Internet of Things

The Internet of Things (IoT) is the network of physical objects ("things") embedded with sensors, software, and connectivity that lets them collect data, exchange it with other devices and systems over the Internet, and act on the physical world. The term was coined by British technologist Kevin Ashton in 1999 in a presentation at Procter & Gamble, and by the end of 2024 the installed base of active connected IoT devices had reached 18.8 billion, projected to hit 21.1 billion by the end of 2025 and roughly 39 billion by 2030 according to the analyst firm IoT Analytics. ^[1][14] IoT spans everything from a Wi-Fi-enabled lightbulb in a living room to a temperature sensor monitoring a turbine blade inside a jet engine, provided the device can be addressed on a network and contributes data or accepts commands from a remote system.

For artificial intelligence, IoT matters in two directions: the streams of sensor data it generates are the raw fuel for machine learning (predictive maintenance, anomaly detection, computer vision, digital twins), and inference is increasingly pushed back down onto the devices themselves through TinyML frameworks and edge AI accelerators. This convergence is now commonly called AIoT (Artificial Intelligence of Things), a market that MarketsandMarkets valued at roughly 25.4 billion dollars in 2025 and projects will reach about 81 billion dollars by 2030 at a 26.1 percent compound annual growth rate. ^[22]

IoT extends digital intelligence into the physical world by combining low-cost microcontrollers, wireless communication, cloud back-ends, and, increasingly, on-device machine learning. Annual shipments of cellular IoT modules, Wi-Fi chips, and Bluetooth radios now run into the billions, and the installed base of active connected devices has overtaken the number of personal computers, smartphones, and human beings combined. ^[14] The category sits at the intersection of embedded systems engineering, networking, data engineering, and applied AI.

What is the Internet of Things?

IoT is most usefully defined by three properties: each "thing" has a unique addressable identity on a network (typically an IP address or a network-layer identifier mapped to one), each thing collects or actuates data about the physical world, and each thing can be reached from outside its immediate physical environment. The classic Internet connects servers and end-user computers; IoT extends the same model to objects that previously had no networking at all.

IoT overlaps with several adjacent terms. Machine-to-machine (M2M) communication usually refers to direct point-to-point links between devices, often over cellular or serial lines, without an Internet back-end; M2M predates IoT by decades and survives inside it. Cyber-physical systems (CPS) is the academic term, popularized by the U.S. National Science Foundation around 2006, for tightly coupled computational and physical components and is broader than IoT because it does not require Internet connectivity. Ubiquitous computing or "ubicomp," coined by Mark Weiser at Xerox PARC in 1988, is the philosophical predecessor that envisioned computers fading into the background of everyday objects. The Industrial Internet, a term promoted by GE around 2012, is roughly synonymous with Industrial IoT.

IoT is sometimes confused with the Web of Things (WoT), a W3C effort to expose IoT devices through web standards (HTTP, JSON, semantic descriptions). The Web of Things is a layer on top of IoT, not a separate phenomenon.

Who coined the term and when?

The phrase "Internet of Things" was coined by British technologist Kevin Ashton in 1999. Recalling its origin a decade later, Ashton wrote: "I could be wrong, but I'm fairly sure the phrase 'Internet of Things' started life as the title of a presentation I made at Procter & Gamble (P&G) in 1999." ^[1] Working at the time as an assistant brand manager on supply-chain visibility for cosmetics, Ashton used the title for a slide deck pitching radio-frequency identification (RFID) tags as a way to give every physical object a digital identity, and deliberately attached the then-buzzword "Internet" to capture senior executives' attention. Later that same year, Ashton helped found the Auto-ID Center at MIT with Sanjay Sarma, Sunny Siu, and David Brock; the center, an industry consortium, set out to standardize an open RFID infrastructure and produced the Electronic Product Code (EPC) that became GS1's EPCglobal standard.

Ashton's original vision was that computers should gather data without human help: "We need to empower computers with their own means of gathering information, so they can see, hear and smell the world for themselves, in all its random glory." ^[1][21] He argued that if "computers... knew everything there was to know about things, using data they gathered without any help from us, we would be able to track and count everything, and greatly reduce waste, loss and cost." ^[1] That premise, sensors feeding data into automated systems, is exactly what makes IoT the natural data source for modern AI.

The Internet of Things has a longer prehistory than the term itself. A networked Coca-Cola vending machine installed at the Carnegie Mellon University Computer Science Department in 1982 is widely credited as the first Internet-connected appliance. Graduate student David Nichols, with Mike Kazar, John Zsarnay, and Ivor Durham, instrumented the machine in Wean Hall so that anyone on ARPANET could query its contents using the finger protocol and learn whether bottles were stocked and cold before walking over. ^[2] The hack was a private convenience but is now treated as the symbolic birth of consumer-style IoT.

In 1990, networking pioneer John Romkey connected a Sunbeam Deluxe Automatic Radiant Control toaster to the Internet, controlling power to the heating element via SNMP over a SLIP link. Demonstrated at the Interop trade show after a challenge from organizer Dan Lynch, Romkey's toaster is sometimes called the first true "thing" on the Internet because it acted on the physical world rather than merely reporting state. ^[3]

Cisco's Internet Business Solutions Group (IBSG) later argued that the Internet of Things was "born" between 2008 and 2009, the period in which, by Cisco's reckoning, the count of devices connected to the Internet first exceeded the global human population. ^[4] The 2010s then brought rapid commercialization driven by smartphones (which contributed a generation of cheap sensors, radios, and processors), low-cost cloud back-ends, and the maturation of low-power wide-area network (LPWAN) standards.

A cluster of significant corporate moves followed. In August 2013, Arm Holdings acquired Finnish IoT software company Sensinode, gaining the NanoStack and NanoService implementations of 6LoWPAN (the IETF standard for carrying IPv6 over low-power 802.15.4 radios) and CoAP that would later anchor Arm's Mbed IoT platform. ^[5][19] On January 13, 2014, Google announced its acquisition of smart-thermostat maker Nest Labs for 3.2 billion dollars in cash, then the largest hardware acquisition in the company's history and a signal that hyperscalers viewed smart home as strategic. ^[6]

The mid-2010s through 2020 brought the rise of Industrial IoT, the Industry 4.0 movement out of Germany, and large-scale smart-city pilots in Barcelona, Singapore, Songdo, and elsewhere. Security incidents also began to define the field: the Mirai botnet, first observed in August 2016 by malware research group MalwareMustDie, launched a record-setting distributed denial-of-service attack on DNS provider Dyn on October 21, 2016, by enslaving hundreds of thousands of IP cameras and home routers shipped with default credentials. ^[7][8]

In December 2019, Amazon, Apple, Google, and the Zigbee Alliance announced Project Connected Home over IP (CHIP), an effort to unify smart-home protocols around a single IP-based standard. The project was renamed Matter on May 11, 2021, and the Zigbee Alliance was rebranded as the Connectivity Standards Alliance (CSA) at the same time. ^[9] Matter 1.0 was published on October 4, 2022, with Matter 1.4 following on November 7, 2024, and Matter 1.5 on November 20, 2025. ^[10][11] The 2020s have also been marked by the integration of generative AI into consumer IoT devices and by widespread on-device inference using edge accelerators, often described as AI-native or "AIoT."

How is an IoT system architected?

A typical IoT solution can be described as a layered stack, although the boundaries blur in practice. At the bottom sit the things themselves: sensors that convert physical phenomena into electrical signals, actuators that convert signals into physical motion or other effects, and microcontrollers that orchestrate them. Above that sit local networks and gateways that aggregate traffic from constrained devices and bridge it to the wider Internet. Wide-area connectivity carries the resulting data to cloud back-ends, where device management, storage, analytics, and integration with business systems live. Applications and dashboards expose the data to humans or to other software.

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IoT solutions also vary by topology. Star topologies put each device in direct contact with a hub or gateway and dominate consumer Wi-Fi and BLE deployments. Mesh topologies let devices forward packets for one another and are common in Thread, Zigbee, and Bluetooth Mesh networks. Cellular IoT designs typically connect each device directly to the operator's base station, removing the local hub.

The split between edge computing and cloud computing is a long-running architectural question. Edge processing reduces latency, bandwidth costs, and exposure of raw data, and is essential for safety-critical or privacy-sensitive applications. Cloud processing offers elastic compute, easy aggregation across fleets, and access to large pretrained models. Real systems usually combine both, with feature extraction or model inference at the edge and aggregation, training, and analytics in the cloud.

Communication protocols

IoT relies on a mix of physical-layer, network-layer, and application-layer protocols. The choice depends on range, throughput, power budget, and whether the device is plugged in or running on a coin cell.

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Mesh networking technologies (Thread, Bluetooth Mesh, Zigbee) extend coverage and resilience without adding wiring, at the cost of higher latency and complexity. Time-Sensitive Networking (TSN), a set of IEEE 802.1 standards, brings deterministic Ethernet to factory floors and is increasingly used in industrial IoT deployments where motion control or safety functions require bounded jitter.

By connectivity share, IoT Analytics reports that Wi-Fi accounts for roughly 32 percent of all IoT connections and remains the single largest connectivity technology, while cellular IoT (2G through 5G plus LTE-M and NB-IoT) accounts for roughly 22 percent. ^[14]

Hardware platforms

IoT hardware spans an enormous range. At one extreme are battery-operated 8-bit microcontrollers running for years on a coin cell; at the other are Linux-class single-board computers and edge servers with discrete GPUs or NPUs. The most common designs use 32-bit Arm Cortex-M microcontrollers, often with integrated radios.

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The Cortex-M family from Arm dominates the low-power tier, with hundreds of vendor variants integrating radios, sensors, and security peripherals. RISC-V cores are growing share, particularly in Espressif's later parts. On the higher tier, Linux-capable Cortex-A application processors and Jetson modules with integrated GPUs anchor the edge AI segment.

Operating systems

IoT devices run a wide range of operating systems depending on resource constraints. The lowest tier runs a real-time operating system (RTOS) on a few tens of kilobytes of RAM; gateway and edge AI devices run embedded Linux distributions; specialized stacks aim at narrow niches.

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Home Assistant Operating System sits at the gateway tier as a turnkey Linux distribution that hosts the open-source Home Assistant smart-home hub.

Cloud IoT platforms

Cloud IoT platforms provide device identity, telemetry ingestion, command routing, over-the-air update orchestration, time-series storage, rule engines, and integration with analytics, machine learning, and business systems. The market consolidated significantly in the early 2020s as several large platforms shut down or were repositioned.

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The shutdowns of Google Cloud IoT Core and IBM Watson IoT Platform within months of each other in 2023 prompted enterprise customers to revisit reliance on hyperscaler-specific IoT services and to favor open standards (Matter, MQTT, OPC UA) and portable runtimes such as EdgeX Foundry, Eclipse ioFog, and Open Horizon. ^[12][13]

Applications

IoT spans nearly every industry. The same underlying components (sensors, microcontrollers, radios, cloud back-ends) appear in very different configurations across consumer, urban, industrial, and scientific use cases.

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Industrial IoT (IIoT) has produced the clearest economic returns. Predictive maintenance models trained on vibration, temperature, and current data from industrial motors and pumps reduce unplanned downtime by detecting bearing wear or cavitation weeks ahead of failure. Digital twin platforms, which keep a synchronized software model of a physical asset or factory, are most useful when fed by IIoT telemetry.

How does AI relate to IoT (AIoT)?

The convergence of artificial intelligence and IoT, abbreviated AIoT, is the most active area of growth in the field. The pattern is twofold: cloud-side AI consumes IoT telemetry to drive analytics and automation, while on-device AI performs inference where the data is generated. The AIoT market was valued at roughly 25.4 billion dollars in 2025 and is projected to reach about 81 billion dollars by 2030 at a 26.1 percent compound annual growth rate by MarketsandMarkets; other analysts using a broader scope put 2024 value far higher, illustrating how much definitions vary. ^[22]

Why does IoT pair so naturally with AI? IoT continuously produces high-volume, high-velocity, labeled or labelable streams of real-world data, exactly the input that supervised and self-supervised machine learning models need, while AI supplies the pattern recognition that turns raw sensor readings into decisions. The two technologies are complementary: IoT is the sensing-and-actuation layer, and AI is the inference layer.

On-device or "edge" inference has become standard practice on devices powerful enough to host a small neural network. Three classes of hardware support it. Microcontroller-class targets run TinyML workloads through frameworks such as TensorFlow Lite for Microcontrollers (TFLM), Edge Impulse, and Glow; typical models include keyword spotters, gesture classifiers, vibration-based anomaly detection models, and small image classifiers. Application-processor-class targets run ONNX Runtime, PyTorch ExecuTorch, or TensorFlow Lite. Edge accelerator targets such as the NVIDIA Jetson Orin, Google Coral Edge TPU, Hailo-8 and Hailo-15, Qualcomm AI Engine in Snapdragon parts, and Arm Ethos-U NPUs deliver from a few TOPS to hundreds of TOPS at single- or low-double-digit watts.

The practical applications cluster around a handful of patterns. Smart cameras run object detection and people-counting locally so that only events, not raw video, leave the device. Industrial gateways host anomaly-detection models for predictive maintenance. Wearables run heart-rate variability and arrhythmia classifiers. Voice assistants perform wake-word detection on-device before sending audio to the cloud. Federated learning techniques, in which a global model is updated by aggregating gradients computed locally on each device, are used in keyboard prediction (notably by Google) and are being explored for fleet-wide IoT learning that respects data sovereignty.

The most recent shift is the arrival of generative AI on edge IoT hardware. Quantization shrinks model weights to 8-bit or 4-bit so that small open-weight language models such as Meta's Llama 3.2 1B/3B, Microsoft's Phi family, and Google's Gemma 2B/3B run interactively on Jetson Orin modules, on Apple Silicon, and on Snapdragon X-series chips. In the smart home, Amazon Alexa and Google Assistant integrate large model back-ends, while open-source Home Assistant supports local LLM integrations through projects like Whisper-on-edge and Ollama. In industrial settings, generative AI is being grafted onto digital twins so that a plant operator can ask natural-language questions of the asset model or have a chatbot draft a maintenance work order from a stream of sensor readings.

What is edge AI and TinyML?

Edge AI is the practice of running AI inference locally on a device or gateway at the edge of the network, rather than sending data to the cloud for processing. It covers a wide span of hardware, from smartphones and IoT gateways down to bare microcontrollers, and is the form of AI that most IoT deployments actually use because it cuts latency, saves bandwidth, keeps raw data private, and works even when connectivity drops.

TinyML (tiny machine learning) is the lowest-power slice of edge AI: running machine learning models directly on microcontrollers and sensors that typically have under 1 MB of memory and clock speeds below 1 GHz, drawing only milliwatts so they can run for years on a coin cell. ^[23] Because the compute and memory budgets are so small, TinyML relies heavily on quantization, pruning, and operator fusion to compress neural networks into a few hundred kilobytes. Canonical TinyML workloads include wake-word and keyword spotting, gesture and activity recognition, predictive-maintenance vibration analysis, and simple computer vision such as person detection. The TinyML market was estimated at roughly 1.5 billion dollars in 2025 and is forecast to grow at a CAGR above 20 percent through the mid-2030s as more sensors ship with embedded inference. ^[23]

The choice between edge and cloud is not binary. A common AIoT pattern is a cascade: a TinyML model on the device wakes on an event (a sound, a vibration spike), a more capable edge computing model on a nearby gateway confirms and classifies it, and only summarized results or hard cases are escalated to large models in the cloud for training and fleet-wide analytics. This keeps the bulk of data local while reserving expensive compute for the cases that need it.

Matter standard

Matter is an open, IP-based connectivity standard intended to give consumer smart-home devices a single common protocol so that products from different vendors interoperate without bridges or cloud round-trips. Matter is governed by the Connectivity Standards Alliance (CSA), formerly the Zigbee Alliance, with founding sponsorship from Apple, Google, Amazon, and Samsung among others.

The project was first announced in December 2019 as Project Connected Home over IP (CHIP). It was renamed Matter on May 11, 2021, when the Zigbee Alliance was simultaneously renamed CSA. ^[9] Matter 1.0 was published on October 4, 2022, defining device types for lighting, switches, plugs, locks, sensors, thermostats, blinds, media devices, and bridges, and specifying Wi-Fi and Thread as the underlying networks for IP transport with Bluetooth Low Energy used for commissioning. ^[10] Matter 1.4 was released on November 7, 2024, with significant additions for energy management (batteries, solar, heat pumps, water heaters) and home routers. Matter 1.5 was published on November 20, 2025, adding camera streaming, soil-moisture sensors, and refinements to closures and energy management. ^[11]

Matter's reference implementation lives in the open-source connectedhomeip repository on GitHub, originally seeded by Apple, Google, and others, and is licensed under Apache 2.0. Adoption has been gradual: support is shipping in Apple Home, Google Home, Amazon Alexa, and Samsung SmartThings, but multi-admin features, ecosystem-specific feature gaps, and certification cost remain points of friction for vendors.

Standards bodies and consortia

IoT is shaped by an unusually large number of standards organizations because it touches networking, hardware, industrial automation, and consumer electronics simultaneously.

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Security and privacy concerns

IoT security is hard for structural reasons. Many devices ship with default credentials, lack any update path, run for years without supervision, and live in environments where physical tampering is feasible. The economic incentive to harden a 5-dollar sensor is weak compared to the cost of compromise.

The Mirai botnet, first observed in August 2016, became the canonical example. Mirai scanned the Internet for IoT devices (chiefly IP cameras, DVRs, and home routers) listening on Telnet with one of approximately 60 default username/password pairs, infected them, and used them as a distributed denial-of-service platform. ^[7] On October 21, 2016, a Mirai-driven attack against managed-DNS provider Dyn caused outages at PayPal, Twitter, Reddit, Netflix, Spotify, and many other services in the eastern United States and parts of Europe. ^[8] The Mirai source code was leaked publicly in late 2016, and variants continue to circulate.

Industrial IoT carries higher stakes. Stuxnet, a worm uncovered in June 2010, targeted Siemens Step 7 software running on Windows PCs that programmed S7-300 PLCs controlling centrifuges at Iran's Natanz uranium-enrichment facility. The worm caused centrifuges to spin out of their normal range while reporting normal readings to operators, and is widely attributed to a joint U.S. and Israeli intelligence operation. ^[18] Stuxnet established that cyber operations could cause physical destruction of industrial equipment.

Other recurring concerns include weak or absent encryption on consumer telemetry, poor handling of cryptographic keys, the use of hard-coded backdoors for remote support, vulnerable bootloaders that allow firmware downgrade attacks, and the long tail of devices that outlive their vendors. AI cuts both ways here: machine-learning intrusion-detection and anomaly-detection models are increasingly deployed to spot compromised devices by their network behavior, while attackers use the same data streams and generative tools to probe and evade defenses. Privacy concerns center on how much intimate data smart speakers, smart cameras, and wearables collect, where it is processed, and how long it is retained, with the EU General Data Protection Regulation (GDPR) and analogous laws elsewhere driving disclosure and minimization requirements.

How big is the IoT market?

Market data for IoT varies sharply by analyst because the definition of a "connected device" differs. The most widely cited tracker, IoT Analytics, reported that the installed base of active connected IoT devices reached 18.8 billion at the end of 2024 (12 percent growth over 2023) and forecast 21.1 billion by the end of 2025 (14 percent growth). ^[14] The same firm projects roughly 39 billion connected devices by 2030 at a 13.2 percent compound annual growth rate from 2025. ^[14] Wi-Fi accounts for roughly 32 percent of all IoT connections and remains the largest single connectivity technology, while cellular IoT (2G through 5G plus LTE-M and NB-IoT) accounts for roughly 22 percent. ^[14]

On the economic side, McKinsey & Company estimated in a 2021 study that IoT could enable between 5.5 trillion and 12.6 trillion dollars in global value by 2030, including value captured by consumers and end customers. ^[15] McKinsey allocated about 26 percent of that total to factory settings and 10 to 14 percent to human-health settings, with B2B applications accounting for 62 to 65 percent of the total. The 2020 actual value captured was estimated at about 1.6 trillion dollars, near the low end of McKinsey's earlier 2015 scenarios, indicating that operationalizing IoT has proved harder than initial forecasts suggested. ^[15]

IDC and Gartner have published their own forecasts; the headline numbers differ but consistently project double-digit annual growth in connected devices and IoT spending through the late 2020s. The AI layer on top is growing faster still: AIoT is forecast at a 26.1 percent CAGR to roughly 81 billion dollars by 2030, and TinyML above 20 percent, both outpacing the device-count growth rate as more value migrates from connectivity to on-device intelligence. ^[22][23]

Regulation

Regulation of IoT has shifted from voluntary guidance to legally binding requirements. The European Union's Cyber Resilience Act (CRA) entered into force on December 10, 2024, with the principal obligations applying from December 11, 2027, and incident-reporting obligations from September 11, 2026. ^[16] The CRA covers products with digital elements placed on the EU market and imposes mandatory cybersecurity requirements on manufacturers, including secure-by-design development, vulnerability handling, and provision of security updates over the product's expected lifetime.

In the United States, the Federal Communications Commission adopted rules in March 2024 to create the U.S. Cyber Trust Mark, a voluntary cybersecurity labeling program for consumer wireless IoT products. ^[17] UL Solutions was conditionally selected as the lead administrator in December 2024, and the program was formally launched by the White House in January 2025. Devices bearing the mark are expected to meet baseline requirements derived from NIST IR 8425, with a QR code linking to a public registry of product details.

Other jurisdictions have moved in parallel. The United Kingdom's Product Security and Telecommunications Infrastructure (PSTI) Act came into force on April 29, 2024, banning default passwords on consumer connected products and requiring published security-update windows. Singapore's Cybersecurity Labelling Scheme (CLS) provides a four-level mark for consumer IoT devices.

ELI5: explain IoT like I'm five

Imagine if your toys, your fridge, and your front door could all talk to the Internet, the same way your phone does. The Internet of Things is just lots of everyday objects that have tiny computers and sensors inside, so they can notice things (like "it's cold" or "the door opened") and tell each other or tell you, even when you are far away. A smart speaker that hears you, a watch that counts your steps, and a doorbell camera are all "things" on this giant network. When you add AI, the things get smarter: instead of only sending raw information, they can decide for themselves, like a camera that knows the difference between your cat and a stranger without asking a faraway computer first.

See also

• Edge AI
• Edge computing
• Machine learning
• Computer vision
• Digital twin
• Federated learning
• Predictive maintenance
• Industrial IoT
• Smart home
• Kevin Ashton

References

1. Ashton, Kevin. "That 'Internet of Things' Thing." RFID Journal, June 22, 2009. https://www.rfidjournal.com/expert-views/that-internet-of-things-thing/73881/ ↩
2. "The History of the First IoT Device." IBM Think, 2024. https://www.ibm.com/think/topics/iot-first-device ↩
3. Romkey, John. "Toast of the IoT: The 1990 Interop Internet Toaster." IEEE Consumer Electronics Magazine, 2017. https://ieeexplore.ieee.org/document/7786805 ↩
4. Evans, Dave. "The Internet of Things: How the Next Evolution of the Internet Is Changing Everything." Cisco IBSG White Paper, April 2011. https://www.cisco.com/c/dam/global/de_de/assets/executives/pdf/Internet_of_Things_IoT_IBSG_0411FINAL.pdf ↩
5. "ARM Acquires Sensinode Oy to Accelerate the Internet of Things." Arm Holdings press release, August 27, 2013. https://www.arm.com/company/news/2013/08/arm-acquires-sensinode-oy-to-accelerate-the-internet-of-things-and-support-30-billion-connected ↩
6. Lawler, Ryan. "Google Is Buying Connected Device Company Nest For $3.2B In Cash." TechCrunch, January 13, 2014. https://techcrunch.com/2014/01/13/google-just-bought-connected-device-company-nest-for-3-2b-in-cash/ ↩
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10. "Matter Arrives, Bringing a More Interoperable, Simple and Secure Internet of Things to Life." CSA press release, October 4, 2022. https://csa-iot.org/newsroom/matter-arrives/ ↩
11. "Matter (standard)." Wikipedia. https://en.wikipedia.org/wiki/Matter_(standard) ↩
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17. "U.S. Cyber Trust Mark." Federal Communications Commission. https://www.fcc.gov/CyberTrustMark ↩
18. "Stuxnet." Wikipedia. https://en.wikipedia.org/wiki/Stuxnet ↩
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21. "That 'Internet of Things' Thing." RFID Journal (primary text of Ashton's column). https://www.itrco.jp/libraries/RFIDjournal-That%20Internet%20of%20Things%20Thing.pdf ↩
22. "AIoT Market worth $81.04 billion by 2030." MarketsandMarkets, 2025. https://www.marketsandmarkets.com/PressReleases/ai-in-iot.asp ↩
23. "Tiny Machine Learning (TinyML) Market Size, Share & Analysis." Market Growth Reports / DataM Intelligence, 2025. https://www.datamintelligence.com/research-report/tinyml-market ↩