Fact-checked by Grok 2 weeks ago

Smart Technologies

Smart technologies encompass the integration of advanced , sensors, connectivity features—often via the (IoT)—and into devices and systems that were previously non-digital, enabling them to communicate, automate processes, adapt to conditions, and support remote operation for enhanced efficiency and user interaction. At their core, smart technologies rely on key components including sensors for (such as or motion detectors), actuators for physical responses, microprocessors for processing, and communication modules like or for networking. These elements allow systems to generate continuous data streams, apply for adaptive behaviors, and operate interactively or autonomously, mimicking intelligent . Evolving from industrial roots since the , modern smart technologies have accelerated with and digital control systems like (Supervisory Control and Data Acquisition), transforming passive objects into responsive ecosystems. Notable applications span multiple sectors, including smart homes where devices like thermostats and lighting systems monitor environments and adjust settings for energy savings and security; smart cities leveraging for traffic management, waste monitoring, and public safety through networked infrastructure; and smart manufacturing (Industry 4.0) using sensors for and . In healthcare, wearables and connected medical devices enable real-time patient monitoring, while in energy systems, smart grids balance dynamically. These implementations improve productivity, , and but raise challenges in cybersecurity, data privacy, and , prompting regulatory frameworks like the EU's Cybersecurity Act to ensure standardized and secure deployment. Overall, smart technologies represent a toward , where interconnected devices facilitate data-driven insights and across daily life and , with ongoing advancements in and networks poised to expand their scope further.

Definition and Fundamentals

Core Definition

Smart technologies encompass systems that integrate sensors, actuators, units, and mechanisms to perceive environmental inputs, analyze them in , and execute adaptive responses, thereby enabling environments that react dynamically and mimic selective aspects of . These technologies generalize beyond isolated devices to include networked structures where software and collaborate to handle unpredictable conditions, such as varying loads or behaviors. At their core, smart technologies exhibit four primary characteristics: adaptability, which permits adjustment to external stimuli through controllable elements like dissipators or learning algorithms; interconnectivity, facilitated by distributed sensing and communication protocols that link components across networks; analysis, involving instant monitoring and processing of inputs from accelerometers or environmental sensors; and self-optimization, achieved via loops that refine performance iteratively without constant human oversight. These traits distinguish smart technologies from static or manual systems by embedding agency and responsiveness directly into the . In contrast to , which broadly apply for general cognitive reasoning and problem-solving across diverse domains, smart technologies prioritize embedded in context-specific applications, focusing on practical environmental interactions rather than comprehensive, human-like . Foundational concepts underpinning this domain include machine-to-machine (M2M) communication, where devices exchange data autonomously to coordinate actions, and , which leverages historical patterns to anticipate events and enable proactive .

Key Enabling Technologies

Smart technologies rely on a suite of foundational hardware components that enable efficient processing and operation at the device level. Microcontrollers, such as those based on architectures, serve as the core processing units in resource-constrained environments, providing low-power computation for tasks like sensor data handling and basic control logic. Edge computing devices, including gateways and specialized accelerators, process data locally to reduce and demands, supporting decision-making in distributed systems. Power-efficient chips, exemplified by ARM processors, optimize energy consumption through advanced architectures that balance performance and battery life, making them essential for battery-operated smart devices. Software foundations underpin the intelligence and scalability of smart technologies by facilitating data analysis and storage. Machine learning algorithms, including supervised and unsupervised models, enable automated decision-making by processing sensor inputs to predict patterns or detect anomalies in real-time environments. Cloud platforms like AWS IoT provide robust infrastructure for data aggregation, storage, and remote management, allowing seamless scaling for large-scale deployments while integrating security features for device authentication. Communication standards form the backbone for interconnectivity in smart ecosystems, ensuring reliable data exchange across devices. networks enable massive machine-type communications, supporting up to 1 million devices per square kilometer with ultra-reliable low-latency connections below 1 ms, ideal for industrial automation and applications. enhances efficiency in dense environments through features like (OFDMA) and Target Wake Time (TWT), improving battery life and supporting higher device densities for home and enterprise . (BLE) operates in the 2.4 GHz band with data rates up to 2 Mb/s, prioritizing low power consumption via adaptive frequency hopping and short connection intervals, which suits wearable devices and short-range sensor networks. Integration models leverage and to promote among heterogeneous devices and systems. , often RESTful in design, allow standardized data exchange between smart devices and cloud services, enabling developers to build cross-platform applications without proprietary lock-in. layers abstract underlying differences, handling and data formatting to ensure seamless communication in multi-vendor ecosystems.

Historical Development

Origins and Early Innovations

The origins of smart technologies can be traced to early 19th-century innovations in , exemplified by the Jacquard loom invented by in 1801. This device used punched cards to control the weaving of complex textile patterns, representing one of the first instances of a programmable machine that automated repetitive tasks through pre-instructed sequences, laying groundwork for later computational control systems. In the mid-20th century, conceptual foundations for smart systems emerged through , a field pioneered by in his 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine. Wiener introduced the idea of feedback loops in machines and living organisms, emphasizing self-regulating systems that could adapt to inputs, which became essential for the responsive behaviors in future smart technologies. Concurrently, Alan Turing's 1936 paper "On Computable Numbers, with an Application to the " formalized the via the , providing a mathematical basis for algorithmic processes that underpin . Turing further advanced early AI theories in his 1950 work "," proposing (later known as the ) to evaluate machine intelligence, influencing the design of adaptive smart devices. Key hardware milestones in the 1940s and 1950s enabled the miniaturization and efficiency needed for smart systems. The , invented by and Walter Brattain at in December 1947, amplified signals and replaced vacuum tubes, allowing for more compact and reliable electronic circuits critical to automated controls. Building on this, at demonstrated the first on September 12, 1958, by fabricating multiple components—transistors, resistors, and capacitors—on a single slice, which revolutionized the of smart electronic systems. By the late 1960s and 1970s, foundational networking and consumer automation emerged. The , launched in 1969 by the U.S. Department of Defense's Advanced Research Projects Agency, connected its first four nodes (at UCLA, Stanford Research Institute, UC Santa Barbara, and the ), establishing packet-switching protocols that formed the basis for interconnected smart devices. In parallel, early smart appliances appeared, such as programmable thermostats introduced by in the late 1970s, like the Chronotherm series around 1977, which allowed users to preset temperature schedules via electronic controls, marking an initial step toward . These developments in the 1970s and 1980s shifted focus toward integrating computation with everyday objects, setting the stage for more advanced smart technologies.

Evolution in the Digital Age

The evolution of smart technologies in the digital age began in the with foundational advancements in networking and that enabled interconnected devices. The , proposed by in 1989 and made publicly available in 1991, provided a universal platform for data sharing and remote access, laying the groundwork for distributed smart systems. This was complemented by the introduction of the in 1992, recognized as the first , which integrated cellular communication, functions, and basic connectivity features like and calendars, foreshadowing the ubiquity of connected personal devices. These developments shifted smart technologies from isolated analog systems to digitally networked ecosystems, facilitating real-time interaction and data exchange. The 2000s marked a period of commercialization and scalability, driven by enhancements in identification and software ecosystems. Radio-frequency identification (RFID) tags saw widespread adoption starting in 2003, particularly after major retailers like Walmart mandated their use for supply chain tracking, enabling automated inventory management and paving the way for pervasive sensing in smart environments. Concurrently, the launch of the Android operating system in 2008 by Google introduced an open-source platform that fostered expansive app ecosystems, allowing developers to create applications for device connectivity and automation, which accelerated the integration of smart features into consumer electronics. The 2010s accelerated innovation through data processing and user interfaces, embedding intelligence into everyday infrastructure. The rise of analytics during this decade enabled the handling of vast datasets from connected devices, supporting and optimized operations in smart systems, as highlighted in early reports on its transformative potential. Voice assistants like Apple's , launched in 2011 with the , introduced for hands-free control of devices, enhancing accessibility in smart homes and vehicles. This era also saw urban-scale implementations, such as the smart city pilot in , , initiated around 2010, which integrated sensors for traffic, energy, and to create responsive urban environments. In the 2020s, connectivity and on-device intelligence have further refined smart technologies for efficiency and resilience. The global rollout of networks in 2020 provided ultra-low latency and higher bandwidth, essential for real-time applications like autonomous systems and remote operations. Edge AI emerged as a key advancement, processing data locally on devices to minimize delays and bandwidth use, enabling applications such as instant in industrial settings. The from 2020 onward drove rapid adoption of remote monitoring technologies, including IoT-enabled health trackers and platforms, which supported contactless surveillance and virtual care amid measures.

Types and Components

Internet of Things (IoT)

The (IoT) refers to a of physical devices, vehicles, appliances, and other objects embedded with sensors, software, processors, and connectivity features that enable them to collect, exchange, and act on data over the internet or other communication networks. This interconnected ecosystem allows everyday objects to become "smart" by facilitating seamless communication without human intervention. The term "" was coined by , a British technology pioneer, in 1999 during a at , where he described a vision of RFID-tagged objects communicating data to improve efficiency. IoT systems are structured around a three-layer that ensures efficient handling from collection to utilization. The layer consists of sensors and actuators that detect environmental changes, such as or motion, and gather from . The network layer manages , using protocols to transmit reliably across devices via wired or wireless means like or cellular networks. Finally, the processes the aggregated through cloud or to deliver actionable insights, enabling services like remote monitoring or automated responses. By 2025, the global number of connected IoT devices has reached approximately 21.1 billion, reflecting rapid adoption across consumer and industrial sectors. In consumer applications, devices like smart refrigerators exemplify this scale; for instance, Samsung's Family Hub refrigerators use built-in cameras and sensors to track inventory, suggest recipes, and integrate with shopping apps for automated reordering. Industrial IoT (IIoT), a subset focused on enterprise environments, drives manufacturing efficiency by connecting machinery for real-time monitoring, predictive maintenance, and optimized production workflows, reducing downtime and resource waste. Data flow in IoT ecosystems commonly employs publish-subscribe models to handle scalable, asynchronous communication among numerous devices. In this pattern, devices publish data to specific topics via a central broker, while other devices subscribe to those topics to receive relevant updates without direct connections. The (Message Queuing Telemetry Transport) protocol exemplifies this approach, offering a lightweight, low-bandwidth mechanism ideal for resource-constrained devices, ensuring efficient message delivery even in unreliable networks.

Artificial Intelligence Integration

Artificial intelligence (AI) plays a pivotal role in enhancing smart technologies by enabling systems to learn from data and make autonomous decisions, particularly through subsets such as neural networks that excel in . Neural networks, inspired by biological processes, process complex inputs from smart environments to identify patterns, such as anomalies in device behavior or user habits, allowing systems to respond intelligently without explicit programming. For instance, convolutional neural networks (CNNs) are widely used for visual in smart security systems, analyzing camera feeds to detect intrusions with high accuracy. This capability transforms raw data into actionable insights, improving efficiency in applications like where graph neural networks recognize dynamic flow patterns to optimize urban mobility. Integration of into smart technologies occurs through two primary methods: embedded AI directly on devices and cloud-based inference for more computationally intensive tasks. Embedded AI, exemplified by TensorFlow Lite, deploys lightweight models on resource-constrained hardware like microcontrollers in smart sensors and wearables, enabling on-device processing for low-latency responses and enhanced privacy by minimizing data transmission. Developed by , TensorFlow Lite optimizes models for size and speed, supporting applications in over 4 billion edge devices worldwide, including smart home appliances that perform real-time without dependency. In contrast, cloud-based inference leverages powerful remote servers for complex computations, such as those from AWS SageMaker integrated with IoT Core, where smart devices offload heavy AI workloads to the cloud for scalable analysis of aggregated data from multiple sources. This hybrid approach balances local efficiency with centralized intelligence, as seen in systems where edge devices handle initial pattern detection while cloud resources refine predictions. Key algorithms underpinning AI in smart technologies include for predictive maintenance and for adaptive controls. Supervised learning algorithms, trained on labeled datasets, forecast equipment failures by analyzing historical , enabling proactive interventions in systems to reduce downtime by up to 50% compared to reactive methods. A seminal framework in this area uses random forests and support vector machines to classify failure modes, demonstrating superior accuracy in environments. Reinforcement learning, on the other hand, facilitates adaptive decision-making through trial-and-error interactions with the environment, optimizing controls in dynamic scenarios like autonomous vehicles where agents learn to navigate by maximizing rewards for safe maneuvers. Deep reinforcement learning variants, such as those employing Q-networks, have achieved stable training for vehicle path planning, outperforming traditional rule-based systems in uncertain conditions. A prominent example of AI integration is in smart homes, where drives by adapting lighting, temperature, and to user preferences based on behavioral patterns. Neural networks analyze usage to create customized profiles, such as adjusting thermostats proactively for energy savings while maintaining comfort. By 2025, global smart home household penetration has exceeded 77%, with AI features like voice assistants and predictive integral to over 50% of consumer deployments in developed markets, reflecting widespread adoption in technologies. This integration draws briefly from streams for input, enhancing overall system responsiveness.

Sensors and Connectivity Protocols

Sensors in smart technologies serve as the foundational hardware for capturing environmental, physical, and , enabling and in interconnected systems. Environmental sensors, such as and detectors, measure ambient conditions to optimize use in buildings or ; for instance, thermistors and capacitive sensors provide accurate readings within ±0.5°C and ±3% , respectively. Motion sensors, including accelerometers, detect movement and orientation through changes in or piezoelectric effects, commonly used in wearable devices for activity tracking with sensitivities down to 1 mg . Biometric sensors, like monitors employing photoplethysmography (PPG), quantify physiological signals such as pulse variability, achieving accuracies of 95% or higher in clinical settings. Connectivity protocols facilitate the transmission of data across networks, prioritizing efficiency in resource-constrained environments. , built on , supports low-power for up to 65,000 devices, enabling self-healing topologies ideal for distributed arrays in homes and industrial monitoring. operates on a sub-1 GHz for reliable, low-interference communication in , supporting up to 232 nodes with a range of 30-100 meters indoors and emphasizing among certified devices. The (CoAP), a UDP-based RESTful protocol, is tailored for resource-limited devices, featuring a compact 4-byte header to minimize overhead in lossy networks like , thus supporting efficient machine-to-machine interactions. Performance metrics for these components are critical for practical deployment, balancing speed and sustainability. In real-time applications, such as industrial control, protocols like achieve end-to-end latencies under 10 ms through optimized beacon modes and direct addressing, ensuring responsive data flows. Energy consumption models focus on duty cycling, where s and protocols reduce active transmission to microjoule levels per packet; for example, and enable battery lives exceeding 5-10 years in sleep-dominant modes by limiting wake-ups to event triggers. CoAP further conserves power via asynchronous messaging, avoiding persistent connections and aligning with intermittent sensor sampling. Advancements in 2025 have introduced quantum sensors, leveraging nitrogen-vacancy (NV) centers in synthetic diamonds to achieve unprecedented precision in industrial settings, detecting magnetic field variations at the nanotesla scale for non-invasive semiconductor inspection and resource exploration. These sensors offer calibration-free operation and robustness in harsh environments, surpassing classical limits by orders of magnitude in sensitivity for applications like fault detection in manufacturing.

Applications and Implementations

Consumer and Home Automation

Consumer and home automation encompasses the integration of smart technologies into personal living spaces to enhance convenience, efficiency, and . These systems allow users to control household devices remotely via smartphones, voice commands, or automated routines, often leveraging wireless connectivity to create interconnected environments. Key examples include smart lighting systems that adjust illumination based on time of day or , intelligent thermostats that optimize heating and cooling, and connected cameras that provide and alerts. Smart lighting solutions, such as introduced in 2012, enable users to customize colors, brightness, and schedules through a central or voice assistants, promoting by dimming unused lights automatically. The system, developed by Signify (formerly Philips Lighting), supports integration with over 1,000 compatible devices and uses protocol for reliable mesh networking. Similarly, the Nest Learning Thermostat, launched in 2011 by Nest Labs (acquired by in 2014), learns user preferences to create adaptive schedules, potentially reducing heating and cooling costs by 10-15% annually through features like auto-away detection and energy reports. Security cameras form another cornerstone of home automation, with devices like the Google Nest Cam offering 1080p video, motion detection, and facial recognition to distinguish between familiar and unfamiliar individuals, sending tailored notifications to users' phones. Popular alternatives include the Ring Spotlight Cam, which incorporates two-way audio and motion-activated lights for deterrence, and the Arlo Pro series, known for wire-free installation and 4K resolution in recent models. These cameras often integrate with broader ecosystems for automated responses, such as turning on lights upon detecting movement. On the consumer device front, wearables like the , first released in April 2015, have revolutionized personal fitness tracking with built-in sensors for heart rate monitoring, step counting, and activity rings that encourage daily movement goals. The device syncs data to the Health app, providing insights into sleep patterns and calorie burn without requiring constant phone interaction. Complementing these are voice-activated ecosystems, such as Amazon's , debuted in 2014 with the speaker, which serves as a central hub for controlling smart home devices through commands like "Alexa, turn off the lights." Alexa's skills framework allows third-party integrations, expanding its utility to over 100,000 compatible routines. The adoption of these technologies has driven robust market growth, with the global smart home sector projected to exceed $150 billion in value by 2025, fueled by increasing consumer demand for seamless . In the United States, household penetration of at least one is expected to reach 63% in 2025, reflecting broader trends toward energy-conscious living where yields up to 20% savings in bills through optimized usage patterns. Globally, penetration rates are rising steadily, with estimates indicating around 25-30% of households incorporating smart features by mid-decade, particularly in urban areas. User interaction is streamlined through dedicated apps and centralized hubs, such as , a 7-inch smart display introduced in 2018 that acts as a control center for managing lights, thermostats, and cameras via touch or voice. The further enables routine creation, like syncing lights to wake-up alarms or coordinating security alerts with door locks, ensuring intuitive access without multiple logins. These interfaces prioritize privacy with features like activity controls and data encryption, making accessible for everyday users.

Industrial and Enterprise Systems

Industrial (IIoT) represents a cornerstone of smart technologies in and environments, enabling collection and analysis from interconnected machines and s to optimize operations. Unlike general , IIoT focuses on rugged, high-reliability systems designed for harsh industrial conditions, supporting applications such as and . A key implementation is , which uses algorithms to forecast equipment failures based on data patterns, thereby minimizing unplanned outages. The GE Predix platform, launched in , exemplifies IIoT's role in by providing a cloud-based for and asset performance management. Predix integrates data from turbines, locomotives, and other heavy machinery to enable proactive interventions, such as scheduling repairs before component degradation leads to breakdowns. In enterprise settings, smart technologies extend to through hybrid systems combining RFID for real-time asset tracking with for secure, tamper-proof transaction logging, enhancing transparency and reducing fraud in global logistics. Similarly, smart grids facilitate by dynamically balancing across enterprise facilities, incorporating sensors to monitor consumption and integrate renewable sources efficiently. These applications have demonstrated significant efficiency gains, with technologies achieving up to 50% reductions in unplanned downtime in manufacturing operations. A prominent case is ' Amberg Electronics Plant in , which leverages IIoT-enabled and twins to produce over 15 million programmable logic controllers annually with a defect rate approaching zero, showcasing the precision of integrated smart systems. Regarding scalability, IIoT deployments in large-scale networks—often involving thousands of nodes—rely on and low-power wide-area networks to handle vast data volumes without issues, as evidenced in industrial case studies managing 10,000+ devices across distributed sites.

Healthcare and Wearables

Smart technologies in healthcare have revolutionized personal health through wearable devices that enable continuous, non-invasive tracking of and biomarkers, allowing for proactive interventions. Continuous glucose monitors (CGMs), such as those developed by , emerged prominently in the as a cornerstone of , providing blood sugar data to users and healthcare providers. The STS, the company's first CGM, received FDA approval in 2006, but significant advancements in the included the G4 PLATINUM in 2012 and the G5 Mobile in 2015, which introduced integration for remote . By 2018, the G6 became the first CGM approved by the FDA for integration with , enhancing accuracy and usability for patients with . Electrocardiogram (ECG)-enabled wearables further expanded cardiac monitoring capabilities, with the Series 4 introducing an FDA-cleared ECG app in 2018 that detects through single-lead recordings. This feature, classified as a Class II medical device, allows users to generate ECG traces on demand and receive irregular rhythm notifications, facilitating early detection of arrhythmias. Subsequent models built on this foundation, but the Series 4 marked a pivotal shift toward consumer-grade devices with clinical-grade diagnostics. Telemedicine integrations have incorporated these wearables into (RPM) systems, where algorithms analyze data streams for , such as irregular heart rates or glucose fluctuations, to alert providers in . For instance, -driven RPM platforms use to identify deviations from baseline vitals, enabling timely virtual consultations and reducing the need for in-person visits. These systems often leverage cloud-based processing to ensure and integration with electronic health records. Recent advancements include multiple FDA approvals in 2025 for AI-assisted diagnostics in wearables and RPM, such as enhanced systems and tools that support clinical decision-making in and . By mid-2025, the FDA had authorized over 1,200 AI-enabled medical devices, many focused on diagnostic imaging and monitoring, underscoring the growing regulatory acceptance of these technologies. Data remains a critical consideration, governed by the Health Insurance Portability and Accountability Act (HIPAA), which mandates safeguards for transmitted from wearables to covered entities like providers or insurers. However, HIPAA applies only when data is shared with such entities, leaving gaps for devices unless integrated into clinical workflows. The impact of these technologies is evident in clinical outcomes, with wearable alerts in RPM systems contributing to a reduction in hospital readmissions by approximately 30% among chronic disease patients through early intervention. For example, anomaly detection has enabled proactive management, lowering readmission rates for conditions like and by facilitating timely adjustments to care plans.

Societal Impacts and Challenges

Benefits and Economic Effects

Smart technologies deliver significant societal benefits by enhancing efficiency and accessibility across various domains. In smart buildings, integration of and (IoT) systems can achieve energy savings of approximately 22% through optimized control of heating, ventilation, and lighting, reducing operational costs and resource consumption for occupants. Additionally, assistive smart technologies, such as voice-activated devices and wearable sensors, improve independence and for individuals with disabilities by facilitating mobility, communication, and daily task management, thereby promoting inclusivity in both home and public environments. Economically, smart technologies drive substantial market growth and employment shifts. The global smart home market, a key segment of smart technologies, is projected to reach $174 billion in revenue by 2025, fueled by increasing adoption of connected devices and solutions. This expansion supports job creation in technology sectors. According to the World Economic Forum's Future of Jobs Report 2025, technological advancements including and are expected to create 170 million new jobs by 2030 while displacing 92 million, resulting in a net increase of 78 million jobs, particularly in roles related to , , and system maintenance. These gains are partially offset by automation-induced displacements, necessitating workforce reskilling to balance impacts. From a sustainability perspective, smart technologies contribute to environmental preservation by optimizing resource use and lowering emissions. Digital solutions, including smart grids and monitoring, have the potential to reduce global by 4-10% by 2030 and up to 20% by 2050 through efficient energy distribution and reduced waste in sectors like and . In agriculture, precision farming enabled by smart sensors and data analytics can increase crop yields by 15-20% while minimizing water and fertilizer inputs, supporting amid climate challenges. Case studies illustrate the regional economic boosts from smart technology hubs. In Silicon Valley, the concentration of smart tech innovation has generated an annual tech GDP of approximately $275 billion, attracting venture capital and fostering high-skill employment in areas like AI and IoT development, which in turn stimulates ancillary industries such as real estate and education.

Ethical and Privacy Concerns

Smart technologies, encompassing Internet of Things (IoT) devices and AI-integrated systems, pose profound privacy challenges through continuous data collection that facilitates potential surveillance. In smart homes, sensors and connected appliances routinely capture sensitive information such as location, voice patterns, and behavioral habits, often transmitting it to third parties without explicit user awareness. This ubiquity increases risks of mass surveillance, where aggregated data from multiple devices can profile individuals for commercial or unauthorized purposes. The European Union's Data Act (Regulation (EU) 2023/2854), which entered into force in January 2024 following its adoption in late 2023, addresses these issues by mandating fairer data sharing and user access rights for IoT-generated data, building on the GDPR to enhance protections in connected environments. Ethical dilemmas further complicate the deployment of smart technologies, particularly biases in AI-driven that amplify societal inequities. Facial recognition systems, integral to many smart security applications, exhibit higher error rates for non-white populations; a NIST evaluation of 189 algorithms revealed that false positives were 10 to 100 times more frequent for Black and Asian faces compared to white faces, stemming from unrepresentative training datasets. Such biases can lead to discriminatory outcomes in applications like or integration. Moreover, ensuring amid pervasive monitoring remains elusive, as users often encounter opaque policies and interconnected device ecosystems that obscure flows. highlights that traditional models fail in smart homes, where devices collect ambiently, prompting calls for dynamic, granular mechanisms to user autonomy. Regulatory responses have evolved to counter these privacy and ethical risks. California's Consumer Privacy Act (CCPA), enacted in 2018 and effective from 2020, empowers consumers with rights to know, delete, and of the sale of their , directly applying to smart device manufacturers and service providers handling IoT information. By 2025, global debates on AI ethics have intensified, with forums like the Global Forum on the Ethics of AI emphasizing the need for binding principles to govern bias mitigation and transparent algorithmic accountability in smart systems. These discussions underscore the tension between innovation and , advocating for harmonized international standards. From a philosophical standpoint, always-on smart devices erode individual by fostering environments of unrelenting and subtle behavioral nudging. This constant connectivity transforms personal spaces into data-rich zones where algorithmic predictions influence choices, diminishing the capacity for unmediated . Ethical analyses argue that such erosion undermines core human values like and , as users become passive subjects in systems designed for optimization over , raising questions about the societal cost of convenience.

Security Risks and Mitigation

Smart technologies face significant security risks from distributed denial-of-service (DDoS) attacks on interconnected networks, where like Mirai exploits weakly secured devices to create capable of overwhelming targets. In , the Mirai botnet infected over 600,000 devices, primarily cameras and routers with credentials, launching DDoS attacks that disrupted major services including DNS provider Dyn, causing widespread outages across and . Ransomware poses a growing threat to industrial systems within smart technologies, encrypting critical (OT) infrastructure and demanding payment to restore access, often leading to production halts and economic losses. For instance, attacks on firms like in 2023 and in 2019 forced manual operations and multimillion-dollar impacts, while a 46% surge in such incidents targeting industrial operators was reported in early 2025, with trojans like W32.Worm.Ramnit exploiting OT access points. Key vulnerabilities in smart technologies include weak encryption in common IoT protocols such as and CoAP, which often transmit data without adequate protection, enabling and man-in-the-middle attacks on sensitive information like device commands or user data. attacks further amplify risks, as demonstrated by the 2020 incident, where nation-state actors inserted into software updates for the platform, compromising up to 18,000 organizations including U.S. government agencies through tainted third-party components. To mitigate these threats, zero-trust architectures enforce continuous verification of all devices and users in ecosystems, assuming no inherent trust based on network location and applying micro-segmentation to limit lateral movement during breaches. technology facilitates secure by leveraging decentralized ledgers and cryptographic hashing to ensure tamper-resistant transactions among nodes, as proposed in frameworks combining proxy re-encryption for privacy-preserving exchanges. Regular updates address vulnerabilities by delivering patches for known exploits, with secure over-the-air mechanisms verifying update to prevent further manipulations. Standards from the National Institute of Standards and Technology (NIST) provide foundational guidelines for security, with the 2024 update to NIST IR 8425 establishing recommended cybersecurity requirements for consumer-grade router products, including baseline controls for , , and . Additionally, NIST's ongoing revisions to foundational activities for manufacturers, announced in 2025, emphasize ecosystem-wide risk assessments to align with evolving threats.

Future Directions

Emerging Trends

One prominent trend in smart technologies is the advancement toward wireless networks, which promise ultra-reliable low-latency communication (URLLC) to support mission-critical applications in ecosystems. Initial specifications and trials for are slated to commence in 2025, with prototypes demonstrating enhanced and integration of for dynamic , building on 5G's foundations to enable seamless connectivity for billions of devices. As of 2025, U.S. government policies are supporting development through requests for comments on spectrum and standards. Complementing this, (FL) is gaining traction as a privacy-preserving paradigm for distributed smart systems, where models are trained across edge devices without centralizing sensitive data, thus mitigating risks in healthcare wearables and smart homes. Recent implementations show FL significantly reducing communication overhead in scenarios while complying with regulations like GDPR. Sustainability efforts are increasingly embedding principles into design to curb (e-waste), projected to reach 82 million metric tons annually by 2030 if unchecked. Manufacturers are adopting modular and energy-efficient processors, such as those using recycled rare-earth materials, to extend device lifespans and facilitate easier upgrades, thereby reducing the environmental footprint of smart appliances. For instance, initiatives in 2025 emphasize AI-optimized in sensors and wearables, cutting energy consumption by 30-50% and diverting e-waste through models. Adoption patterns are shifting toward edge-to-cloud architectures, which process data locally at for decisions while leveraging scalability for complex in smart technologies. This model enhances latency-sensitive applications like autonomous , with increasing adoption for improved resilience. Concurrently, integrations are fostering virtual smart environments, where digital twins of physical systems enable immersive simulations for and remote collaboration. These platforms use / overlays to create interactive spaces, allowing users to monitor and control infrastructures virtually. Market dynamics in 2025 highlight robust growth in quantum-secure communications, driven by the need to protect smart networks against threats to classical . The sector is forecasted to expand at a CAGR of 31.8% from 2025 to 2030, reaching USD 5.40 billion, fueled by standards like those from NIST for securing data transmission. Early deployments in enterprise systems demonstrate (QKD) achieving bit-error rates below 1%, ensuring tamper-proof links for critical smart infrastructure.

Potential Innovations and Barriers

Potential innovations in smart technologies are poised to enhance autonomy, connectivity, and efficiency across consumer, industrial, and societal applications. Agentic AI, which enables autonomous systems to plan and execute multistep tasks using foundation models, represents a key advancement, with applications in robotics such as Tesla's Optimus humanoid robot featuring 22 degrees of freedom for complex manipulations and in IoT-enabled environments for predictive maintenance. Advanced connectivity innovations, including 5G-Advanced networks that have reached over 2.6 billion connections globally as of mid-2025 and projected to top 2.9 billion by year-end, along with early 6G standardization efforts, will support low-latency IoT ecosystems, exemplified by SpaceX's Starlink satellite constellation enabling direct-to-cell services for remote smart devices. Ambient invisible intelligence, integrating sensors and AI into everyday environments for seamless interactions, promises to transform smart homes and cities through real-time data processing without user intervention, as seen in emerging platforms for intuitive energy management. Edge and synergies further drive innovation by distributing workloads closer to data sources, reducing latency in systems like autonomous vehicles, where Waymo's platform has logged over 4 million rides in 2024 using edge for decision-making. Polyfunctional robots, capable of multitasking across environments, are advancing industrial automation, with models like Covariant's RFM-1 demonstrating human-like adaptability in warehouses. through augmented and interfaces will enable immersive interactions, such as dynamic AR overlays in healthcare wearables for patient monitoring. Energy-efficient computing innovations, including specialized semiconductors, address in devices by minimizing power consumption for always-on networks. Despite these advancements, several barriers impede widespread adoption of smart technologies. High implementation costs and infrastructure limitations, such as data center power constraints and supply chain delays for edge devices, hinder scalability, particularly in developing regions. Organizational resistance and skills gaps, including a shortage of expertise in AI governance, slow enterprise integration, with leaders often failing to steer initiatives effectively despite employee readiness. Privacy and ethical concerns, exacerbated by ambient intelligence's pervasive data collection, raise trust issues, necessitating robust opt-out mechanisms and alignment guardrails. Regulatory uncertainties and cybersecurity risks further complicate deployment; for instance, varying regional guidelines and vulnerabilities in connected networks, like satellite jamming, demand adaptive security measures. Financial and cultural barriers, including limited access to and resistance to change within organizations, compound these challenges, as evidenced in 4.0 applications where inadequate government support delays adoption. regulations and talent shortages in multicloud environments also restrict innovation in global smart systems, requiring concerted efforts in policy harmonization and workforce development to overcome them.

References

  1. [1]
    What is 'Smart' Technology? - Williams OIT
    Smart technology integrates computing and telecommunication into other technologies, enabling communication, automation, and remote operation.
  2. [2]
    Smart Device - an overview | ScienceDirect Topics
    Smart devices are defined as devices capable of being remotely controlled to switch on/off or regulate the use or generation of energy at their location of ...<|control11|><|separator|>
  3. [3]
    (PDF) Smart technology, overview, and regulatory framework
    A smart technologies system is integrated technologies used to monitor and manage environments or external system, improving human life and work, and accelerate ...
  4. [4]
    Smart city technologies, key components, and its aspects
    Smart cities use IoT, ICT, smart computing, and network technologies. Domains include smart health, buildings, transportation, and agriculture.
  5. [5]
    (PDF) Smart Technology: A Primer - Academia.edu
    Smart technologies integrate sensors, control units, and actuators for autonomy and efficiency in various applications. The Internet of Things (IoT) enhances ...Missing: definition | Show results with:definition<|control11|><|separator|>
  6. [6]
    Technical Foundations for Smart Connected Systems | NIST
    Apr 17, 2022 · The Smart Connected Systems Division (SCSD) has developed a series of foundational systems engineering frameworks, often published as NIST ...
  7. [7]
    Smart Technologies and IoT in Engineering: Innovations and ...
    Smart technologies and IoT are reshaping engineering with innovative applications. Learn about intelligent systems, smart infrastructure, and IoT's role in ...
  8. [8]
    (PDF) Introduction to Smart Technologies - ResearchGate
    Smart technologies include a variety of sensors, controls, and automated mechanisms, as well as networked data systems, all of which are intended to increase ...
  9. [9]
    Smart technologies | Internet Policy Review
    Sep 12, 2020 · Smart technologies are digital technologies of control, and the most important question will be whose control they facilitate. We will return to ...
  10. [10]
    Smart Manufacturing and Intelligent Manufacturing: A Comparative ...
    The work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify ...
  11. [11]
    What is Machine-to-Machine (M2M)? - TechTarget
    Aug 2, 2019 · This definition explains the meaning of machine-to-machine and explores how M2M enables automated communications between networked devices.Missing: predictive | Show results with:predictive
  12. [12]
    Fundamentals of Machine Learning for Predictive Data Analytics
    This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics.Algorithms, Worked Examples... · Hardcover · $90.00
  13. [13]
    What is IoT? - Internet of Things Explained - Amazon AWS
    Machine learning refers to the software and algorithms used to process data and make real-time decisions based on that data. These machine learning algorithms ...What Is Iot (internet Of... · How Does Iot Work? · What Is Aws Iot And How Can...Missing: foundations | Show results with:foundations
  14. [14]
    How AWS IoT works - AWS IoT Core - AWS Documentation
    AWS IoT provides cloud services and device support that you can use to implement IoT solutions. AWS provides many cloud services to support IoT-based ...Missing: algorithms | Show results with:algorithms
  15. [15]
    [PDF] 5G Cellular IoT NR IIoT - 3GPP
    Even lower cost than LTE-M. Extended coverage: 164 dB maximum coupling loss (at least for standalone). Long battery life.
  16. [16]
    https://www.wi-fi.org/discover-wi-fi/wi-fi-certified-6
  17. [17]
    Bluetooth Technology Overview
    Missing: 5G Wi- Fi
  18. [18]
    A Cloud-Based and RESTful Internet of Things Platform to Foster ...
    Jun 21, 2016 · In this paper, we propose key features for an interoperable, re-usable, elastic and secure smart grid architecture which is cloud-based and ...
  19. [19]
    1801: Punched cards control Jacquard loom | The Storage Engine
    In 1801, Joseph Jacquard used punched cards to control a loom, enabling complex patterns. Later, punched cards were used for data storage and input.
  20. [20]
    Norbert Wiener Issues "Cybernetics", the First Widely Distributed ...
    Cybernetics was also the first conventionally published book to discuss electronic digital computing. Writing as a mathematician rather than an engineer, ...
  21. [21]
    Alan Turing's Everlasting Contributions to Computing, AI and ...
    Jun 23, 2022 · Turing's computability work provided the foundation for modern complexity theory. This theory tries to answer the question “Among those ...
  22. [22]
    1947: Invention of the Point-Contact Transistor | The Silicon Engine
    In December 1947, John Bardeen and Walter Brattain achieved transistor action using germanium with two gold contacts, amplifying a signal up to 100 times.
  23. [23]
    1958: All Semiconductor "Solid Circuit" is Demonstrated
    On September 12, 1958, Jack Kilby of Texas Instruments built a circuit using germanium mesa p-n-p transistor slices he had etched to form transistor, capacitor ...
  24. [24]
    Milestones:Birthplace of the Internet, 1969
    Nov 28, 2023 · With the introduction of a highly-adaptive and robust technology for network access, the ARPANET formed the foundation of today's Internet. The ...
  25. [25]
    https://home.cern/science/computing/birth-web
  26. [26]
    First Smartphone Turns 20: Fun Facts About Simon - Time Magazine
    Aug 18, 2014 · IBM and BellSouth first showed Simon off in late 1992.​​ It was code-named “Angler” and was unveiled at the fall COMDEX convention in Vegas, but ...
  27. [27]
    Walmart and the Past, Present, and Future of RFID - AB&R
    Nov 20, 2023 · Walmart announced in 2003 that its top 100 suppliers must tag their pallets and cases starting by 2005. This announcement followed a successful ...
  28. [28]
    Android versions: A living history from 1.0 to 16 - Computerworld
    a release so ancient it didn't ...Android Version 11 · Android Version 14 · Android Version 15<|separator|>
  29. [29]
    Apple's Siri voice assistant based on extensive research - CNN
    Oct 5, 2011 · Apple's Siri voice assistant based on extensive research. Mark Milian, CNN. 4 min read. Updated 7:10 AM EDT, Wed October 5, 2011. Link Copied!
  30. [30]
    Could Songdo be the world's smartest city? | World Finance
    South Korea's purpose-built 'smart city', Songdo International Business District, is the largest private real estate development in history.
  31. [31]
    5G in 2020 | What's next for connectivity? | Deloitte UK
    Sep 7, 2020 · 5G has been rapidly rolled-out in China, with operators expected to spend $26 billion (£20 billion) deploying over 600,000 5G cells by the end ...
  32. [32]
    What Is Edge AI? | IBM
    Edge AI refers to the deployment of AI models directly on local edge devices to enable real-time data processing and analysis without reliance on cloud ...Missing: 2020s | Show results with:2020s
  33. [33]
    Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes ...
    In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors.
  34. [34]
    What is the Internet of Things (IoT)? - IBM
    IoT refers to a network of devices, vehicles, appliances and other physical objects that are embedded with sensors, software and network connectivity.What is the IoT? · Why is IoT important?
  35. [35]
    What Is the Internet of Things? - Oracle
    Internet of things, IoT, is when physical objects are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging ...
  36. [36]
    Kevin Ashton Describes "the Internet of Things"
    Kevin Ashton is an innovator and consumer sensor expert who coined the phrase “the Internet of Things” to describe the network connecting objects in the ...
  37. [37]
    Understanding IoT Architecture: Key Layers and Core Technologies ...
    Aug 8, 2024 · The layers include the Perception Layer, Network Layer, Processing Layer, Application Layer, and Business Layer [1][2]. Each layer builds ...Layers Of Iot Architecture · Use Case · Industrial Iot (iiot)<|control11|><|separator|>
  38. [38]
    6 IoT Architecture Layers and Components Explained - TechTarget
    Jul 2, 2025 · What are the 6 layers of IoT architecture? · 1. Physical/device layer · 2. Network layer · 3. Data/database layer · 4. Analytics/visualization layer.Having A Clearly Defined Iot... · What Are The Components Of... · What Are The 6 Layers Of Iot...
  39. [39]
    Number of connected IoT devices growing 14% to 21.1 billion globally
    Oct 28, 2025 · Number of connected IoT devices growing 14% to 21.1 billion globally in 2025. Estimated to reach 39 billion in 2030, a CAGR of 13.2% [...]Connected IoT device market... · Wi-Fi IoT · Bluetooth IoT · Cellular IoT
  40. [40]
    Samsung Family Hub™️ | Samsung US | Samsung USA
    Samsung Family Hub™ refrigerators keep your family connected anytime, anywhere. You can share pictures, videos and drawings with Google photos,² control ...
  41. [41]
    6 Ways Industrial IoT Can Benefit Modern Operations
    “IIoT enabled machinery can help optimize material and equipment flow for increased efficiency and yield.
  42. [42]
    MQTT Publish/Subscribe Architecture (Pub/Sub) - HiveMQ
    Rating 9.1/10 (64) Jun 6, 2023 · In this article, we will delve into the Pub/Sub architecture, also known as pub/sub, which is a messaging pattern in software architecture.
  43. [43]
    What is MQTT? - MQTT Protocol Explained - Amazon AWS
    An MQTT client establishes a connection with the MQTT broker. · Once connected, the client can either publish messages, subscribe to specific messages, or do ...
  44. [44]
    https://ieeexplore.ieee.org/document/10941512
  45. [45]
    https://ieeexplore.ieee.org/document/10112264
  46. [46]
    Optimize AI Models with TensorFlow Lite on Edge - Viso Suite
    Explore TensorFlow Lite for efficient AI on 4B+ devices. Learn how TFLite optimizes deep learning on mobile and edge with real-time, privacy-focused AI.What is TensorFlow Lite? · TensorFlow Lite Advantages · Pre-trained Models for...
  47. [47]
    [PDF] Predictive Maintenance using Machine Learning - arXiv
    The ML-based predictive approach analyses the live data and tries to find out the correlation between certain parameters to predict the system failure or ...
  48. [48]
    Predictive Maintenance for Smart Industrial Systems: A Roadmap
    PdM brings several potential benefits in terms of reliability, safety and maintenance costs among many other benefits.
  49. [49]
    Reinforcement-learning-aided adaptive control for autonomous ...
    This paper presents a deep reinforcement learning-aided controller for a 3-DOF autonomous vehicle with combined lateral and longitudinal dynamics.
  50. [50]
    Stable training via elastic adaptive deep reinforcement learning for ...
    Feb 26, 2024 · Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles.Results · Result Analysis · Methods
  51. [51]
    https://www.statista.com/outlook/cmo/smart-home/worldwide
  52. [52]
    [PDF] A review of smart building sensing system for better indoor ...
    Smart building sensing systems use sensors like photo, PIR, ambient temperature, and humidity sensors to manage energy, thermal, visual comfort, and air ...Missing: biometric | Show results with:biometric
  53. [53]
    Sensors and Systems for Physical Rehabilitation and Health ...
    Thus, different types of sensors can be used (e.g., GPS receiver, accelerometer, ECG, blood pressure, blood glucose, body temperature, and breathing sensor).
  54. [54]
    Wearable Sensors for Remote Health Monitoring - PubMed Central
    Jan 12, 2017 · The sensors are capable of measuring physiological signs such as electrocardiogram (ECG), electromyogram (EMG), heart rate (HR), body ...
  55. [55]
    [PDF] A Survey of Protocols and Standards for the Internet of Things
    Z-Wave. Z-Wave is a low power consumption MAC standard that was designed for home automation, but recently used in many IoT applications, including smart ...
  56. [56]
    Internet of Things Protocols and Standards
    Nov 30, 2015 · Z-Wave. Z-Wave is a low-power MAC protocol designed for home automation and has been used for IoT communication, especially for smart home ...
  57. [57]
    RFC 7252: The Constrained Application Protocol (CoAP)
    Summary of each segment:
  58. [58]
    Smart Zigbee/IEEE 802.15.4 MAC for wireless sensor multi-hop ...
    Therefore, in this paper, the Zigbee/IEEE 802.15.4 MAC protocol is modified to suit the beacon-based wireless sensor multi-hop mesh networks application. Such ...
  59. [59]
    Low-Power Wireless for the Internet of Things: Standards and ...
    Specifically, in order to meet reliability, low latency, and low- power consumption, which are the requirements of emerg- ing applications like Cyber Physical ...
  60. [60]
    Flawed Diamonds Make Perfect Quantum Sensors - IEEE Spectrum
    Feb 23, 2025 · Quantum sensors take the biggest roadblock for quantum computers—unwanted interference, or noise—and turn it into a strength.
  61. [61]
    Quantum technologies need big investments to deliver on their big ...
    Feb 26, 2025 · Quantum sensors are measuring both time and gravity with unprecedented precision. For instance, gravimeters for detecting earthquakes and ...
  62. [62]
    Introducing Philips hue: the world's smartest LED bulb, marking a ...
    Oct 29, 2012 · October 29, 2012 · Note: the hue introduction pack can be enlarged with up to 50 individual hue bulbs · RRP £179.
  63. [63]
    Should You Buy a Google Nest Thermostat? - Consumer Reports
    Sep 19, 2025 · When it debuted in 2011, the Nest Learning Thermostat wasn't the first internet-connected thermostat, but it defined the technology for many ...
  64. [64]
    As Days Cool Down, We Tested How Much Money Smart ... - CNET
    Oct 12, 2025 · Google Nest's research, for instance, showed that users of the Nest Learning Thermostat saved an average of 12% to 15% per year without making ...
  65. [65]
    Home Security Camera Reviews and Lab Tests | PCMag
    The Google Nest Cam is a battery-powered indoor/outdoor home security camera that's easy to install and delivers crisp HD video and intelligent alerts.
  66. [66]
    Best Home Security Cameras of 2025 - Security.org
    The Best Home Security Cameras of 2025 · 1 . SimpliSafe · 2 . ADT · 3 . Ring · 4 . Wyze Cam · 5 . Lorex · 6 . Arlo · 7 . Nest · 8 . Blink Camera.
  67. [67]
    https://www.buckleandband.com/blogs/apple-watch-tips/a-brief-history-of-the-apple-watch-2015-2023
  68. [68]
    What Is Alexa? Learn How Amazon's Voice Assistant Works - eufy US
    Jun 26, 2025 · Introduced in 2014 with the launch of the first Echo speaker, Alexa ... Amazon's ecosystem and now millions of third-party devices. It ...
  69. [69]
    Smart Home Market Value & Industry Growth (2025-2032)
    Nov 7, 2024 · The worldwide smart home market is valued at $149.43 billion in 2025. The smart home space is expected to climb to $633.2 billion by 2032 at a compound annual ...Smart Home Market Size · Smart Home Market Growth... · Smart Home Market...Missing: penetration | Show results with:penetration<|separator|>
  70. [70]
    Smart Home Statistics 2025: Smart Device Usage, Spending, etc.
    Jul 30, 2025 · 63%: Share of US households with at least one smart home device in 2025. 38%: Growth in smart security systems installed in US homes compared to ...Popular Smart Home Devices · Consumer Demographics and... · Security Trends
  71. [71]
    Smart Home Statistics and Facts (2025) - Market.us Scoop
    The number of active households using Smart Home technology is expected to reach 93.59 million by 2027. Representing a household penetration rate of 68.6% ...
  72. [72]
    Nest Hub Smart Displays for your Home - Google Store
    With Nest Hub smart displays, you can control your home, entertain with ease, and coordinate and connect. Discover the right home hub for your family.
  73. [73]
    Predix at GE: Machine Learning in Industrial IoT
    Nov 12, 2018 · Predix is GE's software to harvest data from industrial IoT, using machine learning to optimize systems, maintenance, and usage, and build ...
  74. [74]
    GE's Big Bet on Data and Analytics - MIT Sloan Management Review
    Feb 18, 2016 · ... Predix1 software, a cloud-based platform for creating Industrial Internet applications. GE has long had the ability to collect machine data ...Missing: IIoT | Show results with:IIoT
  75. [75]
    Enhancing supply chain performance using RFID technology and ...
    RFID data is widely used in tool tracking, process management, and access control. 3.1. Application of RFID in SCM optimization in the light of industry 4.0.
  76. [76]
    Optimizing energy production with the latest smart grid technologies
    Smart grid technology has countless benefits, including increased grid efficiency and reliability and easy integration with renewable energy sources.
  77. [77]
    Why Predictive Maintenance Is Manufacturing's Next Big Advantage
    Jun 19, 2025 · Predictive maintenance can cut downtime by 50% and save 25% on costs. Learn why it's now a strategic edge for UK and EU manufacturers in ...
  78. [78]
    10 Core Components of Industry 4.0 | Oracle Europe
    Dec 19, 2023 · Engineering conglomerate Siemens' electronics plant in Amberg, Germany. ... production and push defects to nearly zero. Still, as of 2020 ...
  79. [79]
    Scalability challenges in IoT – how to overcome network, security ...
    IoT works great in a pilot. But what happens when you try to scale it across 10,000 devices, multiple locations, and terabytes of data?<|separator|>
  80. [80]
    Continuous Glucose Monitoring Devices: Past, Present, and Future ...
    Early in 2018, the Dexcom G6 became the first CGM to be approved by the FDA for integration into AID systems.
  81. [81]
    The History, Evolution and Future of Continuous Glucose Monitoring ...
    In 2022, Dexcom's G7 was FDA-approved for all types of diabetes above 2 years of age, including a better accuracy (8.2% overall mean absolute relative ...
  82. [82]
    [PDF] DEN180042.pdf - accessdata.fda.gov
    Apple Watch may be unable to collect data when Apple Watch is in close vicinity to strong electromagnetic fields (e.g. electromagnetic anti-theft systems, metal ...
  83. [83]
    Apple unveils Watch Series 4 with FDA-approved ECG
    Sep 12, 2018 · The irregular heart rhythm alert has also received FDA clearance. Both of these features will be available to US customers later this year and ...
  84. [84]
    What the Apple Watch's FDA clearance actually means - The Verge
    Sep 13, 2018 · The US Food and Drug Administration cleared two new features for the Apple Watch Series 4. One is an advanced method of monitoring the heart called an ...
  85. [85]
    Enhancing remote patient monitoring with AI-driven IoMT and cloud ...
    Jul 5, 2025 · ... monitor a patient's abnormal health conditions. From the acquired ... Anomaly Detection in Cyber-Physical Systems-enabled IoT Networks Vol.
  86. [86]
    Editorial: Bench to bedside: AI and remote patient monitoring - PMC
    Feb 27, 2025 · Paper (Shaikh et al.) addresses this issue by proposing an anomaly-based intrusion detection system integrating Random Forest for feature ...
  87. [87]
    The role of AI in remote patient monitoring - LeewayHertz
    This analysis may involve identifying trends, detecting anomalies, and assessing the patient's overall health status based on the collected data. Alerts and ...
  88. [88]
    Artificial Intelligence-Enabled Medical Devices - FDA
    Jul 10, 2025 · AI-Enabled Medical Devices List ; 05/09/2025, K251110, EPIQ Series Diagnostic Ultrasound Systems; Affiniti Series Diagnostic Ultrasound Systems ...Artificial Intelligence · 510(k) Premarket Notification · Software
  89. [89]
    AI Medical Devices: 2025 Status, Regulation & Challenges
    Analysis of AI medical devices in 2025. Learn about market growth, 950+ FDA-cleared devices, new regulations like the EU AI Act, and key clinical ...
  90. [90]
    Does HIPAA Apply to Wearable Health Technology - MedSafe
    Dec 1, 2023 · HIPAA applies to wearable health tech when healthcare providers/insurers collect data. Some manufacturers/app developers may also be covered as ...
  91. [91]
    [PDF] Wearable Technology and HIPAA: What Providers Need to Know
    HIPAA generally provides limited protection for wearable data, but applies when data is shared with a covered entity or business associate, especially if sent ...
  92. [92]
    The HIPAA Compliance of Wearable Technology | blogMD - MicroMD
    Jan 16, 2025 · HIPAA doesn't apply to personal use of wearable tech, but applies when a provider receives data, especially if it interfaces with an EHR system.
  93. [93]
    (PDF) The Impact of Wearable Technology on Health Monitoring
    Oct 12, 2025 · The table above highlights that wearable devices have a profound effect on reducing hospital readmissions,. particularly among chronic ...
  94. [94]
    Efficacy of Remote Health Monitoring in Reducing Hospital ... - NIH
    Sep 13, 2024 · The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, and total hospital stay days at 3 and 6 months after ...
  95. [95]
    https://energyinformatics.springeropen.com/articles/10.1186/s42162-025-00592-8
  96. [96]
    Assistive technology - World Health Organization (WHO)
    Jan 2, 2024 · Assistive products help maintain or improve an individual's functioning related to cognition, communication, hearing, mobility, self-care and ...
  97. [97]
    Smart Home Technology: A Game-Changer for Disability ... - LDRFA
    Smart home technology can greatly improve independence and maximize the quality of life of people with disabilities. Learn history, uses and applications.
  98. [98]
    Digital technologies can cut global emissions by 20%. Here's how
    May 23, 2022 · Rapid adoption of digital technologies could help significantly reduce global emissions in sectors such as energy, materials and mobility.
  99. [99]
    Enhancing precision agriculture: A comprehensive review of ...
    These technologies have been thoroughly tested to show how they can improve crop yield (15-20%), reduce overall investment (25-30%), and make farming more ...
  100. [100]
    The Most Exciting Tech Developments in Silicon Valley for 2025
    Dec 26, 2024 · Home to over 6,600 tech companies, this region generates an astounding $275 billion in tech GDP annually, cementing its role as a powerhouse of ...
  101. [101]
    Data Act explained | Shaping Europe's digital future - European Union
    Sep 12, 2025 · The Data Governance Act became applicable in September 2023. While the Data Governance Act increases trust in voluntary data-sharing mechanisms, ...
  102. [102]
    NIST Study Evaluates Effects of Race, Age, Sex on Face ...
    Dec 19, 2019 · A new NIST study examines how accurately face recognition software tools identify people of varied sex, age and racial background.
  103. [103]
    than just informed: The importance of consent facets in smart homes
    May 11, 2024 · Data collection without proper consent is a growing concern as smart home devices gain prevalence. It is especially difficult to obtain ...
  104. [104]
    California Consumer Privacy Act (CCPA)
    Mar 13, 2024 · The California Consumer Privacy Act of 2018 (CCPA) gives consumers more control over the personal information that businesses collect about them.
  105. [105]
    Global Forum on the Ethics of AI - Artificial Intelligence - UNESCO
    The Third Edition. From 24 to 27 June 2025, the 3rd UNESCO Global Forum on the Ethics of Artificial Intelligence will take place in Bangkok, Thailand.
  106. [106]
    Inevitable challenges of autonomy: ethical concerns in personalized ...
    Oct 3, 2024 · This essential discrepancy means that users' personal autonomy will always be vulnerable to erosion.
  107. [107]
    What is the Mirai Botnet? - Cloudflare
    Learn how Mirai malware turns IoT devices running on the ARC processor and the Linux OS, into botnets. Mirai is commonly used to launch DDoS attacks, ...
  108. [108]
    Heightened DDoS Threat Posed by Mirai and Other Botnets - CISA
    Oct 17, 2017 · An IoT botnet powered by Mirai malware created the DDoS attack. The Mirai malware continuously scans the Internet for vulnerable IoT devices.
  109. [109]
    The Story of the Mirai Botnet - Radware
    The Mirai botnet is a malware designed to hijack Internet of Things (IoT) devices and turn them into remotely controlled “bots” capable of launching ...
  110. [110]
    Biggest Manufacturing Industry Cyber Attacks | Arctic Wolf
    Mar 22, 2024 · We're looking at 10 manufacturing cyber attacks that highlight what's at risk, and what threat actors are targeting, when it comes to this massive industry.Clorox · Norsk Hydro · Mondelez International
  111. [111]
    Ransomware Attacks Targeting Industrial Operators Surge 46% in ...
    Jun 4, 2025 · Trojans exploiting industrial access: A dangerous trojan targeting OT systems – W32.Worm.Ramnit – accounted for 37% of files blocked by ...Missing: examples | Show results with:examples
  112. [112]
    Top IoT Device Vulnerabilities: How To Secure IoT Devices - Fortinet
    1. Weak/hardcoded passwords · 2. Insecure networks · 3. Insecure ecosystem interfaces · 4. insecure update mechanisms · 5. Insecure or outdated components · 6. Lack ...
  113. [113]
    Top 5 Most Commonly Used IoT Protocols and Their Security Issues
    Jun 4, 2024 · Insufficient Encryption. It's easy to forget that MQTT sometimes requires encryption, leaving sensitive data vulnerable. Always use TLS ...
  114. [114]
    SolarWinds Supply Chain Attack | Fortinet
    On March 26, 2020, SolarWinds began distributing Orion updates that contained the hackers' malicious code. The malware spread as thousands of SolarWinds ...
  115. [115]
    SolarWinds Supply Chain Attack Uses SUNBURST Backdoor
    Dec 13, 2020 · FireEye discovered a supply chain attack trojanizing SolarWinds Orion business software updates in order to distribute malware we call SUNBURST.
  116. [116]
    Advanced Persistent Threat Compromise of Government Agencies ...
    Apr 15, 2021 · The threat actor has been observed leveraging a software supply chain compromise of SolarWinds Orion products[2 ] (see Appendix A). The ...
  117. [117]
    How to apply a Zero Trust approach to your IoT solutions - Microsoft
    May 5, 2021 · Enter Zero Trust, the security model that assumes breach and treats every access attempt as if it originates from an open network. In October ...
  118. [118]
    [PDF] Zero Trust Architecture - NIST Technical Series Publications
    Zero trust focuses on protecting resources (assets, services, workflows, network accounts, etc.), not network segments, as the network location is no longer.
  119. [119]
    Blockchain based secure IoT data sharing framework for SDN ...
    A blockchain-based data sharing framework that combines a PRE scheme is introduced for secure device-to-device communication in smart communities.
  120. [120]
    How Regular IoT Firmware Updates Can Prevent Security Breaches
    Jul 31, 2024 · Outdated firmware is vulnerable to hackers, and its absence puts devices at risk of security breaches, data breaches, and even health risks.What is IoT Firmware? · Importance of Regular... · The Process of Implementing...
  121. [121]
    IoT Firmware Security and Update Mechanisms: A Deep Dive
    Jun 10, 2025 · Firmware updates are crucial to fix software bugs, patch vulnerabilities, or add new security features. Equally important is ensuring these updates are secure ...Key Challenges With IoT... · Consequences of Insecure IoT...
  122. [122]
    Cybersecurity for IoT Program Published NIST IR 8425A | CSRC
    NIST Cybersecurity for IoT Program Publishes NIST IR 8425A, Recommended Cybersecurity Requirements for Consumer-Grade Router Products September 10, 2024.
  123. [123]
    NIST Cybersecurity for IoT Program
    NIST's Cybersecurity for the Internet of Things (IoT) program supports the development and application of standards, guidelines, and related tools.Consumer IoT Cybersecurity · IOT Advisory Board · IoT Federal Working Group
  124. [124]
    Updating Foundational Activities for IoT Product Manufacturers | NIST
    Sep 30, 2025 · Over the past few months, NIST has been revising and updating Foundational Activities for IoT Product Manufacturers (NIST IR 8259 Revision 1 ...
  125. [125]
    6G will make ubiquitous cellular connectivity a reality | Fierce Network
    Aug 11, 2025 · 6G's defining characteristics are order-of-magnitude increases in speed, reliability, coverage and spectral efficiency beyond those of 5G, ...
  126. [126]
    6G Wireless Networks - Intel
    Initial work on 6G wireless network specifications will begin in 2025, and this NextG is expected to launch in 2030. Future 6G networks will integrate new ...Missing: trials | Show results with:trials
  127. [127]
    [PDF] FCC TAC 6G Working Group Report 2025
    Aug 5, 2025 · Operational Efficiency: 6G technologies are expected to improve network automation, energy efficiency, and resource management, helping ...
  128. [128]
    Federated Learning for Privacy-Preserving AI
    Dec 1, 2020 · FL enables us to build cross-enterprise, cross-data and cross-domain AI applications while complying with data protection laws and regulations.
  129. [129]
    Enabling Privacy-Preserving Edge AI: Federated Learning ...
    Federated Learning (FL) presents a compelling solution to this dilemma, enabling decentralized data processing without compromising privacy.
  130. [130]
    Advancements in E-waste recycling technologies - ScienceDirect.com
    The world stands on the brink of an electronics waste (e-waste) revolution, fueled by a drive for sustainability and technological advancement.
  131. [131]
    https://ieeexplore.ieee.org/document/10622150/
  132. [132]
    How Does Green Computing Reduce E-Waste? → Question
    Apr 6, 2025 · One of the primary ways green computing reduces e-waste is by extending the lifespan of existing devices. This can be achieved through several strategies.
  133. [133]
    What is Edge to Cloud? | HPE
    Edge to cloud refers to the fact that enterprise data is no longer confined to the data center; It is being generated at the edge in ever-growing amounts.How Can Organizations Manage... · How Does An Edge-To-Cloud... · Why Is An Edge-To-Cloud...
  134. [134]
    Edge Computing Trends: Adoption, Challenges, and Future Outlook
    Jul 15, 2025 · Microsoft Azure IoT Edge is the most widely used edge platform, though organizations adopt diverse solutions. • The hybrid cloud-edge model ...
  135. [135]
    Towards Smart Manufacturing Metaverse via Digital Twinning ... - arXiv
    Oct 18, 2025 · In addition, social metaverse facilitates the planning and control of manufacturing activities in the networked virtual environment. This ...
  136. [136]
    Metaverse And Smart City Integration - Consensus
    The integration of the metaverse with smart cities presents a transformative opportunity to enhance urban living by merging digital and physical realms.<|separator|>
  137. [137]
    Quantum Communication Market Size, Share, & Growth Forecast ...
    The global quantum communication market size was estimated at USD 0.74 billion in 2024 and is projected to reach USD 5.54 billion by 2030, growing at a CAGR ...
  138. [138]
    Quantum Communication Market Size | Industry Report, 2030
    The global quantum communication market is expected to grow at a compound annual growth rate of 31.8% from 2025 to 2030 to reach USD 5.40 billion by 2030. Which ...
  139. [139]
    Quantum Communications Market Size, Statistics Report 2034
    The quantum communications market size was estimated at USD 951.2 million in 2024 and is expected to grow at a CAGR of 28.3% between 2025 and 2034, ...
  140. [140]
    None
    Summary of each segment:
  141. [141]
    Gartner's Top 10 Strategic Technology Trends for 2025
    Oct 21, 2024 · What are the top technology trends for 2025? · Agentic AI · Post-quantum Cryptography · Spatial Computing · AI Governance Platforms · Ambient ...Missing: smart | Show results with:smart
  142. [142]
    Address the Hidden Barriers to AI and GenAI Adoption - Gartner
    Aug 15, 2025 · These barriers stem from disconnects between employees and executives, causing AI initiatives to fail due to a lack of employee adoption.Access Research · Gartner Research: Trusted... · Pick The Right Tools And...
  143. [143]
    A Managerial Insight into Technology Non-Adoption - IEEE Xplore
    Key hindrances include high implementation costs, limited market data access, organizational resistance to innovation, and external environmental factors. The ...
  144. [144]
    Barriers to Adoption Industry 4.0 in Textile Sector - IEEE Xplore
    Aug 19, 2025 · The research identifies key driving factors such as technological infrastructure limitations and inadequate government support, while financial ...<|control11|><|separator|>