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How Edge Computing Solves Four Key IoT Challenges

How Edge Computing Solves Four Key IoT Challenges

Over twenty years since the term “Internet of Things,” or IoT, was coined, the IoT is now a mature ecosystem poised to take a big leap forward as a result of 5G and edge computing. IoT applications are expanding into mission-critical, ultra-low latency, and high-density use cases across industries. According to forecasts by IDC, the number of connected IoT devices will grow to 55.7 billion by 2025.

This rapid growth is dependent on the infrastructure needed to support it. While 5G often takes the spotlight for enabling new IoT use cases, edge computing is already working to solve many of the issues created by the exponential increase in IoTs.

This post will show how the edge computing model addresses key challenges like real-time data analysis, an exponential increase in data, security and privacy issues, and the need for increased connectivity and device autonomy.

What is IoT?

IoT refers to the array of “smart” devices that enable monitoring, data transmission, analytics, and other actions via an Internet-connection. Furthermore, as ZDNet writes, “The term IoT is mainly used for devices that wouldn’t usually be generally expected to have an Internet connection, and that can communicate with the network independently of human action. For this reason, a PC isn’t generally considered an IoT device and neither is a smartphone – even though the latter is crammed with sensors.”

Examples of IoTs range from simple monitoring devices like sensors to complex machinery that performs mission-critical applications and services in a variety of industries. The wide range of IoTs includes:

  • wearable devices like smart watches and health monitors;
  • connected appliances like refrigerators and thermostats;
  • autonomous vehicles;
  • digital twins for running manufacturing tests;
  • shipping container and logistics tracking;
  • smart farming equipment for monitoring soil, weather, and crops.

Challenges of IoT

Despite the possibilities of IoTs, many businesses are still hesitant to use them. A 2019 Gartner presentation on IoT platforms and solutions noted that although 57% of IoT investments delivered returns that exceeded their expectations, IT decision makers still list a number of obstacles to adoption [1]. Those surveyed listed some of the top barriers to success as:

  • security;
  • privacy concerns;
  • data and information management;
  • reliability and availability; and
  • costs and funding concerns.

The use of edge computing mitigates these challenges by processing data closer to where it is generated or needed, providing not only faster processing, but other benefits such as cost-efficiency, improved security and privacy, actionable data insights, and improved reliability.

Real-Time Processing and Analytics

Although the Internet was originally designed for communication between humans, the expansion of wireless connectivity into IoTs enables data generation at the speed of machines–and as a result, requires machine-speed data processing. Furthermore, as noted in a 2019 Deloitte report, “Because data can become essentially valueless after it is generated, often within milliseconds, the speed at which organizations can convert data into insight and then into action is generally considered mission critical.”

In other words, the data that is generated by IoT sensors and other devices is only useful to the extent that it is actionable, requiring real-time analytics and automated responses. Edge computing enables both by reducing latency and enabling event-driven responses that automatically take place on edge nodes, rather than waiting for data to make lengthy round trips to and from the cloud.

An Explosion of Data

Another challenge IoTs present is the sheer amount of data generated, particularly for sensors and other IoTs that provide data for machine-learning (ML) and AI algorithms. Sending huge amounts of data to and from the cloud for processing would be prohibitively expensive. Edge computing reduces this burden by processing data locally. As The New Stack writes, “Processing large amounts of sensor data at the edge reduces network bandwidth costs and cloud data storage costs. Edge computing allows for the analysis and filtering of data closer to the sensors so only the relevant data is sent to the cloud.”

But the explosion of data generated by IoT is not only confined to individual devices that process large amounts of data; it extends to the increasingly large number of IoTs that are being produced. As noted in the LFE’s 2020 State of the Edge Report, “Increasingly, every car, light socket, factory robot, wall switch, and appliance will have a network connection … These devices will collectively generate zettabytes of data, and edge computing will activate that data and put it to work.” Not only will edge computing enable faster analysis of this data, LFE notes, “It will also obviate much of the delays, costs, and complexities of sending all the collected data back to a centralized location for processing.”

Security and Privacy

As IoTs generate more and more data, another challenge arises: securing that data to ensure the privacy and proper governance of sensitive information. A 2020 article from Security Magazine summarizes the numerous security challenges posed by IoTs, including:

  • ability for devices to interact with the physical world;
  • use in DDoS attacks;
  • large attack surfaces, including the application, platform, and hardware; and
  • black-box configurations.

These security risks are compounded by the lower computational power and memory of many IoT devices, making them difficult to encrypt and resulting in weak security protocols. However, this effect can be mitigated by edge computing, as noted in a 2020 study from the journal Digital Communications and Networks. It states that, “Placing security mechanisms at a trusted edge layer can alleviate the security challenges caused by resource constraints at the IoT device layer.”

In addition, privacy challenges that result from the expansion of IoT devices into fields like healthcare can be overcome through edge computing. As Wired UK writes, “A technique known as federated learning allows an algorithm to be trained across multiple servers or edge devices that hold local data samples, without that data having to be shared or exchanged. … This allows the AI to learn from use cases, but protects the privacy of users.” In other words, edge computing enables processing to take place on devices themselves, ensuring users’ privacy and improving their data’s security.

Reliability and Autonomy

One of the problems inherent in relying on the cloud for Internet connectivity is that IoTs expand Internet connectivity into areas that may not be amenable to wireless Internet. This applies to not only current and emerging scenarios such as fitting mines and basements with industrial IoT devices and expanding Internet access to geographical areas with poor connectivity, like sub-Saharan African, but also future use cases involving dimensions of the ocean depths and outer space.

Mission-critical IoTs will also need reduced reliance on connectivity to avoid failure in the event of connection loss. In these cases, edge computing improves autonomy by allowing processes to take place on the device edge or via a gateway and edge server to ensure IoTs like self-driving cars and personal medical devices can continue to monitor and respond to events in real-time.

Use Cases for Edge and IoT

To summarize, edge computing helps to solve IoT challenges by providing an array benefits, including:

  • Lower latency
  • Bandwidth and availability
  • Network connectivity
  • Network security
  • Autonomy
  • Data privacy
  • Cost-efficiency

As a result, edge computing is necessary for IoTs with time-sensitive data, high volumes of data, strict data privacy requirements, or those that require autonomous capabilities, ultra-low latency, or operate in areas with low connectivity. A 2020 Forrester report lists a number of use cases where edge computing can be used in various industries to improve IoT [2]. Their list includes:

  • Automotive: self-driving cars and vehicle infotainment
  • Manufacturing: factory automation and remote supervision
  • Healthcare: robotic telesurgery and remote patient monitoring
  • Media and Entertainment: immersive video applications and AR/VR games
  • Smart Cities and Utilities: smart buildings and smart transportation

Edge Computing with Azion

Azion’s Edge Computing Platform maximizes the benefits of edge computing for IoT with a serverless model that reduces costs and simplifies day-to-day operations, providing businesses more time to develop innovative IoT solutions. With our serverless platform, companies pay only for the resources they use, rather than partitioning ahead of time, thus simplifying resource management, eliminating costs from wasted resources, and lowering the barrier to entry with lower upfront costs.

In addition, our serverless computing solution, Edge Functions, makes it easy for developers to build and run event-driven functions, an ideal solution for IoTs that generate volumes of information that must be analyzed and acted upon in real time. As explained by a 2020 article in Read-Write magazine, “database usage requires moderate data rates and no need for real-time responses. But that’s not going to work when you have massive streams of data coming in each second that need immediate analysis.” Edge Functions, which scale automatically in response to events on the edge of the network, provide the scalability and real-time processing needed for IoT applications.

To learn more about Edge Functions’ features, benefits, and pricing, visit the product page on our website or contact sales for more information about Azion and IoT.

References:

[1] Goodness, E. (2019). The Future of the IoT Platform and Solutions Market (pp. 7-10, Rep.). San Diego, CA: Gartner.

[2] Pelino, M., & Staten, J. (2020). How 5G And Edge Computing Advance IoT Value (p. 16, Rep.). Cambridge, MA: Forrester.