of energy in living and housing in 2010-2017. (2018).
25
LUKE, T. a. Pientalojen polttopuun k¨aytt¨o 2016/2017. (2018).
26
Lund PD, L. J., Mikkola J, Salpakari J. . Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy (2015).
27
Doan, T. T., Safavi Naini, R., Li, S., Avizheh, S., Venkateswarlu, M. K., & Fong, P. W. L. (2018). Towards a Resilient Smart Home. In IoT S&P ’18: Proceedings of the 2018 Workshop on IoT Security and Privacy (pp. 1–7). ACM. https://doi.org/10.1145/3229565.3229570 (doi.org in Bing)
28
Statistics Finland – Statistics by topic – Energy consumption in households. https://stat.fi/til/asen/kas_en.html.
29
Final Consumption of Energy in Households – Motiva. https://www.motiva.fi/en/databank/energy-use-in-finland/final-consumption-of-energy/final-consumption-of-energy-in-households/.
30
Akbari-Dibavar, A., Nojavan, S., Mohammadi-Ivatloo, B. & Zare, K. Home energy management system considering effective demand response strategies and uncertainties. Energy Reports 8, 5256–5271 (2022).
31
Habibu, M. A., Sivakumar, S., Kanagachidambaresan, G. R. & Mwanandiye, E. S. An effective IoT-based demand response for energy-efficient smart homes. Energy Informatics 2025 8:1 8, 125- (2025).
32
Vindegg, M. & Julsrud, T. E. Digitised demand response in practice: The role of digital housekeeping for smart energy technologies. Energy Efficiency 2024 18:1 18, 1- (2024).
33
Hofmann, M. & Lindberg, K. B. Residential demand response and dynamic electricity contracts with hourly prices: A study of Norwegian households during the 2021/22 energy crisis. Smart Energy 13, 100126 (2024).
34
Han, Y., Du, X., Zhang, H., Ni, J. & Fan, F. Does smart home adoption reduce household electricity-related CO2 emissions? ——Evidence from Hangzhou city, China. Energy 289, 129890 (2024). https://doi.org:https://doi.org/10.1016/j.energy.2023.129890
35
King, J. Energy Impacts of Smart Home Technologies (2018).
36
Jo, H. & Yoon, Y. I. Intelligent smart home energy efficiency model using artificial TensorFlow engine. Human-centric Computing and Information Sciences 8, 9 (2018). https://doi.org:10.1186/s13673-018-0132-y
37
Jean-Nicolas Louis, A. C., Eva Pongrácz. Smart Houses for Energy Efficiency and Carbon Dioxide Emission Reduction. The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (2014).
38
Elkhorchani, H. & Grayaa, K. Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. Journal of Cleaner Production 135, 950-962 (2016). https://doi.org:https://doi.org/10.1016/j.jclepro.2016.06.179
39
Elkhorchani, H. & Grayaa, K. Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. J. Clean. Prod. 135, 950–962 (2016).
40
Hardy, J. E. & Manges, W. W. Wireless Sensors and Networks for Advanced Energy Management.
41
Anna A. Malysheva, , B. R., , N. S., , P. C. J. & , K. Energy Efficiency Assessment in Smart Homes: A
42
Bilich, A., Langham, K., Geyer, R., Goyal, L., Hansen, J., Krishnan, A., Bergesen, J., & Sinha, P. (2017). Life Cycle Assessment of Solar Photovoltaic Microgrid Systems in Off Grid Communities. Environmental Science & Technology.
44
Bremer, C. et al. How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI. ACM J. Comput. Sustain. Soc. 1, Article 4 (2023). https://doi.org:10.1145/3608115
45
Raza, A., Jingzhao, L., Ghadi, Y., Adnan, M. & Ali, M. Smart home energy management systems: Research challenges and survey. Alexandria Engineering Journal 92, 117–170 (2024).
46
Duman, A. C., Erden, H. S., Gönül, Ö. & Güler, Ö. A home energy management system with an integrated smart thermostat for demand response in smart grids. Sustain. Cities Soc. 65, (2021).
47
Pohl, J., Frick, V., Hoefner, A., Santarius, T. & Finkbeiner, M. Environmental saving potentials of a smart home system from a life cycle perspective: How green is the smart home? J. Clean. Prod. 312, 127845 (2021).
48
He, Y. et al. Driving sustainable circular economy in electronics: A comprehensive review on environmental life cycle assessment of e-waste recycling. Environmental Pollution 342, 123081 (2024).
49
Benqassem, S. et al. AU-DELÀ DES CHIFFRES : Comprendre les impacts environnementaux du numérique et agir.
50
UN, “UN Global Issue: Ageing, ¨. (20 May 2014 ).
51
Dobransky, K. H., E. The disability divide in internet access and use. Inf. Commun. 313–334 (2006).
52
Vicente, M. R. L., A.J. A multidimensional analysis of the disability digital divide: Some evidence for Internet use. Inf. Soc., 48–64 (2010).
53
Commission Notice providing guidance on new or substantially modified provisions of the recast Energy Performance of Buildings Directive (EU) 2024/1275 (Technical building systems, indoor environmental quality and inspections (Articles 13, 23 and 24)). https://energy.ec.europa.eu/document/download/77a9516d-8579-4c5b-af65-236f0029e7f1_en?filename=Technical+building+systems%2C+indoor+environmental+quality+and+inspections+%28Articles+13%2C+23+and+24%29+-+annex+10.pdf.
54
Lu, X., Pang, Z., Fu, Y. & O’Neill, Z. Advances in research and applications of CO2-based demand-controlled ventilation in commercial buildings: A critical review of control strategies and performance evaluation. Build. Environ. 223, 109455 (2022).
55
Dimitroulopoulou, S. et al. Indoor air quality guidelines from across the world: An appraisal considering energy saving, health, productivity, and comfort. Environ. Int. 178, 108127 (2023).
56
Mota, A., Serôdio, C., Briga-Sá, A. & Valente, A. Next-generation smart homes: CO2 monitoring with Matter protocol to support indoor air quality. Internet of Things 32, 101649 (2025).
57
Pratama, B. & , R. P. J. Smart Home Appliances Regulation and Principles. (2021). https://doi.org: 10.4108/eai.8-6-2021.2314344
58
Ammar Odeh, A. A. T., Tareq Alhajahjeh,Francisco Aparicio,Sara Hamed,Nizar Al Daradkeh,Nasser Ali Al-Jarallah. Smart and Agile Cybersecurity for IoT and IIoT Environments. (2024).
59
Secure by design – Wikipedia. https://en.wikipedia.org/wiki/Secure_by_design.
60
IoT Secure by Design guidance for manufacturers | Cyber.gov.au. https://www.cyber.gov.au/business-government/secure-design/secure-by-design/iot-secure-by-design-guidance-for-manufacturers.
61
Code of Practice for consumer IoT security – GOV.UK. https://www.gov.uk/government/publications/code-of-practice-for-consumer-iot-security/code-of-practice-for-consumer-iot-security.
62
Sadek, I., Codjo, J., Rehman, S. U. & Abdulrazak, B. Security and privacy in the internet of things healthcare systems: Toward a robust solution in real-life deployment. Computer Methods and Programs in Biomedicine Update 2, 100071 (2022).
63
Chanal, P. M. & Kakkasageri, M. S. Security and Privacy in IoT: A Survey. Wirel. Pers. Commun. 115, 1667–1693 (2020).
64
Bhardwaj, A., Bharany, S., Abulfaraj, A. W., Osman Ibrahim, A. & Nagmeldin, W. Fortifying home IoT security: A framework for comprehensive examination of vulnerabilities and intrusion detection strategies for smart cities. Egyptian Informatics Journal 25, 100443 (2024).
65
Art. 3 GDPR – Territorial scope – General Data Protection Regulation (GDPR). https://gdpr-info.eu/art-3-gdpr/.
66
Alshaleel, M. K. & Alshaleel, M. K. The Extraterritoriality of the gdpr and Its Effect on gcc Businesses. Global Journal of Comparative Law 13, 201–226 (2024).
67
Understanding the California Consumer Privacy Act (CCPA). https://legal.thomsonreuters.com/blog/the-california-consumer-privacy-act/.
68
Boye, H. I. M. E. a. A. Adapting Green Building Practices and Smart Technology inDeveloping Countries: A Review. (2024). https://doi.org:https://www.researchgate.net/deref/https%3A%2F%2Fdoi.org%2F10.62154%2Fajesre.2024.016.010407?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIiwicG9zaXRpb24iOiJwYWdlQ29udGVudCJ9fQ