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Smart object
Smart object
Smart object
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A smart object is an object that enhances the interaction with not only people but also with other smart objects. Also known as smart connected products or smart connected things (SCoT), they are products, assets and other things embedded with processors, sensors, software and connectivity that allow data to be exchanged between the product and its environment, manufacturer, operator/user, and other products and systems. Connectivity also enables some capabilities of the product to exist outside the physical device, in what is known as the product cloud. The data collected from these products can be then analysed to inform decision-making, enable operational efficiencies and continuously improve the performance of the product.

It can not only refer to interaction with physical world objects but also to interaction with virtual (computing environment) objects. A smart physical object may be created either as an artifact or manufactured product or by embedding electronic tags such as RFID tags or sensors into non-smart physical objects. Smart virtual objects are created as software objects that are intrinsic when creating and operating a virtual or cyber world simulation or game. The concept of a smart object has several origins and uses, see History. There are also several overlapping terms, see also smart device, tangible object or tangible user interface and Thing as in the Internet of things.

History

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In the early 1990s, Mark Weiser, from whom the term ubiquitous computing originated, referred to a vision "When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial",[1][2] Although Weiser did not specifically refer to an object as being smart, his early work did imply that smart physical objects are smart in the sense that they act as digital information sources. Hiroshi Ishii and Brygg Ullmer[3] refer to tangible objects in terms of tangibles bits or tangible user interfaces that enable users to "grasp & manipulate" bits in the center of users' attention by coupling the bits with everyday physical objects and architectural surfaces.

The smart object concept was introduced by Marcelo Kallman and Daniel Thalmann[4] as an object that can describe its own possible interactions. The main focus here is to model interactions of smart virtual objects with virtual humans, agents, in virtual worlds. The opposite approach to smart objects is 'plain' objects that do not provide this information. The additional information provided by this concept enables far more general interaction schemes, and can greatly simplify the planner of an artificial intelligence agent.[4]

In contrast to smart virtual objects used in virtual worlds, Lev Manovich focuses on physical space filled with electronic and visual information. Here, "smart objects" are described as "objects connected to the Net; objects that can sense their users and display smart behaviour".[5]

More recently in the early 2010s, smart objects are being proposed as a key enabler for the vision of the Internet of things.[6] The combination of the Internet and emerging technologies such as near field communications, real-time localization, and embedded sensors enables everyday objects to be transformed into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of things and enable novel computing applications.[6] In 2018, one of the world's first smart houses was built in Klaukkala, Finland in the form of a five-floor apartment block, using the Kone Residential Flow solution created by KONE, allowing even a smartphone to act as a home key.[7][8]

Characteristics

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Although we can view interaction with physical smart object in the physical world as distinct from interaction with virtual smart objects in a virtual simulated world, these can be related. Poslad[2] considers the progression of: how

  • humans use models of smart objects situated in the physical world to enhance human to physical world interaction; versus how
  • smart physical objects situated in the physical world can model human interaction in order to lessen the need for human to physical world interaction; versus how
  • virtual smart objects by modelling both physical world objects and modelling humans as objects and their subsequent interactions can form a predominantly smart virtual object environment.

Smart physical objects

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The concept smart for a smart physical object simply means that it is active, digital, networked, can operate to some extent autonomously, is reconfigurable and has local control of the resources it needs such as energy, data storage, etc.[2] Note, a smart object does not necessarily need to be intelligent as in exhibiting a strong essence of artificial intelligence—although it can be designed to also be intelligent.

Physical world smart objects can be described in terms of three properties:[6]

  • Awareness: is a smart object's ability to understand (that is, sense, interpret, and react to) events and human activities occurring in the physical world.
  • Representation: refers to a smart object's application and programming model—in particular, programming abstractions.
  • Interaction: denotes the object's ability to converse with the user in terms of input, output, control, and feedback.

Based upon these properties, these have been classified into three types:[6]

  • Activity-Aware Smart Objects: Are objects that can record information about work activities and its own use.
  • Policy-Aware Smart Objects: Are objects that are activity-aware Objects can interpret events and activities with respect to predefined organizational policies.
  • Process-Aware Smart Objects: Processes play a fundamental role in industrial work management and operation. A process is a collection of related activities or tasks that are ordered according to their position in time and space.

Smart virtual objects

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For the virtual object in a virtual world case, an object is called smart when it has the ability to describe its possible interactions.[4] This focuses on constructing a virtual world using only virtual objects that contain their own interaction information. There are four basic elements to constructing such a smart virtual object framework.[4]

  • Object properties: physical properties and a text description
  • Interaction information: position of handles, buttons, grips, and the like
  • Object behavior: different behaviors based on state variables
  • Agent behaviors: description of the behavior an agent should follow when using the object

Some versions of smart objects also include animation information in the object information, but this is not considered to be an efficient approach, since this can make objects inappropriately oversized.[9]

Categorization

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The terms smart, connected product or smart product can be confusing as it is used to cover a broad range of different products, ranging from smart home appliances (e.g., smart bathroom scales or smart light bulbs) to smart cars (e.g., Tesla). While these products share certain similarities, they often differ substantially in their capabilities. Raff et al. developed a conceptual framework that distinguishes different smart products based on their capabilities, which features 4 types of smart product archetypes (in ascending order of "smartness").[10]

  • Digital
  • Connected
  • Responsive
  • Intelligent

Advantages

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Smart, connected products have three primary components:[11]: 67 

  • Physical – made up of the product's mechanical and electrical parts.
  • Smart – made up of sensors, microprocessors, data storage, controls, software, and an embedded operating system with enhanced user interface.
  • Connectivity – made up of ports, antennae, and protocols enabling wired/wireless connections that serve two purposes, it allows data to be exchanged with the product and enables some functions of the product to exist outside the physical device.

Each component expands the capabilities of one another resulting in "a virtuous cycle of value improvement".[11] First, the smart components of a product amplify the value and capabilities of the physical components. Then, connectivity amplifies the value and capabilities of the smart components. These improvements include:

  • Monitoring of the product's conditions, its external environment, and its operations and usage.
  • Control of various product functions to better respond to changes in its environment, as well as to personalize the user experience.
  • Optimization of the product's overall operations based on actual performance data, and reduction of downtimes through predictive maintenance and remote service.
  • Autonomous product operation, including learning from their environment, adapting to users' preferences and self-diagnosing and service.[12]

The Internet of things (IoT)

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Main article: Internet of things

The Internet of things is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment.[13] The phrase "Internet of things" reflects the growing number of smart, connected products and highlights the new opportunities they can represent. The Internet, whether involving people or things, is a mechanism for transmitting information. What makes smart, connected products fundamentally different is not the Internet, but the changing nature of the 'things'.[11]: 66  Once a product is smart and connected to the cloud, the products and services will become part of an interconnected management solution. Companies can evolve from making products to offering more complex, higher-value offerings within a "system of systems".[14][15]

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Smart object

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from Grokipedia
A smart object, also known as an intelligent object, is an autonomous physical or digital device equipped with sensing, processing, and networking capabilities that enable it to collect data from its environment, make decisions, and interact with other objects, systems, or users within the (IoT) framework. These objects integrate technologies such as sensors for , microprocessors for local or cloud-based , actuators for executing actions, and communication protocols like , , or to facilitate connectivity. Key characteristics of smart objects include their ability to operate independently or collaboratively in networks, often incorporating energy-efficient designs such as battery power or harvesting mechanisms to ensure prolonged functionality. They can range from everyday items like fitness trackers and smart thermostats to industrial tools such as sensors, enhancing efficiency through real-time monitoring and . In IoT ecosystems, smart objects form interconnected networks that exchange data to support applications in diverse sectors, including smart homes, healthcare, , and Industry 4.0. The evolution of smart objects has been driven by advancements in embedded AI, low-power wide-area networks (LPWAN), and , allowing them to process complex algorithms on-device while minimizing latency and . For instance, in smart cities, they enable traffic optimization via geolocation sensors, while in healthcare, connected devices like monitors provide remote patient data for timely interventions. Despite their benefits in and , challenges such as data , interoperability standards, and cybersecurity remain critical considerations in their deployment.

Fundamentals

Definition

A smart object is a physical or virtual equipped with embedded processors, sensors, software, and connectivity features that enable it to perceive, process data, interact autonomously with users, environments, or other objects, and adapt behaviors accordingly. This conceptualization of smart objects draws from the paradigm of , which envisions seamless integration of computational elements into everyday environments to support intelligent interactions. While IoT devices emphasize networked and data exchange across systems, smart objects—as building blocks of the IoT—prioritize localized intelligence, unique identity, and self-directed decision-making embedded within the entity itself. Basic forms include a physical RFID-tagged inventory item that autonomously identifies its location, senses environmental conditions, and communicates status updates, or a virtual counterpart like a that replicates and adapts to a physical object's in a simulated environment.

Key Components

Smart objects rely on a combination of hardware elements to enable their functionality, including embedded processors such as microcontrollers—often ARM-based chips—that serve as the central computing unit for processing data and executing tasks. These processors are typically low-power and optimized for resource-constrained environments. Sensors provide essential data input by detecting environmental changes, such as or motion, while actuators deliver physical responses, like adjusting a mechanism based on processed information. Additionally, a reliable power source, such as batteries or energy harvesters, ensures operational longevity in deployed settings. On the software side, smart objects incorporate layered architectures tailored for efficiency. Real-time operating systems (RTOS), such as or Zephyr, manage tasks in constrained hardware environments by prioritizing low latency and minimal resource usage. Firmware provides the foundational autonomy, controlling hardware interactions and enabling independent operation through embedded code that handles core functions like and basic decision-making. Application programming interfaces (APIs) facilitate interaction between the object's internal components and external systems, allowing seamless integration with broader networks or applications. Connectivity features are integral, featuring wireless modules like (BLE) or that enable data exchange with other devices or cloud services. These modules support bidirectional communication while incorporating techniques, such as sleep modes and duty cycling, to extend battery life and maintain efficiency in intermittent connectivity scenarios. The integration model of these components centers on , where local processing occurs on the device itself to facilitate real-time decision-making without constant reliance on remote servers. In this architecture, sensors feed data to the embedded processor, which runs and RTOS-managed algorithms to analyze inputs and trigger actuators, while connectivity modules handle selective offloading of complex tasks. This can be visualized as a layered stack: hardware at the base supporting software execution, with edge processing bridging local autonomy and networked collaboration, reducing latency to milliseconds for responsive applications. Such models apply primarily to physical smart objects.

Historical Development

Origins

The conceptual foundations of smart objects emerged in the late amid broader advancements in and human-computer interaction, laying the groundwork for objects that could perceive, respond, and integrate seamlessly into everyday environments. A pivotal influence was Mark Weiser's vision of , developed during his tenure as chief technologist at PARC in the early 1990s, which envisioned computing embedded in ordinary objects to enhance human activities without drawing attention to itself. Weiser introduced the notion of "," co-authored with John Seely Brown, emphasizing interfaces that promote peripheral awareness and blend into the background of daily life, allowing objects to support users subtly rather than demanding focus. Early theoretical influences on smart objects drew from 1990s explorations in and agent-based systems, building on prior ideas of responsive environments. Notably, Nicholas Negroponte's concepts from The Architecture Machine (1970), which evolved through his leadership of the founded in 1985, proposed machine-mediated dialogues between humans and their surroundings, fostering intelligent environments where computational elements adapt to user needs in architectural and spatial contexts. These ideas influenced research by highlighting how objects could exhibit agency-like behaviors, anticipating virtual systems where inanimate elements interact dynamically. In the pre-IoT era, the work by Marcelo Kallmann and Daniel Thalmann formalized early models for smart objects in virtual simulations, introducing a framework where objects encode their own interaction possibilities and behavioral responses to agents. Their approach established procedural models for object-agent interactions, enabling realistic simulations of grasping, manipulation, and environmental adaptation without exhaustive predefined animations. This development marked a shift toward objects with intrinsic "intelligence" in digital realms, driven by the need to automate complex interactions efficiently. The initial motivations for these conceptual origins stemmed from desires to enhance human-object interactions and introduce into daily life, addressing limitations in traditional that isolated technology from natural environments. These ideas evolved into the practical frameworks of modern IoT, as explored in subsequent milestones.

Key Milestones

In 1999, coined the term "" during a presentation at , envisioning a network of interconnected everyday objects equipped with RFID technology for automatic identification and exchange, which laid the groundwork for smart object integration. In the early 2000s, conceptual foundations for smart objects began to take shape, as explored net-connected objects in his 2001 book The Language of New Media, describing examples such as internet-accessible coffee machines and robots that enabled remote interaction and control. Parallel to this, the adoption of (RFID) tags and sensor networks gained momentum in supply chains, facilitating automated tracking and exchange for inventory management during the decade. The 2010s witnessed the explosive growth of the (IoT), propelling smart object development into mainstream applications. In 2013, forecasted that the installed base of IoT devices—excluding PCs, tablets, and smartphones—would expand to 26 billion units by 2020, underscoring the scale of connectivity anticipated. That momentum was further amplified in 2014 by Michael E. Porter and James E. Heppelmann's article, which detailed how smart, connected products, enabled by sensors and networks, were reshaping competitive landscapes across industries through enhanced functionality, reliability, and utilization. A key practical advancement came in 2018, as illustrated in a study on smart homes integrating cyber-physical systems to enable seamless physical-virtual interactions, such as capacitive sensors on doors triggering alarms on smart TVs and virtual sensors pulling external data for real-time context-aware responses. Following 2020, scholarly efforts sought to consolidate the field's fragmented understanding, as exemplified by Raff et al.'s analysis in the Journal of Product Innovation Management, which synthesized prior work into a hierarchical categorization of smart products as digital (IT-equipped with data handling), connected (networked for exchange), responsive (sensing and adapting in real time), and intelligent (autonomous decision-making). This framework highlighted the increasing but divergent research trends up to 2019.

Types and Categorization

Smart Physical Objects

Smart physical objects are tangible entities augmented with embedded technologies for sensing and actuation, enabling them to perceive and respond to their physical surroundings. These objects integrate hardware components such as sensors to detect environmental changes and actuators to execute physical actions, transforming everyday items into interactive systems within the (IoT). For instance, wearable fitness trackers monitor physiological data like and movement, while industrial machinery incorporates sensors for operational oversight and . Key attributes of smart physical objects include being active, meaning self-powered through batteries or to operate independently; digital, with onboard processing for and computation; networked, facilitating communication via protocols like or ; autonomous, allowing limited decision-making without constant human input; and reconfigurable, supporting software updates to adapt behaviors or functions over time. These traits enable the objects to function as building blocks in IoT ecosystems, bridging the physical and digital domains. Within Gerd Kortuem's 2010 framework, smart physical objects are categorized by awareness levels, including policy-aware objects that enforce predefined rules, such as smart locks that grant access based on user credentials and policies, and process-aware objects that adapt to operational workflows, like assembly line robots that adjust to production variations for efficiency. This framework highlights how such objects support specialized interactions in real-world applications. Smart physical objects interact with their environments primarily by perceiving inputs through sensors—such as , motion, or detectors—and responding via actuators that perform actions like opening mechanisms or adjusting machinery. This sensory-actuation loop allows for real-time adaptation, for example, in tools that monitor worker or equipment that self-regulates based on detected anomalies, ensuring seamless material and environmental engagement. Unlike their virtual counterparts, these objects are inherently tied to physical hardware and direct world interactions.

Smart Virtual Objects

Smart virtual objects are non-physical, software-based entities designed to replicate the characteristics and functionalities of real-world objects in digital simulations and virtual environments. They function as digital twins, providing accurate virtual counterparts to physical systems for purposes such as testing, prediction, and optimization, particularly in fields like where they mirror asset behaviors using streams. This approach allows for the emulation of complex interactions without the need for tangible hardware, emphasizing decentralized control and reusability in frameworks. A foundational model for these objects was proposed by Kallmann and Thalmann in 1998, outlining key attributes that enable realistic virtual interactions. Descriptive properties include state variables capturing intrinsic features, such as physical attributes like weight, , and movement descriptions (e.g., or of parts). Behaviors encompass response algorithms defined through commands, variables, and sequences that govern state-dependent actions, such as an opening only when closed and unobstructed. Agent actions involve AI-driven interactions, guided by positional vectors and gestures (e.g., hand shapes for grasping or pressing), which facilitate dynamic engagement between virtual agents and objects using techniques like . Representative examples illustrate their practical implementation. In (CAD) software, virtual prototypes simulate product designs to test mechanical behaviors and performance prior to physical fabrication, reducing development costs and time. Similarly, simulated IoT networks model interconnected devices in virtual scenarios, supporting by evaluating sensor deployments, mobility patterns, and propagation effects in contexts. These virtual objects enable interaction modes centered on remote monitoring and , where integration allows for scenario forecasting and system optimization absent physical entities. For instance, in manufacturing digital twins, simulations predict equipment failures or process inefficiencies, informing maintenance strategies without disrupting operations. This capability extends to broader predictive modeling, such as analyzing environmental impacts in simulated ecosystems, enhancing decision-making through iterative virtual testing.

Categorization Frameworks

Categorization frameworks for smart objects provide structured models to classify and analyze their capabilities, enabling systematic design and evaluation across diverse applications. One influential framework, proposed by Kortuem et al. in 2010, delineates three types based on increasing levels of and interactivity. Activity-aware smart objects focus on detecting and logging user activities through environmental sensing, such as tools that record usage patterns for pay-per-use billing. Policy-aware smart objects extend this by interpreting activities against predefined rules, enforcing compliance through alerts, as in safety equipment that warns of policy violations. Process-aware smart objects integrate further by embedding support, guiding users through organizational processes like tasks with contextual instructions. Building on earlier conceptualizations, Raff et al. in 2020 introduced a hierarchical framework of four s for smart products, which applies broadly to smart objects by emphasizing capability progression. The digital represents basic data-enabled objects with storage, processing, and transmission functions, exemplified by a standalone . Connected archetypes add networking and interaction, allowing , such as inventory tags that communicate stock levels. Responsive archetypes incorporate sensing and adaptability, enabling real-time reactions like voice assistants that adjust to user contexts. The intelligent achieves through reasoning and , as seen in learning thermostats that optimize settings independently. These frameworks serve as analytical tools to facilitate the design of smart objects by providing architectural abstractions that support from simple sensing to complex . They also enable maturity evaluations, assessing how objects evolve along dimensions to meet application demands, such as transitioning from activity logging to process integration in industrial settings. The evolution of these categories reflects a progression from Kortuem's awareness-based types, rooted in visions, to Raff's capability-driven archetypes, which synthesize diverse criteria into a unified model for the digital age. This advancement allows mapping to both physical and virtual smart objects; for instance, a policy-aware physical aligns with responsive archetypes, while virtual process models fit intelligent ones, promoting in cyber-physical systems.

Enabling Technologies

Sensors and Actuators

Sensors in smart objects serve as the primary mechanisms for , enabling these devices to perceive and interact with their physical environment. They convert physical inputs—such as , , , or motion—into measurable digital signals that inform decision-making processes. Common types include sensors, which detect thermal variations to monitor conditions in enclosed spaces; motion sensors like accelerometers and gyroscopes, which capture , , or orientation changes for tracking movement; and environmental sensors such as humidity detectors and GPS modules, which provide data on moisture levels or geospatial positioning. These sensors play a in fostering environmental awareness, allowing smart objects to gather from diverse sources for and response. Actuators complement sensors by translating processed data or external commands into physical actions, thereby enabling smart objects to effect changes in their surroundings. They function as output devices that control mechanisms like movement, signaling, or flow regulation. Representative examples include electric motors, which drive mechanical components for tasks such as opening doors or adjusting positions; LEDs or displays for visual feedback and alerts; and solenoid valves, which manage fluid or gas flow in industrial or plumbing applications. Through these mechanisms, actuators allow smart objects to respond dynamically, closing the loop in cyber-physical systems by implementing decisions derived from sensor inputs. Integrating sensors and actuators into smart objects presents several challenges that impact reliability and performance. is essential to maintain accuracy, as environmental factors like fluctuations or aging components can drift readings, requiring periodic adjustments to align outputs with standards—particularly difficult in low-cost, distributed IoT deployments. Energy efficiency remains a key concern, especially for battery-operated devices, where continuous sensing and actuation drain power; techniques such as duty cycling or low-power modes are employed to extend operational life without compromising functionality. Additionally, fusing data from multiple heterogeneous sensors enhances perceptual accuracy by combining complementary inputs (e.g., motion and GPS for precise localization), but it introduces computational overhead, issues, and error propagation risks that demand efficient algorithms to resolve. A practical of sensor-actuator is found in smart thermostats, where and sensors continuously monitor room conditions to detect deviations from set points, while actuators—such as relays or heating elements—automatically adjust the HVAC system to restore comfort levels, optimizing energy use through closed-loop control. This integration exemplifies how sensors provide the perceptual foundation and actuators execute responsive actions in everyday smart physical objects.

Connectivity Protocols

Smart objects rely on a variety of connectivity protocols to enable seamless communication, data exchange, and integration within networks, allowing them to interact with other devices, services, and users in real-time. These protocols are designed to address the unique constraints of smart objects, such as limited power, processing capabilities, and intermittent connectivity, while supporting diverse applications from gadgets to industrial systems. Key protocols include (BLE), which facilitates short-range, low-power connections ideal for personal area networks, such as wearables and proximity-based interactions. For and , and are widely adopted; offers high-bandwidth connectivity for data-intensive tasks, while provides energy-efficient, for coordinating multiple devices like lights and sensors. (LPWAN) protocols such as LoRaWAN and NB-IoT enable long-range, low-data-rate communications suitable for large-scale deployments in smart cities, , and . In IoT messaging scenarios, lightweight application-layer protocols like MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol) enable efficient publish-subscribe communication and RESTful interactions over unreliable networks, respectively. These protocols incorporate characteristics tailored to resource-constrained environments, including lightweight overhead to minimize battery drain and bandwidth usage, built-in security features such as AES encryption for data protection, and scalability mechanisms like mesh topologies in that allow devices to messages across large networks. For instance, MQTT's asynchronous messaging reduces latency in event-driven systems, while CoAP's UDP-based design supports operations for group communications among smart objects. The evolution of connectivity protocols for smart objects traces back to early RFID standards in the 1990s, which enabled basic identification and tracking through passive communication, laying the groundwork for object-to-object interaction. Subsequent advancements incorporated wireless personal area networks like in the early 2000s for low-power mesh systems, followed by the integration of cellular technologies such as , which now supports ultra-reliable, low-latency communications (URLLC) for time-sensitive applications like autonomous vehicles and remote surgery involving smart objects. A practical example is a smart bulb employing to join a network: upon , it authenticates via a coordinator device, forms a link with nearby bulbs for extended coverage, and responds to voice commands by relaying status updates to a central hub. This illustrates how protocols enable dynamic, self-organizing networks that enhance user control and energy efficiency in everyday smart environments.

Applications

Internet of Things Integration

Smart objects serve as the foundational nodes in the (IoT), forming a network of interconnected physical and digital devices equipped with sensing, processing, and communication capabilities to enable data exchange and automated interactions within data-driven ecosystems. These objects, often embedded with sensors and actuators, collect environmental data and respond dynamically, creating scalable systems where devices collaborate to optimize operations across diverse environments. In this context, IoT represents an extension of connectivity to everyday objects, transforming them into intelligent entities that contribute to broader networked intelligence. Integration of smart objects into IoT ecosystems relies on mechanisms such as gateways, which act as intermediaries for protocol translation and , bridging heterogeneous devices using standards like or CoAP to ensure seamless communication. platforms, including AWS IoT, facilitate centralized , , and by aggregating inputs from multiple smart objects, enabling scalable processing and remote control across global networks. Complementing this, edge processing occurs locally on gateways or devices to handle real-time decisions, reducing latency by performing computations near the data source rather than relying solely on distant resources. The proliferation of smart objects within IoT has driven significant growth, with earlier projections from in 2020 estimating 75 billion connected devices by 2025, though updated analyses indicate a more conservative figure of approximately 21.1 billion connected IoT devices globally as of late 2025, reflecting a 14% year-over-year increase. These numbers underscore the rapid expansion of IoT networks, projected to reach 39 billion devices by 2030 at a of 13.2%. In practical applications, such as infrastructure, traffic lights functioning as smart objects communicate via IoT protocols to monitor vehicle flow and adjust signal timings dynamically, reducing vehicle wait times by approximately 40% in deployed systems like those in .

Industry-Specific Uses

In healthcare, smart objects such as wearables equipped with sensors enable continuous monitoring by collecting physiological like , activity levels, and in real-time, often paired with AI for gesture analysis to enhance adherence monitoring without invasive procedures. These devices facilitate remote oversight, allowing healthcare providers to detect anomalies early and adjust treatments accordingly. A notable example is ingestible smart pills developed by Proteus Digital Health, which integrate sensors to track adherence; clinical trials have demonstrated their efficacy in confirming times and improving therapeutic outcomes with a favorable safety profile. In manufacturing, smart objects play a central role in Industry 4.0 through sensor-equipped machines that support by analyzing , , and operational data to forecast equipment failures. This approach minimizes downtime and optimizes in production lines. For instance, a in the automotive sector implemented dynamic using real-time models on multi-component systems, resulting in improved reliability and reduced unplanned outages. Another application involves frameworks that bridge theoretical models with practical deployments, enabling fault in industrial settings. Agriculture leverages smart objects like soil sensors for precision farming, where devices measure moisture, nutrient levels (e.g., NPK), pH, and temperature to optimize irrigation and fertilization. These sensors enable data-driven decisions that enhance crop yields while conserving resources such as water and fertilizers. A case study utilizing IoT-based soil parameter monitoring, including electrical conductivity and nutrient content, demonstrated accurate crop recommendations and efficient field management in real-time scenarios. In retail, smart shelves integrated with RFID technology automate management by detecting item movements, stock levels, and out-of-stock conditions through event-based data processing. This allows for seamless restocking alerts and reduces manual audits. A European departmental store case study on RFID implementation showed significant improvements in processes, including faster tracking and reduced losses from misplaced items. Case studies from the 2020s illustrate broader deployments, such as autonomous drones as smart objects in for last-mile delivery and inventory transport, integrating IoT for real-time and monitoring to streamline operations in urban and rural settings.

Advantages

Operational Benefits

Smart objects provide enhanced monitoring and control by delivering streams that enable , allowing operators to anticipate and prevent equipment failures before they occur. In environments, this capability has been shown to reduce unplanned and improve overall production by 20-30% through optimized scheduling and adjustments. For instance, IoT-integrated sensors on machinery continuously analyze , temperature, and usage patterns to forecast wear, thereby minimizing disruptions and extending asset life. The autonomy and optimization features of smart objects further streamline daily operations by enabling self-adjusting systems that reduce the need for human intervention. In smart buildings, adaptive systems use embedded sensors to automatically modulate illumination based on and levels, optimizing comfort while cutting manual oversight. These systems can achieve energy reductions of up to 29% by integrating with broader networks. Resource efficiency is another key operational gain, as smart objects facilitate intelligent actuation that conserves energy and materials through data-driven adjustments. According to Porter and Heppelmann's analysis, connected products enhance utilization rates and reliability by automating , such as in industrial equipment where sensors trigger precise adjustments to avoid waste. This leads to measurable savings in operational costs, with examples including optimized energy use in connected HVAC systems that respond dynamically to environmental data. Scalability is bolstered by the modular nature of smart object networks, which support seamless expansion without overhauling . These networks leverage standardized protocols to integrate additional devices incrementally, enabling operations to grow from small deployments to large-scale systems while maintaining performance. For example, in IoT ecosystems, cloud-based architectures allow for elastic scaling, accommodating increased data loads and device counts in or urban settings.

Strategic Impacts

The integration of smart objects has profoundly reshaped business models, transitioning companies from traditional one-time product sales to recurring service-based revenue streams, often termed "as-a-service" models. This shift allows manufacturers to offer connected equipment that provides ongoing value through monitoring, maintenance, and performance optimization, generating predictable income via subscriptions or usage-based pricing. For instance, smart connected products enable data-driven services like for equipment uptime, fundamentally altering how value is captured in supply chains. Smart objects serve as key innovation drivers by facilitating the creation of novel centered on consumer experiences. Through embedded sensors and real-time data collection, these objects enable adaptive functionalities, such as tailoring product interactions to individual preferences in everyday goods, which enhances user engagement and fosters . Academic studies highlight how this , powered by , influences consumer behavior by reducing decision friction and increasing purchase intentions, thereby spurring in consumer-facing applications. In smart product ecosystems, control over data ownership and advanced analytics confers significant competitive advantages, allowing firms to differentiate through superior insights and market positioning. Organizations leveraging these ecosystems can derive cumulative financial benefits equivalent to 2–9% of annual over the next five years from data-enabled benefits, including enhanced productivity and new revenue opportunities, as evidenced by research on collaborative data strategies. This edge stems from the ability to analyze aggregated from interconnected objects, informing strategic decisions that outpace rivals reliant on siloed . Economically, the proliferation of smart objects within IoT frameworks is projected to contribute substantially to global GDP growth, with estimates indicating a value creation of $5.5 to $12.6 by 2030 across industries. In manufacturing alone, IoT-driven advancements, including smart objects, are expected to add $2.1 to sector GDP through and gains. These projections underscore the macroeconomic transformative potential, amplifying broader contributions estimated at over $11 to global GDP by the same period.

Challenges

Security and Privacy

Smart objects, as integral components of IoT ecosystems, face significant security threats from cyberattacks such as distributed denial-of-service (DDoS) attacks, which exploit vulnerable connected devices to overwhelm networks and disrupt services. Data breaches often stem from unsecured sensors transmitting sensitive information without , enabling attackers to intercept and exfiltrate , as seen in incidents involving unpatched IoT firmware vulnerabilities. Privacy risks arise from constant capabilities in devices like smart home cameras, which collect audio, video, and behavioral that can be accessed without user consent, leading to unauthorized monitoring and potential . A notable example is the 2016 Mirai attack, which infected hundreds of thousands of IoT devices including cameras and routers through default credentials, launching massive DDoS assaults that disrupted major services and underscored the fragility of smart object networks. More recently, in 2025, a healthcare IoT security breach exposed over 1 million medical devices online due to unmanaged endpoints, weak , and lack of updates, leaking sensitive and highlighting ongoing risks in critical sectors. To mitigate these threats, encryption standards like (TLS) 1.3 are employed to secure data in transit between smart objects and cloud services, preventing interception by adversaries. Secure boot processes verify the integrity of during device initialization, ensuring only authenticated software loads and blocking injection from the ground up. Zero-trust architectures further enhance protection by requiring continuous verification of all devices and users, assuming no inherent trust within the network and segmenting access to limit breach propagation. Regulatory frameworks play a crucial role in enforcing security for smart objects, with the European Union's (GDPR) mandating privacy-by-design principles, including explicit consent for data collection and robust breach notifications. Post-2023 developments, such as the EU adopted in 2024, impose mandatory cybersecurity requirements on manufacturers of connected products, including vulnerability handling, secure updates, and conformity assessments to address IoT-specific risks. Compliance with these regulations helps mitigate liabilities while promoting standardized protections across global smart object deployments.

Interoperability Issues

One of the primary challenges in deploying smart objects arises from the heterogeneity of communication protocols, which often results in isolated silos where devices from different manufacturers cannot communicate effectively. For instance, protocols such as Zigbee, LoRaWAN, and Bluetooth Low Energy operate on distinct standards, leading to integration difficulties in diverse IoT ecosystems. This fragmentation is exacerbated by vendor lock-in, where proprietary systems restrict interoperability and limit the development of cross-platform applications. Additionally, incompatibilities in data formats—such as variations in JSON, XML, or CSV structures—create semantic barriers, hindering the meaningful exchange and interpretation of data across devices. As of 2025, emerging challenges include interoperability fragmentation in AI-driven IoT systems and ambient IoT networks, where battery-free sensors require unified protocols to avoid data silos and scalability issues. Efforts to address these issues have focused on establishing unified standards through initiatives like oneM2M, which provides a horizontal framework for machine-to-machine and IoT communications, enabling scalable interoperability across domains. By 2025, oneM2M has advanced its specifications to include enhanced and interworking capabilities for home and industrial applications, facilitating broader adoption. Complementing this, the IPSO Alliance, under the , promotes the IPSO Smart Objects model—a common object-oriented that standardizes data representation and interactions for resource-constrained devices. These frameworks aim to create a cohesive by defining abstract interfaces and resource models that transcend vendor-specific implementations. To bridge existing gaps, platforms have emerged as key solutions, acting as layers that translate between heterogeneous protocols and normalize data flows. These platforms often incorporate open APIs, allowing developers to integrate disparate smart objects without custom coding for each protocol. For example, open-source like those based on FIWARE or enables seamless connectivity in multi-device setups by providing protocol gateways and data harmonization services. The impacts of these interoperability issues are particularly evident in multi-vendor environments, where they delay widespread adoption by increasing deployment costs and complexity. In smart grids, for instance, the integration of mixed device types—such as sensors from various suppliers—often requires extensive custom , slowing the transition to efficient, resilient energy systems. According to NIST guidelines, unresolved interoperability challenges can lead to suboptimal resource utilization and hinder long-term investments in grid modernization.

Emerging Innovations

Recent advancements in (AI) and (ML) are enabling smart objects to achieve greater autonomy through on-device learning, where computational processes occur directly on resource-constrained edge devices rather than relying on centralized cloud infrastructure. This shift reduces latency and enhances privacy by processing data locally, with techniques like allowing multiple smart objects to collaboratively train models without sharing raw data. For instance, frameworks tailored for IoT edge devices have demonstrated improved personalization and robustness in dynamic environments, such as adaptive systems for resource-limited sensors. Progress in connectivity technologies is further propelling smart objects toward seamless integration, with providing ultra-reliable low-latency communication (URLLC) essential for real-time applications in critical IoT scenarios, achieving latencies as low as 1 millisecond. Looking ahead, networks promise even greater enhancements, including latencies as low as 1 millisecond and terabit-per-second speeds, facilitating massive machine-type communications for dense deployments of smart objects in urban settings. Complementing these, integration ensures secure data sharing among smart objects by leveraging decentralized ledgers and smart contracts, mitigating risks of tampering and enabling tamper-proof transactions in IoT ecosystems. Sustainability efforts are gaining prominence in smart object design, particularly through energy-harvesting sensors that capture ambient energy from sources like vibration, light, or to power devices without batteries, thereby minimizing . These eco-friendly approaches align with 2025 environmental regulations aimed at reducing IoT battery waste, including projections of up to 78 million batteries disposed daily globally, by encouraging energy-harvesting and battery-free designs. Advancements in such harvesting techniques have shown potential to extend device lifespans indefinitely in low-power applications, fostering greener IoT deployments. Hybrid models are emerging that deepen the fusion of physical and virtual realms for smart objects, utilizing (AR) and (VR) interfaces to create interactive digital twins—virtual replicas that mirror and control real-world counterparts in real time. This integration allows users to visualize and manipulate smart objects through immersive overlays, enhancing usability in domains like smart homes and industrial monitoring, where AR-enabled IoT systems enable intuitive control of interconnected devices. Such developments, combining VR with IoT data streams, support scalable environments where physical objects seamlessly interact with virtual simulations.

Potential Societal Impacts

Smart objects, integrated into everyday environments through the (IoT), offer significant positive societal effects by enhancing accessibility for vulnerable populations and boosting urban efficiency. For individuals with disabilities, assistive technologies such as wearable IoT devices enable real-time health monitoring and adaptive controls, allowing greater independence in daily activities like navigation or medication management. In smart cities, these objects optimize , reducing and ; for instance, IoT-enabled traffic systems in urban areas can decrease commute times by up to 20%, improving overall for residents. Such advancements promote environmental and citizen-centric services, fostering more inclusive and efficient urban living. However, the proliferation of smart objects raises ethical concerns, particularly the exacerbation of the and job displacement due to . The widens as access to IoT infrastructure remains uneven, disproportionately affecting elderly individuals, rural communities, and low-income groups who lack the devices or needed to benefit from smart technologies, potentially deepening social inequalities. Similarly, in sectors like and services could displace millions of jobs; projections indicate that IoT-driven efficiencies may lead to workforce reductions, necessitating reskilling programs to mitigate economic hardship and social disruption. These issues highlight the need for balanced deployment to avoid unintended societal costs. Addressing these challenges requires robust policy frameworks, including global standards for equitable access and ethical AI integration in smart objects, with discussions intensifying post-2023. International bodies like and the ITU advocate for guidelines emphasizing fairness, transparency, and inclusivity in AIoT systems, such as mandating and bias audits to ensure broad . Recent harmonized regulations, including the EU's AI Act updates, promote ethical deployment by requiring impact assessments for high-risk IoT applications, aiming to bridge divides and protect workers. Looking ahead, ubiquitous smart environments are poised to reshape human-object relationships by 2030 and beyond, creating seamless, intelligent ecosystems that enhance societal . Visions from reports like SMARTer2030 project that pervasive IoT integration could generate up to $5 trillion in economic and social value annually through optimized healthcare, , and , fostering a more connected and responsive world. Yet, this evolution demands proactive governance to realize equitable benefits, transforming passive interactions into proactive, empathetic engagements between humans and their surroundings.

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