Posted on Wednesday January 25, 2023
zigron | 8 min read

Edge Computing: The Key to Unlocking the Potential of IOT + AI

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Edge computing is a type of distributed computing that brings computation and data storage closer to the location where it is needed, making real-time responses possible. This article will explore the benefits of edge computing, its various applications, and the challenges faced by those who wish to implement it. Zigron is the right partner for edge computing solutions, so if you’re interested in learning more, be sure to get in contact with us.

 

What is Edge Computing?

 

 

Edge computing is a type of distributed computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. In edge computing, data is processed at the edge of the network, instead of being sent to a centralized data center or cloud. This can be done either by installing computational resources (e.g. servers, storage) at the edge of the network, or by using mobile devices that are capable of processing data locally. Edge computing is often used in situations where real-time responses are required, or where bandwidth or latency issues would make centralized processing impractical. Some common examples of edge computing applications include streaming video, virtual reality, gaming, autonomous vehicles, and Internet of Things (IoT) devices.

 

The Benefits of Edge Computing

 

 

  • Reduced Costs: By processing data locally, businesses can avoid the high costs associated with sending data to and from a central location. In addition, edge computing can help businesses to save on bandwidth and storage costs.
  • Increased Efficiency: By bringing computation and data storage closer to the devices that need it, businesses can avoid the delays that are associated with sending data to and from a central location. This can be especially beneficial for businesses that need to provide real-time responses to their customers.
  • Improved Performance: By processing data locally, businesses can avoid the latency issues that are associated with sending data to and from a central location. This can help businesses to provide a better experience for their customers.
  • Greater Reliability: By distributing computational resources across multiple locations, businesses can avoid the single point of failure that is associated with centralized systems. In addition, edge computing can help businesses to improve uptime by providing backup resources in case of an outage.
  • Increased Security: By processing data locally, businesses can keep sensitive information out of the hands of hackers who may try to access it through a central server. In addition, edge computing can help businesses to protect against denial-of-service attacks by distributing computational resources across multiple locations.

Technologies used in Edge Computing

 

 

  • IoT and Edge AI: IoT devices are increasingly being integrated with edge computing to allow for real-time data processing and decision making, this is known as IoT edge intelligence.
  • Mobile Edge Computing: Mobile edge computing (MEC) is a type of edge computing that brings computation and data storage closer to mobile devices.
  • Fog Computing: also known as Edge Cloud, it brings computation and data storage closer to the edge of the network, but it is often used in situations where real-time responses are not required, but where bandwidth or latency issues would make centralized processing impractical.

Challenges in Edge Computing

 

 

  • Higher Costs: While fog computing can save businesses money by reducing bandwidth requirements, the initial cost of deploying a fog system can be higher than the cost of deploying a traditional centralized system. This is due to the fact that fog systems typically require more hardware and software than traditional systems.
  • Security: Ensuring the security of data processed at the edge is a major challenge, as edge devices may not have the same level of security as centralized data centers.
  • Interoperability: A lack of standardization in edge computing can make it difficult for different devices and systems to communicate with one another.
  • Management and Maintenance: As edge devices are distributed and may be located in remote or hard-to-reach locations, managing and maintaining them can be difficult and costly.
  • Integration with existing infrastructure: Integrating edge computing solutions with existing IT infrastructure can be challenging and may require significant changes to current systems.

Applications of Edge Computing:

 

 

  • IoT: Edge computing is particularly useful in the context of the Internet of Things (IoT) as it allows for real-time data processing and decision making at the edge of the network, reducing the need to transmit large amounts of data to a central location.
  • Autonomous vehicles: Edge computing can be used to process sensor data in real-time and make decisions for autonomous vehicles, allowing for faster and more accurate navigation.
  • Industrial Automation: Edge computing can be used to process data from industrial sensors and machines in real-time, allowing for faster and more accurate decision making in manufacturing and industrial processes.
  • Video and Media Streaming: Edge computing can be used to process and deliver video and media content in real-time, improving the user experience.

Challenges in Edge Computing and Potential Solutions

One of the challenges of Edge Computing is cost. Installing and maintaining computational resources at the edge of the network can be expensive. In addition, mobile devices that are capable of processing data locally can also be costly. Another challenge of Edge Computing is complexity. The distributed nature of Edge Computing can make it difficult to manage and monitor. Security is also a concern, as data stored at the edge of the network is more vulnerable to attack.

Another challenge is performance. Edge Computing systems can be slower than traditional centralized systems, due to the increased latency associated with data being processed at the

edge of the network. Scalability is also an issue, as it can be difficult to add new nodes to a distributed system.

Despite these challenges, there are potential solutions that can help mitigate them. For example, using lower-cost devices or installing computational resources in locations that have existing infrastructure can help reduce costs. In addition, careful planning and management can help reduce complexity and improve security. And finally, using caching and other techniques can help improve performance and scalability.

Edge Computing and Big Data Analytics

 

 

Big data analytics is the process of examining large and complex data sets to uncover hidden patterns, correlations, and other insights. Data sets can be generated by a variety of sources, such as social media, sensors, transactions, and more. The goal of big data analytics is to help organizations make better decisions by providing them with insights that would otherwise be unavailable.

Edge computing can play a role in big data analytics in two main ways. First, edge computing can help to reduce the amount of data that needs to be sent to the cloud for processing. This can save on bandwidth costs and reduce latency. Second, data processed at the edge can be

used to train and improve machine learning models. Machine learning models that are deployed at the edge can make predictions based on data from IoT devices in real-time.

There are a few challenges that need to be considered when using edge computing for big data analytics. First, edge devices tend to be less powerful than cloud servers, so they may not be able to handle all of the processing required for complex data sets. Second, there is a risk that sensitive data will be stored onedge devices, which could be lost or stolen. Finally, managing a fleet of edge devices can be complex and time-consuming.

Despite these challenges, edge computing can offer significant advantages for big data analytics. By bringing computation and storage closer to the source of the data, edge computing can help to reduce costs, latency, and privacy concerns. In addition, edge computing can enable real-time insights by providing access to low-latency data sources.

Edge Computing and Security

Edge computing brings data storage and computation closer to the devices or people that need it. This can be done by installing computational resources at the edge of the network or using mobile devices that are capable of processing data locally. Edge computing is often used in situations where real-time responses are required or where bandwidth or latency issues would make centralized processing impractical.

Security is a key concern for businesses when it comes to edge computing. Data encryption is one common security measure that is used, as well as other measures such as firewalls, intrusion detection and prevention systems, and virtual private networks. The physical security of edge computing devices is also important, as they are often located in remote or hard-to-reach locations.

There are many benefits that businesses can reap from using edge computing. However, it is important to consider the security risks involved before implementing anyedge computing solutions. By taking proper precautions and implementing effective security measures, businesses can help mitigate the risks associated with edge computing.

Edge Computing Deployment

When deploying an edge computing solution, businesses need to consider a number of factors such as data processing needs, data security, and performance requirements.

Data processing needs should be assessed in order to determine the best way to collect, process, and store data. Data security is also an important consideration, as sensitive data may

be stored on edge devices. Performance requirements must be considered when designing and deploying an edge computing solution, as applications must be able to meet the demands of real-time data processing.

Edge devices must have the necessary processing power and storage capacity to support the applications that will be running on them. Edge computing solutions must also be designed with scalability in mind, as businesses may need to add more devices or increase the amount of data processed over time.

Testing applications is essential to ensure that they meet performance requirements. Edge computing solutions should also be monitored and updated on a regular basis to ensure that they continue to meet the needs of businesses.

The Future of Edge Computing

The future of edge computing looks promising, with more powerful devices, increased use of AI, better edge-cloud integration, and new applications. Security and privacy concerns will continue to be important considerations.

As devices become more powerful, they will be able to handle more data processing locally. This will reduce the need to send data to the cloud for processing, which can save on bandwidth and processing costs. In addition, more powerful devices will be able to support more demanding applications.

AI will play an increasingly important role in edge computing. Machine learning models can be trained on data that is processed at the edge, which can improve the accuracy of predictions. In addition, AI can be used to manage resources and optimize performance.

Edge-cloud integration will become increasingly important as businesses look to take advantage of both the benefits of the cloud and the benefits of edge computing. A hybrid approach that combines the two can offer the best of both worlds.

New applications for edge computing are being developed all the time. As businesses become more aware of the potential of edge computing, they are finding new ways to use it to improve efficiency and performance.

Why Zigron is the Right Partner for Edge Computing Solutions

 

 

Edge computing is a powerful technology that can bring computation and data storage closer to the location where it is needed, improving response times and reducing costs. However, implementing edge computing solutions can be challenging and requires careful consideration of costs, security, and integration with existing systems. Zigron is the right partner for edge computing solutions because of their extensive experience in the field. We can help companies develop edge computing based solutions that are tailored to their specific needs. Our team of experts are available to answer any questions you may have and they offer a free consultation to discuss your specific needs. Zigron has a proven track record of helping companies successfully implement edge computing solutions. Contact us today at sales@zigron.com or 412-478-6588 to get started on your project.

 



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