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How Edge AI is Revolutionizing Mobile App Development

September 10, 2021

The growth of artificial intelligence has influenced app development in recent years. AI and machine learning have matured to provide flexible algorithms for intuitive and seamless experiences.

Developers, users, and businesses now have a whole new view of intelligent interactions within mobile apps.

They have embraced the current trend in the mobile app industry dominated by the Internet of Things (IoT) and mobile computing applications.

IoT and mobile devices generate loads of data at the network edge. Collecting such massive amounts of data in cloud data centers comes with a fair share of challenges, such as high network bandwidth and latency usage.

The solution to fully unleash the potential of big data and avoid issues associated with cloud computing is to bring AI to the network edge. This is what edge artificial intelligence does by combining AI and edge computing.

This article looks at artificial intelligence at the edge, discusses how it is changing the mobile application development landscape and gives examples of intelligence edge.

Table of contents

Artificial intelligence at the edge

In today’s Internet of Things (IoT) era, connected devices are generating large volumes of data available for collection and analysis. Artificial intelligence systems are required to make sense of this large quantity of data in real-time.

Edge AI is driven by and IoT and big data

A typical edge computing environment hosts machine learning models on the cloud.

In this setup, a prediction request is sent from the device to an application programming interface API based on the cloud. A response to the request is sent back over the internet.

Transmitting a small amount of data to a cloud-hosted API over the internet is usually straightforward. However, you may face challenges if you want to transmit a large amount of data like high-quality photos and videos. Still, you may not be able to transmit data from the device to the cloud over the internet in areas with the poor network coverage. That is why it is significant for data computation and data storage to be located on the device.

Besides, data-driven experiences are immediate and delay-intolerant. You may not want a video camera recording a traffic accident at an intersection to delay information or a pizza delivery drone to delay along the way. These are examples of fast-acting activities that need lots of data quickly.

Latency is reduced if the data has to travel to and from the cloud, which takes too long.

The solution is to localize such data-intensive processes and ensure data processing happens at the edge or near the hardware device.

Artificial intelligence at the edge helps overcome the issues of the traditional cloud, including the lack of security and high latency.

Why do we need edge AI?

Using machine learning algorithms, edge artificial intelligence processes data acquired by devices locally. This eliminates the need to connect your device to the internet to allow data processing.

Data processing happens in real-time, and response is received in milliseconds. Compared to the cloud model, edge computing attracts fewer communication costs.

Edge AI leverages on-device artificial intelligence to provide a rapid response time with high privacy and low latency while ensuring better use of network bandwidth.

By moving AI computations at the network edge, edge intelligence opens up new opportunities for AI-powered apps. These apps are enhanced in terms of speed, privacy, and security.

How mobile applications are leveraging AI

Mobile app developers have turned to edge AI in their quest to improve mobile app user experiences. They are applying edge intelligence in:

  • IoT integration
  • Voice-based technology
  • Delivering personalized content

IoT integration

IoT integrated with AI is a great step towards creating personalized experiences for users. A large chunk of real-time data is collected when you use an app with IoT technology.

Edge AI allows analysis of sensor data locally and automation of operational decisions. The most significant data is stored in a data center or the cloud.

By learning customer’s behavioral patterns, AI streamlines services offered to that particular client. The consequence is improved mobile app performance. Mobile development along with IoT leads to better resource use with higher efficiency.

Voice-based technology

Voice-powered technologies offer individualized experiences by identifying and differentiating users between voices. While this might pose privacy fears, edge voice-AI comes to seal loopholes. Edge Voice-AI eliminates the compliance and privacy issue by not sending voices to users.

Edge voice-AI works by turning the user’s speech into text on a device at the edge. Natural language understanding NLU interprets the text and performs operations like simple conversions and mathematical calculations.

Actions requiring storage and complex computation are sent to the fog for processing. These include conversations with translation, fact retrieval, and user history. Additional external information, including time and weather, is retrieved from the cloud when needed.

Speech is constructed upon the creation of a complete response to the users from either fog or cloud. The speech is them made at the fog or edge.

Robotic speech is made at the edge, while natural-sounding speech is made at the fog, but this depends on the present state of the technology.

Delivering personalized content

Consumer behavior is changing rapidly, and edge AI is the direct response. Edge intelligence brings in advanced personalization with real-time reactions and actions between consumers and brands. Edge AI operates directly on individual data to bring exclusive personalization.

With AI, businesses analyze users’ behavior to determine their preferences. For instance, by leveraging machine learning, a retailer can generate more electronic device options consumers may like based on their previous purchases.

Examples of intelligent edge

Intelligent edge has various use cases. It is applied in many industries, including the gaming industry, automotive industry, and healthcare industry.

Gaming industry

Modern online video gaming faces many limitations, considering the current storage, computing, and network infrastructure. Video games played on a cloud-based game server sometimes delay and lag, causing inconveniences to gamers.

The solution to these issues is to bring power closer to end-users and allow edge intelligence to make the games respond faster. This will also open up new opportunities such as sophisticated mobile games, augmented reality, and virtual reality gaming.

Automotive industry

Autonomous vehicles gather a lot of data and perform heavy computations to facilitate quick decisions. Today, vehicles have built-in intelligence such as fast data processing and cognitive mechanisms that lead to improved response time.

For example, Tesla cars have 360 cameras that allow the vehicle to see two cars ahead and predict the right time when braking is needed. This is an Edge AI application that applies algorithms locally. Edge AI is important to making split-second decisions that promote safety.

Healthcare industry

The healthcare sector is integrating IoT infrastructure with edge intelligence to improve patient outcomes. Some of the remote health monitoring devices in use today, including blood pressure monitors and insulin pumps, use Edge AI.

Many processes in healthcare facilities, such as predictive risk assessments and patient data analytics, also use edge intelligence.

Application developers are optimizing healthcare apps to notify users incase of any issues. For example, you need to make X steps to burn Y amount of calories. Or, if you are on a mountain climbing mission for a certain amount of time, the app reminds you when to take resting breaks, eat breaks, and drink.


The need for local data storage and data computation is inevitable. With this in mind, edge artificial intelligence is expected to grow bigger. Artificial intelligence at the edge brings many benefits, including reduced bandwidth costs, improved latency, privacy, security, and increased speed.

Further reading

Peer Review Contributions by: Rabo James Bature