Wi-Fi 7 is reshaping how devices communicate, and Wi-Fi 8, with even more advanced capabilities, is on the horizon. Businesses building connected devices need to understand how next-generation Wi-Fi impacts design decisions, user expectations and product viability.

Whether you’re developing smart home devices, industrial systems or enterprise-grade solutions, staying ahead of these evolving standards helps you deliver reliable and future-ready products.

Discover the key feature differences between Wi-Fi 7 and Wi-Fi 8, use cases and what this all means for product development.

What Is Wi-Fi 7?

Wi-Fi 7, or standard IEEE 802.11be, is the latest generation of wireless technology with extended capabilities of Wi-Fi 6 and 6E. The standard offers precise coordination, better use of spectrum and enhanced flexibility. Wi-Fi 7 delivers speeds of up to 46 Gbps.

The technology is commercially available in chipsets, routers, access points and adapters. Wi-Fi 7 maintains backward compatibility with Wi-Fi 6 and 6E, making it easier to transition without overhauling existing infrastructure.

Key Features of Wi-Fi 7

Wi-Fi 7 redefines wireless connectivity with robust capabilities that boost efficiency. The features of Wi-Fi 7 include:

320 MHz Channel

Wi-Fi 7 has a channel bandwidth of 320 megahertz (MHz). This additional bandwidth is primarily available in the 6 GHz spectrum and is beneficial in large data load settings, where faster data transfers reduce latency and streamline user experiences.

4096 QAM

Quadrature amplitude modulation (QAM) determines how much data can be encoded into a signal. Wi-Fi 7 moves from 1024 QAM of Wi-Fi 6 to 4096 QAM, meaning more bits are transmitted per signal burst. For product developers, this means less time spent on data handoffs, which improves responsiveness and frees up network resources.

Multi-Link Operation 

Multi-link operation (MLO) allows devices to operate across the 2.4 GHz, 5GHz and 6GHz bands simultaneously or dynamically. This flexibility reduces congestion, balances traffic and adds redundancy for stable connections.

MLO modes include:

• Multi-link single radio: Allows a user to alternate bands but receive only one frequency spectrum at a time.
• Multi-link multi-radio: Allows for simultaneous and non-simultaneous transmission using two or more radios to operate across bands.
• Enhanced multi-link single radio: Allows transmission on only one band at a time but adds more intelligence to how the device switches between frequency ranges.

Multiple Resource Units 

Wi-Fi 7 introduces flexible ways to allocate spectrum through multiple resource units (MRUs). These resource units allow a single device to pull together fragmented portions of the channel to form a usable transmission path. For environments with mixed traffic or partial interference, MRUs help maintain efficient operation and minimize wasted spectrum.

Preamble Puncturing

Preamble puncturing enables devices and clients to avoid portions of a channel experiencing interference while still using the rest of the bandwidth. This technique increases bandwidth availability in spectrum-dense areas. For developers building products for offices or industrial zones, this feature better supports multiple devices on the same network.

Restricted Target Wake Time

Wi-Fi 7 enhances the target wake time feature in Wi-Fi 6. With restricted target wake time (R-TWT), devices schedule times to wake and communicate, reducing overlap and saving energy. This has direct implications for battery-powered smart devices, where predictable, energy-efficient operation is essential.

Improved Power Efficiency and Low Latency

With better control over how and when devices communicate, Wi-Fi 7 improves energy use. Lower latency allows time-sensitive applications to respond more quickly, which translates to smoother operation and more reliable communication for industrial automation and health care products.

Enhanced MU-MIMO and OFDMA

Wi-Fi 7 extends the multiple-user (MU) multiple input multiple output (MIMO) — MU-MIMO — and orthogonal frequency division multiple access (OFDMA) techniques from Wi-Fi 6. These features allow more devices to transmit data simultaneously, improving overall network efficiency. It’s useful in high-density environments like offices and stadiums.

What Is Wi-Fi 8?

Wi-Fi 8 technology, known as IEEE 802.11bn, is currently in development. Compatible devices are expected to launch in early 2028, and early drafts suggest a shift toward reliability, coordination and resource efficiency.

The standard is being designed for future-forward use cases, such as autonomous systems, immersive computing and dense Internet of Things (IoT) environments. IEEE 802.11bn will maintain backward compatibility with Wi-Fi 7, 6 and 6E, and is anticipated to exceed 46 Gbps.

Key Features of Wi-Fi 8

While still in early development, Wi-Fi 8 will expand wireless capabilities. Here are some features that support next-gen applications.

Ultra-High Reliability

Wi-Fi 8 aims to reduce jitter and packet loss by improving scheduling, redundancy and error correction. This shift prioritizes consistency over peak throughput and supports applications where dropped signals impact performance or safety.

Multiple Access Point Coordination

The standard aims to create a more seamless experience for devices moving across spaces, improve load balancing and help avoid signal conflicts. For businesses managing high-density deployments, wireless networks may behave like well-managed wired ones.

Advanced Power Management

Wi-Fi 8 refines device wake cycles and energy scheduling to extend battery life further in ultra-low-power devices. These refinements support long-lifespan IoT devices in industrial and outdoor settings where frequent battery replacement isn’t practical.

Enhanced Spectrum Utilization

IEEE 802.11bn will introduce smarter spectrum management through predictive traffic scheduling and adaptive channel use. This reduces interference and increases network performance as the number of connected devices increases. For developers, this means more-reliable performance in real-world conditions.

Integrated mmWave Support

Millimeter wave (mmWave) communication has already been employed in 5G for high-density urban environments and fixed wireless access solutions to provide high-speed, low-latency communication. Millimeter wave will become more native in Wi-Fi 8 and offer fast, short-range communication ideal for virtual reality (VR) headsets, docking stations and local data transfer between machines. Built-in mmWave support allows product designers to target these use cases without relying on separate radio systems.

Wi-Fi 7 is reshaping how devices communicate, and Wi-Fi 8, with even more advanced capabilities, is on the horizon. Businesses building connected devices need to understand how next-generation Wi-Fi impacts design decisions, user expectations and product viability.

Whether you’re developing smart home devices, industrial systems or enterprise-grade solutions, staying ahead of these evolving standards helps you deliver reliable and future-ready products.

Discover the key feature differences between Wi-Fi 7 and Wi-Fi 8, use cases and what this all means for product development.

What Is Wi-Fi 7?

Wi-Fi 7, or standard IEEE 802.11be, is the latest generation of wireless technology with extended capabilities of Wi-Fi 6 and 6E. The standard offers precise coordination, better use of spectrum and enhanced flexibility. Wi-Fi 7 delivers speeds of up to 46 Gbps.

The technology is commercially available in chipsets, routers, access points and adapters. Wi-Fi 7 maintains backward compatibility with Wi-Fi 6 and 6E, making it easier to transition without overhauling existing infrastructure.

Key Features of Wi-Fi 7

Wi-Fi 7 redefines wireless connectivity with robust capabilities that boost efficiency. The features of Wi-Fi 7 include:

320 MHz Channel

Wi-Fi 7 has a channel bandwidth of 320 megahertz (MHz). This additional bandwidth is primarily available in the 6 GHz spectrum and is beneficial in large data load settings, where faster data transfers reduce latency and streamline user experiences.

4096 QAM

Quadrature amplitude modulation (QAM) determines how much data can be encoded into a signal. Wi-Fi 7 moves from 1024 QAM of Wi-Fi 6 to 4096 QAM, meaning more bits are transmitted per signal burst. For product developers, this means less time spent on data handoffs, which improves responsiveness and frees up network resources.

Multi-Link Operation 

Multi-link operation (MLO) allows devices to operate across the 2.4 GHz, 5GHz and 6GHz bands simultaneously or dynamically. This flexibility reduces congestion, balances traffic and adds redundancy for stable connections.

MLO modes include:

• Multi-link single radio: Allows a user to alternate bands but receive only one frequency spectrum at a time.
• Multi-link multi-radio: Allows for simultaneous and non-simultaneous transmission using two or more radios to operate across bands.
• Enhanced multi-link single radio: Allows transmission on only one band at a time but adds more intelligence to how the device switches between frequency ranges.

Multiple Resource Units 

Wi-Fi 7 introduces flexible ways to allocate spectrum through multiple resource units (MRUs). These resource units allow a single device to pull together fragmented portions of the channel to form a usable transmission path. For environments with mixed traffic or partial interference, MRUs help maintain efficient operation and minimize wasted spectrum.

Preamble Puncturing

Preamble puncturing enables devices and clients to avoid portions of a channel experiencing interference while still using the rest of the bandwidth. This technique increases bandwidth availability in spectrum-dense areas. For developers building products for offices or industrial zones, this feature better supports multiple devices on the same network.

Restricted Target Wake Time

Wi-Fi 7 enhances the target wake time feature in Wi-Fi 6. With restricted target wake time (R-TWT), devices schedule times to wake and communicate, reducing overlap and saving energy. This has direct implications for battery-powered smart devices, where predictable, energy-efficient operation is essential.

Improved Power Efficiency and Low Latency

With better control over how and when devices communicate, Wi-Fi 7 improves energy use. Lower latency allows time-sensitive applications to respond more quickly, which translates to smoother operation and more reliable communication for industrial automation and health care products.

Enhanced MU-MIMO and OFDMA

Wi-Fi 7 extends the multiple-user (MU) multiple input multiple output (MIMO) — MU-MIMO — and orthogonal frequency division multiple access (OFDMA) techniques from Wi-Fi 6. These features allow more devices to transmit data simultaneously, improving overall network efficiency. It’s useful in high-density environments like offices and stadiums.

What Is Wi-Fi 8?

Wi-Fi 8 technology, known as IEEE 802.11bn, is currently in development. Compatible devices are expected to launch in early 2028, and early drafts suggest a shift toward reliability, coordination and resource efficiency.

The standard is being designed for future-forward use cases, such as autonomous systems, immersive computing and dense Internet of Things (IoT) environments. IEEE 802.11bn will maintain backward compatibility with Wi-Fi 7, 6 and 6E, and is anticipated to exceed 46 Gbps.

Key Features of Wi-Fi 8

While still in early development, Wi-Fi 8 will expand wireless capabilities. Here are some features that support next-gen applications.

Ultra-High Reliability

Wi-Fi 8 aims to reduce jitter and packet loss by improving scheduling, redundancy and error correction. This shift prioritizes consistency over peak throughput and supports applications where dropped signals impact performance or safety.

Multiple Access Point Coordination

The standard aims to create a more seamless experience for devices moving across spaces, improve load balancing and help avoid signal conflicts. For businesses managing high-density deployments, wireless networks may behave like well-managed wired ones.

Advanced Power Management

Wi-Fi 8 refines device wake cycles and energy scheduling to extend battery life further in ultra-low-power devices. These refinements support long-lifespan IoT devices in industrial and outdoor settings where frequent battery replacement isn’t practical.

Enhanced Spectrum Utilization

IEEE 802.11bn will introduce smarter spectrum management through predictive traffic scheduling and adaptive channel use. This reduces interference and increases network performance as the number of connected devices increases. For developers, this means more-reliable performance in real-world conditions.

Integrated mmWave Support

Millimeter wave (mmWave) communication has already been employed in 5G for high-density urban environments and fixed wireless access solutions to provide high-speed, low-latency communication. Millimeter wave will become more native in Wi-Fi 8 and offer fast, short-range communication ideal for virtual reality (VR) headsets, docking stations and local data transfer between machines. Built-in mmWave support allows product designers to target these use cases without relying on separate radio systems.

Lower Latency

From manufacturing to health care facilities, edge computing is a powerful technology reshaping how industries handle data and streamline operations. This is made possible through Internet of Things (IoT) devices, such as sensors, cameras and specialized processors embedded at the edge. The market value of IoT-enabled devices is projected to increase to $6.5 billion in 2030, which is a growth of over $4 billion compared to 2020.

This guide explores the key benefits of edge computing and IoT technology.

What Is Edge Computing in IoT? 

Edge computing with IoT technology involves processing data closer to where it’s generated, which is at the network’s edge. Instead of sending every bit of information online to a distant cloud server, IoT edge devices analyze and process the data locally. This localized processing helps minimize delays, improves responsiveness and reduces the burden on bandwidth, which is crucial as IoT deployments continue to scale.

IoT devices such as sensors and smart appliances gather data in real time. With edge computing capabilities, IoT devices function as nearby edge gateways that filter, analyze and respond to data instantly.

For example, when an industrial sensor detects component damage or overheating, it prompts equipment to shut down without waiting for instructions from the cloud. Edge computing improves how IoT systems operate by enabling faster, smarter and secure device interactions while keeping critical processing close to where the computing happens.

Edge Computing and Cloud Computing in IoT 

Edge-enabled IoT devices also sync with the cloud for long-term storage and coordination across systems. This hybrid approach ensures fast local action while keeping the broader IoT ecosystem connected and intelligent.

Cloud computing centralizes data processing in large-scale data centers, which is ideal for massive data storage, analytics and long-term decision-making. In contrast, edge computing processes time-sensitive data near the device itself, enabling more real-time responses. Edge computing in IoT devices complements the cloud by reducing lag, preserving bandwidth and enhancing local autonomy.

Traditional cloud-first models struggle with latency, network instability and data overload. By shifting some computing power to the edge, businesses can overcome these limitations and enhance efficiency, reliability and data security. For example, sensitive health data in hospitals can be processed locally with less risk of cyber threat than data that is routinely transmitted over networks.

How Is Artificial Intelligence Used With IoT in Edge Technology?

Artificial intelligence (AI) and machine learning (ML) have a critical role in how IoT devices process data at the edge. While traditional cloud computing relies on centralized servers for data processing, edge computing also performs AI and ML tasks directly on IoT-enabled local devices.

This decentralized approach enables real-time analysis and faster decision-making, even without internet connectivity. From security cameras to wearable health trackers, edge AI allows data to be processed where it’s generated for more immediate insights. As more industries demand timely insights, AI, ML and IoT are driving innovation in many functional and efficient ways. For example, smart traffic lights with IoT sensors or cameras analyze data and adjust traffic signals according to vehicular flow.

The Benefits of IoT Edge Computing

Edge computing offers a wealth of advantages for IoT technology, improving how businesses operate while enabling richer interactions with connected devices. The following are key benefits of employing edge-based IoT technology:

• Reduces latency: Edge computing significantly minimizes processing delays by computing data close to IoT devices. Local data handling eliminates latency that occurs when information has to travel to and from an online cloud server. This is especially important for applications like smart surveillance systems and automated industrial equipment. With edge computing and IoT, immediate decision-making becomes possible and practical, allowing systems to respond to environmental inputs and operational changes in a timely manner.
• Lowers energy costs: When data is processed and filtered locally, it reduces the need for constant, high-volume transmission to the cloud. It also decreases reliance on a centralized IT infrastructure and costly cloud service charges. The result is lower bandwidth consumption and less power usage across the network. Edge devices also optimize power through such techniques as sleep modes, adaptive processing and task prioritization, which are beneficial for battery-powered IoT sensors in remote areas.
• Enables real-time tracking and analytics: Edge computing and IoT enable immediate data monitoring, making it suitable for time-sensitive applications such as predictive maintenance, asset tracking and remote monitoring. Whether it’s identifying early signs of equipment failure or adjusting environmental controls for smart buildings, decisions can be made the moment data is collected. This improves a company’s operational efficiency, safety and responsiveness.
• Enhances data security: One of the core benefits of edge computing with IoT devices is its ability to boost data security. By processing regulated data locally, businesses reduce the risk of exposing data during cloud transmission. Edge devices also implement built-in encryption, authentication protocols and access control at the source. This layered security approach makes edge computing with IoT valuable in industries such as finance, healthcare and critical infrastructure, where data breaches can result in serious damage and regulatory penalties.
• Leverages IoT machine learning: By integrating IoT with ML and AI, edge computing allows intelligent algorithms to operate directly at the source of the data. From smart homes that learn user preferences to industrial sensors that detect equipment defects, edge-based AI and ML process raw data and provide actionable steps. This is essential for time-sensitive smart data analysis and predictive modeling without cloud dependency.

• Provides scalability solutions: As IoT networks expand, centralized processing can quickly become a bottleneck for cloud data transfers. Edge computing distributes the processing workload across multiple local nodes, making it easier to manage and scale infrastructure. This architecture allows organizations to add more devices and handle more data without compromising performance or overwhelming core systems, which is beneficial for growing edge computing capability with IoT ecosystems.
• Boosts network reliability: Since most data processing occurs locally, the network becomes more resilient with IoT edge computing technology. This means computer systems continue to function even if cloud connectivity is lost or delayed. Network reliability is essential for mission-critical operations in industries like manufacturing, transportation and agriculture, where downtime is costly and continuous operations are a priority.

Eight Use Cases for Edge Computing in IoT

Edge computing with IoT technology is revolutionizing the way we interact with the world around us. Here are eight examples of how it’s being used in real-world applications:

1. Predictive Maintenance

Edge computing enables industrial IoT devices to continuously monitor equipment conditions at any time. By analyzing data like temperature, vibration and energy consumption at the source, businesses detect early signs of heavy equipment failure. This allows companies to schedule maintenance before breakdowns occur, reducing downtime and extending the machinery’s lifespan while streamlining operations.

2. Remote Monitoring for Jobsites

Edge computing enhances remote monitoring by enabling IoT devices to process and act on data locally. This is essential in hard-to-reach jobsites, such as oil rigs, rural cell towers and distant wind farms. Edge-enabled sensors flag discrepancies or safety risks immediately without waiting for cloud-based analysis, ensuring quicker response times and operational reliability.

3. Smart Grids

IoT in edge computing helps connect and modernize smart grids, which include power plants and substations. Smart grids enhance traditional electricity networks through the integration of digital technologies, sensors and software. This innovative approach enables precise and time-sensitive management of electricity supply and demand, resulting in reduced costs and improved grid reliability.

Edge-enabled smart meters and sensors placed throughout the grid collect real-time data, such as energy consumption, load balancing and equipment status. The data is processed locally, enabling improved decision-making for efficient energy distribution, fault detection and reduced utility costs.

4. Connected and Autonomous Vehicles

A standout application of edge computing in IoT is autonomous vehicles. Self-driving cars rely on local data processing to respond instantly to road conditions, traffic and obstacles. By analyzing input from onboard sensors such as cameras and light detection and ranging scanners, these vehicles make split-second decisions without depending on external networks. This enables efficient route optimization, improved fuel usage and enhanced safety, making edge computing instrumental in the development of smart transportation.

5. Health Care

Edge computing is transforming health care experiences through smart IoT devices. Wearables and remote monitoring systems gather data to track vital signs such as heart rate and transmit it securely for immediate analysis. Critical alerts can be generated locally on the device, allowing medical professionals to intervene right away. This setup supports telehealth services, chronic disease management and personalized care, even in clinics with limited bandwidth and connectivity.

6. Supply Chain Management

Edge computing in IoT delivers end-to-end visibility across supply chains. Radio frequency identification (RFID) tags, global positioning system (GPS) trackers and environmental sensors placed on goods and transport vehicles provide location and condition data. For example, it helps supply chains reroute shipments during delays or prompt alerts for temperature breaches, enabling more agile logistics and improved quality control.

7. Farming and Environmental Monitoring

IoT sensors deployed in agriculture and environmental science collect data on soil conditions, air and water quality and weather patterns. Real-time monitoring is possible without the need for constant connectivity. This leads to more efficient farming practices and proactive environmental management. Edge computing with IoT devices enables sensors to analyze areas locally and act instantly, such as activating irrigation systems or prompting air quality warnings.

8. Augmented Reality and Virtual Reality

IoT in edge computing significantly enhances augmented reality (AR) and virtual reality (VR) experiences by reducing latency and bandwidth strain. Improved responsiveness allows AR and VR tools to adapt instantly to the user’s physical environment and even function offline, providing more powerful applications that were once limited by cloud-based delays. Applications such as virtual product demos, AR-based maintenance instructions or immersive training simulations benefit from timely responsiveness when data is processed close to the user.

Five Types of Edge IoT Devices

Edge computing enhances a wide range of IoT devices for different purposes. Let’s take a closer look at some of these IoT devices:

1. Sensors

Depending on the operation, IoT sensors capture on-the-spot data such as temperature, pressure, humidity and motion. These devices gather localized data and process it at nearby edge nodes, enabling rapid decision-making without relying on a central cloud. For example, in manufacturing, vibration and thermal sensors detect early signs of equipment failure, prompting repair notifications to prevent breakdowns.

IoT-ready wireless connectivity solutions are also improving the performance and responsiveness of edge IoT devices with sensors. In smart homes, motion sensors adjust lighting dynamically, enhancing comfort and energy efficiency. This boosts applications that require timely data processing and control, including security systems and home automation.

2. Cameras

Smart cameras are evolving beyond capturing images. With edge computing in IoT, cameras process and analyze footage directly where it’s captured. This reduces latency, offloads network traffic and enables immediate action. For example, in a smart city, edge-enabled cameras detect unusual activity and trigger alerts without sending large video data to a central cloud.

In retail, cameras analyze shopping movements to optimize store layouts. At industrial sites, cameras use edge intelligence to monitor production lines and flag possible issues the moment they occur. Integrated with AI, smart cameras support facial recognition, license plate reading and crowd analytics, all with minimized data transfer.

3. Monitors

Edge-enabled IoT monitors are used to track key markers such as energy usage, air quality, fluid levels and machine performance. Whether it’s optimizing heating and cooling systems or flagging irregularities in water treatment plants, IoT monitors provide a critical layer of operational visibility.

In industrial settings, these devices combine sensor data with edge processing to deliver responsive insights that drive efficiency. For example, they enable predictive maintenance by identifying subtle signs of wear and tear. IoT monitors are also used in smart energy systems to identify consumption peaks and automatically adjust settings to minimize costs.

4. Drones

Drones integrated with edge IoT capabilities are transforming industries that require inspections, surveillance and deliveries in hard-to-reach areas. These airborne edge devices use cameras, sensors and onboard processors to collect and analyze data during flight without relying on cloud uploads.

In energy and utility sectors, drones inspect remote equipment like wind turbines or oil pipelines, relaying condition updates to technicians. In warehouses, drones assist in inventory checks and maintenance inspections. They also enable ultra-fast deliveries, bypassing traffic and reaching remote locations in emergencies.

5. Controllers

At the core of IoT in edge computing are controllers, which are smart systems that manage, automate and secure networks of connected devices. These controllers integrate sensor inputs, camera feeds and actuator outputs to make intelligent, localized decisions.

For example, a smart controller reads room temperatures from multiple sensors and instantly adjusts airflows. In industrial factories, energy management controllers monitor equipment and optimize power usage. Their ability to automate workflows and coordinate diverse devices makes them essential devices in homes, offices and industrial environments. With built-in security features and local processing power, IoT edge controllers help ensure reliability and minimize operational downtimes.

Connect With Synaptics Today for IoT Edge Device Design

Edge computing in IoT addresses the limitations of cloud-based IoT, enabling devices to overcome bandwidth, latency and security challenges. Partner with Synaptics for the latest IoT edge device design solutions, such as Synaptics Astra™ AI-native IoT processors powered by open software and outstanding wireless connectivity for a secure, multi-modal device edge.

Synaptics is a trusted leader in AI and edge technology, delivering reliable, high-performance solutions that make connected devices smarter and more efficient. We help industries create secure, intuitive digital experiences that transform how users engage with intelligent connected devices. We also provide customized multimedia compute solutions with a unified AI framework that rapidly deploys to edge devices. From smart homes to workplaces, we specialize in engineering exceptional experiences that drive the next wave of digital transformation.

Take advantage of the latest IoT edge computing technology. Contact us today to discover how our IoT edge device design solutions enhance your business.