IoT devices have evolved from simple data collectors into intelligent systems, leveraging technologies like Edge AI to process data locally. This shift has enabled faster decision-making, greater autonomy, and new functionality for various sectors, such as healthcare and industrial automation.
Central to this growth is the connectivity framework underpinning IoT ecosystems. While there are various connectivity standards like cellular networks, low-power wide-area networks (LPWAN), and wireless protocols such as Wi-Fi, Zigbee, and Z-Wave, Wi-Fi remains one of the most widely used, powering over 31% of global IoT connections.
Wi-Fi’s importance continues to grow with advancements like Wi-Fi 6E, which delivered faster speeds, lower latency, and greater energy efficiency to more than 473 million IoT devices shipped in 2023.
Now, with the rollout of Wi-Fi 7 in 2024, IoT applications will benefit from even higher performance, making it possible to support demanding use cases like advanced Edge AI processing and real-time automation. These innovations are setting the stage for the next wave of IoT growth.
Historical Development of Wi-Fi
Wi-Fi has been instrumental in the growth of IoT technology. Early IoT solutions used Wi-Fi primarily for its ubiquity, high data rate, and ease of deployment. However, these initial implementations often faced limitations like network congestion, high power consumption, and inconsistent performance in dense device environments.
Now, Wi-Fi has undergone significant evolution, transitioning from Wi-Fi 4 and Wi-Fi 5 to Wi-Fi 6, 6E, and now Wi-Fi 7. Each new generation has introduced features designed to enhance IoT performance.
Evolution of Wi-Fi Generations
| Wi-Fi Generation | Year Introduced | Key Features |
|---|---|---|
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Wi-Fi 4 (802.11n)
|
2009
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MIMO (Multiple Input Multiple Output) for improved signal reliability and range, suitable for IoT.
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Wi-Fi 5 (802.11ac)
|
2014
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MU-MIMO (Multi-User MIMO) for simultaneous connections, reducing device power consumption in IoT applications.
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Wi-Fi HaLow (802.11ah)
|
2016
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Operates in sub-1 GHz bands for long-range, low-power communication. Ideal for battery-operated IoT in industrial and agricultural settings.
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Wi-Fi 6 (802.11ax)
|
2019
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OFDMA for efficient multi-device communication, minimizing power usage. Target Wake Time (TWT) for extended IoT device battery life.
|
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Wi-Fi 6E
|
2020
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Access to the 6 GHz spectrum, reducing interference and enhancing IoT efficiency. Retains TWT for optimized power management.
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Wi-Fi 7 (802.11be)
|
2024
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Multi-Link Operation (MLO) for seamless multi-band communication, reducing power usage. Advanced interference management for battery-powered IoT applications.
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Impact on IoT Devices
Advances in Wi-Fi technology are transforming the capabilities of IoT and enabling them to operate more efficiently. Let’s explore the impact Wi-Fi has on IoT devices:
1. Solving the Latency Challenge for Real-Time Intelligence
Low latency, or the delay in data communication, is a critical factor in edge AI applications where precision and timing are essential. Wi-Fi 7 introduces deterministic latency, a feature designed to deliver minimal delays. While not the sole solution, it provides a robust framework for applications that demand reliable, real-time performance.
For example, autonomous drones inspecting power lines or bridges can greatly benefit from these advancements. As these devices roam across coverage areas, environmental factors such as obstacles and interference may affect the performance of certain frequency bands.
Wi-Fi 7’s Multi-Link Operation (MLO) enables seamless transitions between bands, ensuring uninterrupted connectivity. This allows drones to adapt dynamically to changing conditions, maintaining reliable communication even in challenging environments.
It’s important to note that real-time performance in edge AI devices also depends on the broader ecosystem, such as optimized hardware, software algorithms, and complementary connectivity protocols. Together, these elements enable the low-latency, reliable operations demanded by modern IoT applications.
2. Enhanced Network Capacity for Dense IoT Deployments
As IoT networks become denser, especially in industrial settings, handling a large number of connected devices within the same environment is a critical challenge. Wi-Fi 6E and Wi-Fi 7 address this challenge not just through wider bandwidth channels but also through advanced technologies designed for more efficient handling of multiple devices.
While wider communication channels (160 MHz in Wi-Fi 6E and up to 320 MHz in Wi-Fi 7) help with data-heavy applications, technologies like Orthogonal Frequency-Division Multiple Access (OFDMA) and Target Wake Time (TWT) are more directly responsible for optimizing the capacity to handle many devices.
These features allow Wi-Fi networks to support simultaneous communication between numerous devices without congestion, ensuring reliable performance even in environments with high device density.
For example, in a smart city, where traffic sensors, Wi-Fi hotspots, and connected surveillance cameras operate side by side, Wi-Fi’s ability to efficiently manage this large number of devices allows for uninterrupted data flow, even during peak usage.
Similarly, industrial IoT networks, such as those in factories with autonomous robots and predictive maintenance sensors, can rely on Wi-Fi to enable real-time data sharing without compromising performance.
This shift towards greater efficiency, coupled with advancements in multi-user support and seamless coordination across devices, ensures that Wi-Fi is an ideal choice for high-density IoT deployments, particularly in urban environments.
3. Power Efficiency
Power efficiency is a critical challenge for IoT devices, especially those deployed in resource-constrained environments where battery replacement or frequent maintenance is impractical. Modern Wi-Fi advancements address this issue by introducing innovative strategies and chipsets tailored for ultra-low power consumption.
An example is Silicon Labs’ new SiWx917 ultra-low-power WiFi 6 IoT chipset, which delivers significant improvements in energy efficiency, enabling battery life of up to two years in specific IoT applications.
This chipset integrates Target Wake Time (TWT), a feature that schedules communication intervals for devices, minimizing energy use during idle periods.
By reducing unnecessary network activity, TWT optimizes the power consumption of IoT devices like environmental sensors and wearable health monitors. The SiWx917 also includes advanced features such as a Cortex-M4 processor for on-device processing, reducing reliance on external compute resources and saving additional power.
Synergy Between Wi-Fi and Other IoT Connectivity Protocols
The IoT ecosystem thrives on connectivity diversity, with each protocol carving its niche to meet the unique demands of different applications. While Wi-Fi has earned its reputation as a backbone for high-bandwidth, low-latency environments, it does not operate in isolation.
Instead, it functions as part of a multi-technology ecosystem, complementing other protocols like Zigbee, Bluetooth, cellular networks, and LPWAN to address diverse IoT challenges.
Wi-Fi and Cellular: Powering Mobile and Large-Scale IoT
Cellular technologies like LTE-M and 5G offer unmatched wide-area coverage, mobility, and the ability to keep IoT devices “on the grid.” They are essential for applications such as connected vehicles, remote asset tracking, and large-scale smart city deployments. Wi-Fi, on the other hand, excels at localized high-speed data transfer, making it an effective complement to cellular networks.
In hybrid scenarios, devices use cellular connectivity for persistent tracking and visibility, switching to Wi-Fi when available to handle large amounts of data efficiently.
Applications include:
Wi-Fi and Zigbee: Collaboration in Smart Homes
Hybrid smart home systems use Zigbee for efficient local communication and Wi-Fi for seamless internet access, ensuring robust functionality with balanced power efficiency and speed.
Zigbee, a low-power, mesh networking protocol, is a popular choice for applications requiring long battery life and reliable operation across multiple nodes, such as smart lighting and home automation systems. Wi-Fi, on the other hand, delivers the bandwidth needed for streaming video feeds from security cameras or managing cloud-connected devices.
In smart home ecosystems, hybrid solutions often leverage the strengths of both technologies. For example, a Zigbee-enabled smart thermostat might communicate locally with sensors and switches while also using Wi-Fi for remote access via a smartphone app.
Some devices act as bridges, seamlessly speaking both Zigbee and Wi-Fi to connect low-power local networks with the broader internet. This synergy balances power efficiency and high-speed data transfer, ensuring robust functionality without compromising battery life.
Wi-Fi and Bluetooth: For Proximity-Based Applications
Bluetooth is synonymous with short-range, low-power communication, making it ideal for wearable devices, beacons, and proximity-based IoT use cases. Bluetooth Low Energy (BLE) beacons have been used in retail to guide customers with personalized offers based on their location within a store.
However, while initially touted as transformative, their adoption has been uneven. Challenges like privacy concerns, the need for dedicated apps, and competition from technologies such as Wi-Fi have limited their widespread impact.
Even so, they remain valuable in niche retail applications, such as inventory management and targeted engagement in loyalty programs. Wi-Fi complements Bluetooth in such scenarios by supporting analytics platforms and transmitting aggregated data to centralized servers.
Wi-Fi and LPWAN: For Long-Range, Low-Power Needs
Low-Power Wide-Area Networks (LPWAN) protocols like LoRaWAN and NB-IoT excel in applications requiring long-range communication and low power consumption, such as environmental monitoring, asset tracking, and smart agriculture. These protocols are ideal for deploying IoT devices in remote areas where power sources are scarce and cellular connectivity may be unreliable. Wi-Fi integrates with LPWAN systems by acting as a gateway between localized IoT deployments and cloud platforms.
LoRaWAN-enabled soil sensors gather data on parameters like moisture and temperature across the field. These sensors transmit their data using low-power, long-range LoRaWAN connectivity to a central Wi-Fi gateway, strategically placed within range.
The Wi-Fi gateway processes and forwards the data to a cloud-based analytics platform via a Wi-Fi connection. This hybrid approach leverages LoRaWAN for energy efficiency and extended battery life in field sensors, while Wi-Fi ensures high-speed, reliable transmission of aggregated data for real-time decision-making and insights.
Making IoT Work with embedUR
Wi-Fi has come a long way, evolving from a simple connectivity solution to a critical enabler of modern IoT ecosystems.
With the introduction of Wi-Fi 7, network congestion, latency, and energy efficiency issues are finally being addressed, creating new opportunities for smarter, faster, and more reliable IoT applications. However, technology alone is not enough. Success in IoT depends on making all the pieces work together seamlessly.
That’s where embedUR comes in. For over 20 years, we’ve been driving innovation in IoT connectivity, building the firmware and protocols that power some of the most advanced devices in the industry.
We understand that IoT is not just about getting devices to connect—it’s about making them perform reliably in real-world conditions. Our team specializes in tackling the tough challenges of wireless connectivity, helping companies like yours bring innovative, scalable IoT products to market.
If you’re ready to move beyond off-the-shelf solutions and need a partner who knows how to make IoT work, let’s talk. embedUR can help turn your vision into reality, ensuring your products are not just ready for today’s challenges but built to lead tomorrow’s market. Did you like this post? Then you’ll love reading about all the non-data transmission uses of Wi-Fi.
Manufacturing is an enormous, well-entrenched, mature market. Many of its business processes have been tried and true since time immemorial. However, a new wave of Edge Artificial Intelligence solutions has the potential to dramatically change how products are created.
With them, suppliers can streamline workflow, reduce delays, gain more insight into their operations, differentiate their services, and enhance customer satisfaction.
Manufacturing is a large global industry, generating trillions of dollars. Competition is intense, so suppliers are constantly searching for an advantage. IoT has already had a massive impact on efficiency, now Edge AI is on the brink of providing massive advantages in literally every part of their operation.
Edge AI’s Important Role in Industry 4.0
Edge AI is a key element in Industry 4.0, a movement that builds upon previous industrial revolutions by leveraging new technologies and creating smart factories and intelligent supply chains.
Industry 4.0 represents a big paradigm shift in manufacturing, enabling companies to achieve higher levels of efficiency, agility, and competitiveness, which are vital in today’s rapidly evolving global market.
Manufacturing is an Equipment Heavy Industry
Let’s take a closer look at this equipment. Manufacturers now have a wide and growing range of equipment that creates their products.
a) Industrial Robots: automated, programmable devices capable of movement on three or more axes. They are used in assembly, welding, painting, pick-and-place, packaging, labeling, inspecting, and testing.
b) Programmable Logic Controllers: specialized digital computers that control pieces of machinery.
c) Supervisory Control and Data Acquisition: process, gather, and monitor real-time data, and interact directly with devices, like sensors, valves, pumps, and motors.
d) Centrifuge: uses centrifugal force to separate components of a mixture based on their density
e) Mixers: blend, mix, or homogenize various materials in the manufacturing process.
f) Drill Press: create holes in various materials, typically metal, wood, or plastic.
g) Conveyor Belts: mechanical material handling solutions that transport products and materials from one area to another. They are often used in factories, warehouses, and other industrial settings to move boxes, packages, and raw materials.
And many more. Traditionally, these devices had little to no intelligence. As microprocessor technology has shrunk in size and grown in capabilities, they have become smarter.
IoT was the first wave, enabling data to be collected from just about any device, process, motor or machine in the production line. This Big Data collection and analysis has allowed us to accurately predict equipment failures and take preventative measures to avoid them, and it has given factory managers countless other insights that help keep things running smoothly. However, storing big data in the Cloud and all the compute required to process it, isn’t cheap.
Edge computing is a game changer because it enables manufacturers to move data processing from large central servers to small intelligent devices. Edge AI takes this intelligence to another level by infusing IoT-enabled devices and equipment with on-board AI to filter, process, interpret and act up on the data they are collecting – without needing to pass so much, or any, data to the cloud, nor wait for insights or instructions from the Cloud. This is a significant evolution that will make factories even smarter, more efficient, and more flexible than ever before.
Benefits of IoT and Edge AI in Factories
IoT in manufacturing led to Big Data and analytics which enabled us to correlate information and gain insights into how their individual machines in a manufacturing plant are performing; and getting visibility on potential reliability issues or imminent failure.
With Edge AI on the horizon, new edge solutions are being designed to improve the efficiency and performance of manufacturing equipment further still.
Now Edge AI solutions can calculate machine vibrations, temperature, acceleration, displacement, and sound frequencies and more to determine the health of a motor or machine, and act upon that information autonomously – without needing to push large amounts of data to the cloud, or waiting for the cloud to interpret it, and suggest a course of action.
How IoT and Edge AI Edges Business Forward
Edge AI enables manufacturers to Increase efficiency. The solutions collect device data and leverage it immediately to gain unprecedented insights used to improve operations.
Equipment failures disrupt operations, sap productivity, and compromise safety. Real-time monitoring evaluates machinery performance around the clock. These systems use AI analytics to watch for potential problems. If anomalies arise, they alert personnel who can take swift action before failure occurs.
Manufacturers need to adhere to a wide range of industry and government regulations. Continuous monitoring of production processes provides them with the data needed to verify that their systems adhere to such standards.
Analytics provide insights into demand, material availability, supplier performance, and logistic workflow. Management engages in proactive decision-making and is better able to balance resources to allocation, and adapt to market fluctuations. Manufacturers lower excess inventory, keep systems running longer and more efficiently, and streamline supply chain and manufacturing plant workflow.
When a system goes down, chaos ensues: interrupting manufacturing runs, disrupting supply chains, draining productivity, and creating safety hazards. With Edge AI solutions, manufacturers gain real-time data visibility into equipment performance and usage data so they address problems before they knock everything offline.
Materials constantly move through the supply chain, the manufacturing plant, and the distribution channel. Also, suppliers have on-site equipment that helps keep manufacturing processes moving forward. However, they don’t always know what is where. New edge AI solutions can provide them with the visibility to track these items in real time
Real time monitoring enables companies to detect defects throughout the production process. By overseeing production and detecting defects in real-time, manufacturers can adjust before faulty products come off the line. Suppliers lower the risk of making faulty products, saving money on costly recalls and repairs.
A New Manufacturing Era Emerges
In sum, Industry 4.0 represents a significant shift in how products are designed, manufactured, and delivered. It leverages advanced Edge AI to improve efficiency, productivity, and flexibility.
In fact, Edge AI will be everywhere and in just about everything going forward. Given the capabilities, the type of applications possible is virtually limitless. Manufacturing and industrial processes are being transformed through the integration of digital technologies. These new solutions enhance suppliers’ competitiveness, efficiency, and productivity. It changes how goods are produced, distributed, and serviced, improves quality, offers greater customization, and provides more responsiveness.
In essence, Industry 4.0 is ushering in an era of connectivity, advanced analytics, automation, and intelligent manufacturing that will transform the industry. Engineer and economist Klaus Schwab, founder and executive chairman of the World Economic Forum, said the scale, scope and complexity of Industry 4.0’s transformation will be unlike anything humankind has experienced. The speed and breadth of data becoming available forces suppliers to rethink and reimagine every part of their operation. Companies that embrace these technologies can gain a competitive edge and drive positive economic outcomes.
The Challenge of Creating Industry 4.0 Applications
While the benefits are game changing, manufacturers face challenges. Right now, these ideas are more theoretical than readily available. The infrastructure needed to support Edge AI applications is largely non-existent.
Creating such solutions is complex because they perform very sophisticated functions. To put it bluntly, most companies lack such expertise as well as the time, funding, and desire to develop Edge AI applications themselves.
Find the Right Partner
Manufacturers must build up the infrastructure before they can deploy applications that deliver Industry 4.0’s potential benefits. However, they cannot do it by themselves. The work requires an embedded systems firm with the know-how to develop software directly from the AI microprocessors the applications are intended to run on.
Such companies collaborate closely with silicon vendors, network equipment vendors, software suppliers, and AI experts, so they can deliver Edge AI solutions today. These embedded systems specialists pull functions together and adapt them for each customer, thus accelerating product development and reducing your time to market.
As an embedded systems company, embedUR has been at it for decades. We work closely with chip vendors and can help your organization realize Industry 4.0’s potential; edging ahead of your competition. Contact us to de-risk your Edge AI projects and accelerate time to market.
No expertise – you have no choice
Some expertise – what to build vs. buy?
How “build / buy” decision-making usually goes
Time to market or time to MVP should be your North Star.
Understanding the Level of Effort
Design Imperatives for IoT
So what’s unique about IoT Projects?
Engineering Effort
Complexity of IoT Embedded System Development
Some Engineering-Specific Pros and Cons
DevOps and Governance
The Team Decision
