Despite edge computing in SCADA emerging as a technology with the promise of creating a faster IoT ecosystem, the road to bandwidth efficiency isn’t obstacle-free, especially for manufacturing—an early adopter in this tech race.

Predicted to account for 84% of the edge market in 2030 (along with media and transport), manufacturing’s use of edge for industrial automation has many challenges due to the complex architecture of distributed devices.

From limited resources to communication glitches, challenges in edge computing could make the difference between smooth operations and costly disruptions. That’s why in this article we’ll break down the top five challenges in edge computing along with examples and solutions for you to tackle them.

#1. Limited Computational Resources

Edge devices work with limited processing power, memory, and storage capabilities. This is often seen as an advantage, as it helps minimize energy consumption from edge devices powered by batteries.

However, this also represents one of the main edge computing challenges, as deploying applications and other services may require additional computational resources.

Example: Picture the use of edge devices in offshore oil rigs. Here, it’s extremely important to collect and analyze information for real-time analysis. When it comes to predictive maintenance and the optimization of drilling operations, complex algorithms could provide valuable insights. But with the lack of computational resources running these algorithms, it becomes a challenge.



  • Lean code: Use lightweight algorithms to reduce the code size and memory consumed in edge devices.
  • Data filtering: Leverage algorithms that filter and prioritize data at the edge so you can reduce the size of the information that needs to be processed.
  • Edge-Cloud collaboration: Create a hybrid approach in which you load tasks that require more resources for edge devices into the cloud.

#2. Poor Performance Due to Data Latency

Although the proximity of edge devices to data sources reduces the volume of data transmitted to centralized servers, managing local network conditions is still one of the challenges of edge computing. Local networks can still experience congestion and interference which affects performance.

Example: Going back to the oil rig example, imagine engineers are using edge devices to analyze seismic data in real time to make decisions in drilling operations. While edge devices are physically close, the network infrastructure and the need to transfer large quantities of data can introduce latency locally.



  • Edge device location: Place edge devices closer to the data source to reduce the distance data needs to travel, which minimizes latency.
  • Content Delivery Networks (CDNs): Use CDNs to optimize the distribution of data closer to edge devices.
  • Edge caching: Use caching mechanisms to store frequently used information at the edge. Meanwhile, less critical data can be sent to centralized servers.

#3. Security Concerns

Edge computing security challenges include surface attacks due to the distributed nature of edge environments. Despite edge computing being a vulnerable technology when it comes to cyber threats, it also has a number of solutions available that make it a fierce technology when protecting sensitive data.

Example: There’s a manufacturing plant in which edge devices control critical machinery. Unfortunately, you have weak authentication methods in place which give malicious actors the opportunity to gain unauthorized access to one of the edge devices. This actor not only steals sensitive data but also disrupts your operations.



  • Data encryption protocols: Prevent unauthorized access to sensitive information by implementing end-to-end encryption securing both stored and in transit data.
  • Intrusion Detection/Prevention Systems (IDS/IPS): These systems can monitor your network to detect and prevent malicious activities at the edge.
  • Secure boot mechanism: Use secure boot processes to analyze firmware and software before they’re executed on the edge.

#4. Communication Issues Between Devices

Another one of the main challenges in edge computing is interoperability or communication issues between devices. There are diverse edge devices, platforms, and communication protocols collaborating for the effectiveness of edge computing deployments. And seamless communication between them isn’t always guaranteed.

Example: Picture a building that operates sensors and controls lighting at the edge. If any of these components use a different communication protocol or are incompatible with the central control system, integrating an effective IoT ecosystem becomes a challenge. Moreover, scalability and the efficiency of the building are compromised.



  • Standardized protocols: Use industry-standard communication protocols, such as MQTT (Message Queuing Telemetry Transport) or HTTP/HTTPS (Hypertext Transfer Protocol/Secure).
  • APIs and interfaces: APIs can define the methods and protocols that edge devices can use to communicate facilitating the interaction.
  • Edge orchestration: As its name suggests, you can manage and coordinate the communication between different edge devices using cloud computing platforms or open-source solutions.

#5. Efficient Data Management and Storage

The last one of the challenges in edge computing that we’ll address is efficient data management and storage. The increasing volume of data generated at the edge daily (if not managed and stored correctly) can be lost, causing delayed decision-making and security risks.

Example: Imagine a smart building with multiple IoT sensors that track energy usage and temperature. These represent continuous amounts of data every day at every hour. However, edge devices have limited storage available, meaning engineers have to determine which data should be stored locally and which data should be sent to the cloud for long-term storage while ensuring that critical data isn’t lost.



  • Tiered storage: Implement a strategy in which data is categorized based on its importance and access frequency.
  • Edge analytics and filtering: Use edge analytics, filtering, and aggregation to only store the data you need.
  • Local edge caching: Store frequently accessed data at the edge with local caching, as it reduces the time to get data needed for repetitive tasks.

Mastering Obstacles at the Edge

The benefits and challenges associated with edge computing create a dynamic of innovation vs. struggles. On one side, we have the visionary promise of a decentralized and responsive IoT ecosystem. But on the other hand, we encounter practical hurdles such as limited resources, communication glitches, and security.

The thing is, edge computing brings the opportunity to turn challenges into stepping stones to unlock its full potential. It also gives way for innovation, prompting solutions that not only mitigate challenges but elevate the edge computing landscape.

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