Top 10 Edge Network Technologies in 2023-24

Edge network technologies are reshaping the digital landscape by bringing computation and storage closer to end-users. This revolutionary shift is paramount for real-time applications such as augmented reality (AR), virtual reality (VR), and self-driving cars. As data generated by connected devices continues to skyrocket, the importance of edge networks cannot be overstated.

Edge networks are becoming increasingly important as the amount of data generated by connected devices continues to grow. Edge networks can help to process and analyze this data closer to the source, which can improve efficiency and reduce costs. In this comprehensive guide, we delve into the top 10 edge network technologies of 2023-24, providing expert insights and firsthand knowledge to showcase the incredible potential of these advancements.

Benefits of Edge Network Technologies

Edge network technologies offer a number of benefits, including:

Improved performance and reduced latency: Edge networks can deliver real-time performance for applications that require low latency, such as AR, VR, and self-driving cars.
Increased scalability: Edge networks can be scaled to support a large number of devices and applications.
Reduced costs: Edge networks can help to reduce costs by processing data closer to the source.
Improved security: Edge networks can be more secure than traditional networks because data is processed and stored closer to the user.

Top 10 Edge Network Technologies in 2023-24

The following are the top 10 edge network technologies in 2023-24

1. 5G

5G networks provide the high bandwidth and low latency needed for edge computing applications.

5G networks provide a number of advantages over previous generations of cellular networks, including:

Higher bandwidth: 5G networks can support peak data rates of up to 10 Gbps, which is significantly faster than 4G LTE networks.
Lower latency: 5G networks can achieve latency of less than 1 millisecond, which is essential for real-time applications such as AR, VR, and self-driving cars.
Greater capacity: 5G networks can support more devices and traffic than previous generations of cellular networks.

These advantages make 5G networks ideal for edge computing applications. Edge computing applications require high bandwidth, low latency, and the ability to process data close to the source. 5G networks can provide all of these things, which is why they are expected to play a major role in the development of edge computing.

Here are some specific examples of how 5G can be used to support edge computing applications:

AR and VR: 5G can provide the high bandwidth and low latency needed for AR and VR applications to deliver a seamless and immersive experience.
Self-driving cars: 5G can provide the low latency needed for self-driving cars to make real-time decisions about their surroundings.
Industrial IoT: 5G can be used to connect industrial machines and sensors to the cloud, which can enable real-time monitoring and control of industrial processes.
Smart cities: 5G can be used to connect sensors and devices in smart cities to the cloud, which can enable real-time monitoring and management of traffic, energy, and other city services.

As 5G networks become more widely deployed, we can expect to see even more innovative and disruptive edge computing applications emerge.

In addition to the above, 5G networks can also be used to support edge computing applications by providing:

More reliable connectivity: 5G networks are expected to be more reliable than previous generations of cellular networks, which is important for edge computing applications that require high availability.
More secure connectivity: 5G networks are expected to be more secure than previous generations of cellular networks, which is important for edge computing applications that handle sensitive data.
More flexibility: 5G networks are more flexible than previous generations of cellular networks, which makes it easier to deploy and manage edge computing applications.

Overall, 5G networks are a key enabler for edge computing. By providing high bandwidth, low latency, and greater capacity, 5G networks can enable a new generation of edge computing applications that are more powerful, reliable, and secure.

2. Edge computing

Edge computing brings computation and storage closer to the end user, which can improve performance and reduce latency.

Edge computing is a distributed computing paradigm that brings computation and storage closer to the end user. This can improve performance and reduce latency for applications that require real-time processing, such as augmented reality (AR), virtual reality (VR), and self-driving cars.

In traditional cloud computing, data is collected and processed in centralized data centers. This can lead to latency issues, especially for applications that require real-time processing. Edge computing solves this problem by bringing computation and storage closer to the source of the data. This reduces the distance that data needs to travel, which improves performance and reduces latency.

Edge computing can be implemented in a variety of ways. One common approach is to deploy edge servers in strategic locations, such as at cell towers, data centers, and edge computing gateways. Edge servers can be used to process data locally and store it temporarily, which can improve performance for applications that need to access data quickly.

Another approach to edge computing is to use fog computing. Fog computing is a distributed computing paradigm that extends cloud computing to the edge of the network. Fog computing devices can be deployed at the edge of the network, such as on industrial machines, smart devices, and vehicles. Fog computing devices can process data locally and store it temporarily, which can improve performance for applications that need to access data quickly.

Edge computing offers a number of benefits, including:

Improved performance and reduced latency: Edge computing can deliver real-time performance for applications that require low latency, such as AR, VR, and self-driving cars.
Increased scalability: Edge computing can be scaled to support a large number of devices and applications.
Reduced costs: Edge computing can help to reduce costs by processing data closer to the source.
Improved security: Edge networks can be more secure than traditional networks because data is processed and stored closer to the user.

Edge computing is still in its early stages of development, but it has the potential to revolutionize the way we use the internet. By bringing computation and storage closer to the end user, edge computing can enable new applications and services that were not possible before.

Here are some specific examples of edge computing applications:

AR and VR: Edge computing can be used to deliver AR and VR experiences with low latency and high fidelity.
Self-driving cars: Edge computing can be used to process data from sensors and cameras in real time to help self-driving cars navigate safely.
Industrial IoT: Edge computing can be used to monitor and control industrial machines and processes in real time.
Smart cities: Edge computing can be used to monitor and manage traffic, energy, and other city services in real time.
Healthcare: Edge computing can be used to collect and process medical data from sensors and devices in real time to improve patient care.

As edge computing technology continues to develop, we can expect to see even more innovative and disruptive applications emerge.

Also read: Top 10 Edge Computing Companies in the World

3. Artificial intelligence (AI)

AI can be used to optimize edge networks and applications, such as by predicting traffic patterns and routing traffic more efficiently.

AI can be used to optimize edge networks and applications in a number of ways, including:

Predicting traffic patterns: AI can be used to predict traffic patterns on edge networks and applications. This information can be used to optimize network resources and routing decisions.
Routing traffic more efficiently: AI can be used to route traffic more efficiently on edge networks. This can be done by considering factors such as network congestion, latency, and application requirements.
Optimizing resource allocation: AI can be used to optimize resource allocation on edge devices. This can be done by considering factors such as device capabilities, application requirements, and energy consumption.
Detecting and preventing cyberattacks: AI can be used to detect and prevent cyberattacks on edge networks and applications. This can be done by analyzing network traffic and device behavior for suspicious activity.
Improving the performance of edge applications: AI can be used to improve the performance of edge applications by optimizing algorithms and data processing.

Here are some specific examples of how AI is being used to optimize edge networks and applications today:

5G networks: AI is being used to optimize 5G networks for performance and reliability. For example, AI is being used to predict traffic patterns and allocate network resources more efficiently.
Self-driving cars: AI is being used to optimize self-driving cars for safety and efficiency. For example, AI is being used to predict traffic patterns and route self-driving cars around congestion.
Industrial IoT: AI is being used to optimize industrial IoT networks for efficiency and productivity. For example, AI is being used to predict machine failures and schedule maintenance more effectively.
Smart cities: AI is being used to optimize smart city networks for efficiency and sustainability. For example, AI is being used to predict traffic patterns and optimize traffic flow.

As AI technology continues to develop, we can expect to see even more innovative and effective ways to use AI to optimize edge networks and applications.

In addition to the above, AI can also be used to optimize edge networks and applications by:

Providing insights into network and application performance: AI can be used to analyze data from edge networks and applications to identify trends and patterns. This information can be used to improve network and application performance.
Automating tasks: AI can be used to automate tasks such as network configuration, security monitoring, and application management. This can free up human resources to focus on more strategic tasks.
Improving the user experience: AI can be used to improve the user experience of edge applications by personalized recommendations, real-time insights, and predictive analytics.

Overall, AI has the potential to revolutionize the way we optimize edge networks and applications. By automating tasks, providing insights, and improving the user experience, AI can help us to create edge networks and applications that are more efficient, reliable, and user-friendly.

4. Machine learning (ML)

ML can be used to detect and prevent cyberattacks, as well as to improve the performance of edge applications.

Machine learning (ML) can be used to detect and prevent cyberattacks on edge networks and applications in a number of ways, including:

Anomaly detection: ML can be used to identify anomalies in network traffic and device behavior that may indicate a cyberattack.
Malware detection: ML can be used to detect malware on edge devices.
Intrusion detection: ML can be used to detect unauthorized access to edge networks and applications.
Zero-day attack detection: ML can be used to detect zero-day attacks, which are attacks that exploit vulnerabilities that are not yet known to the public.

ML can also be used to improve the performance of edge applications by:

Optimizing algorithms: ML can be used to optimize algorithms for edge applications. This can improve the efficiency and performance of the applications.
Improving data processing: ML can be used to improve data processing for edge applications. This can reduce the latency and improve the accuracy of the applications.
Personalizing the user experience: ML can be used to personalize the user experience for edge applications. This can improve the user satisfaction and engagement with the applications.

Here are some specific examples of how ML is being used to detect and prevent cyberattacks on edge networks and applications today:

5G networks: ML is being used to detect and prevent cyberattacks on 5G networks. For example, ML is being used to identify anomalies in network traffic and device behavior that may indicate a denial-of-service attack.
Self-driving cars: ML is being used to detect and prevent cyberattacks on self-driving cars. For example, ML is being used to identify anomalies in sensor data that may indicate a hack.
Industrial IoT: ML is being used to detect and prevent cyberattacks on industrial IoT networks. For example, ML is being used to identify anomalies in machine data that may indicate a malware infection.
Smart cities: ML is being used to detect and prevent cyberattacks on smart city networks. For example, ML is being used to identify anomalies in traffic data that may indicate a denial-of-service attack.

As ML technology continues to develop, we can expect to see even more innovative and effective ways to use ML to detect and prevent cyberattacks on edge networks and applications.

In addition to the above, ML can also be used to detect and prevent cyberattacks on edge networks and applications by:

Using artificial intelligence (AI): AI can be used to improve the accuracy and efficiency of ML models.
Using edge computing: ML models can be deployed on edge devices, which can reduce latency and improve performance.
Using open source tools: There are a number of open source ML tools that can be used to detect and prevent cyberattacks on edge networks and applications.

Overall, ML has the potential to revolutionize the way we detect and prevent cyberattacks on edge networks and applications. By automating tasks, providing insights, and improving the accuracy of detection, ML can help us to create edge networks and applications that are more secure and resilient.

5. Network slicing

Network slicing allows operators to create multiple virtual networks on the same physical infrastructure, which can be used to support different types of edge applications.

Network slicing is a technology that allows operators to create multiple virtual networks on the same physical infrastructure. This allows operators to support different types of edge applications, each with its own unique requirements.

For example, an operator could create a slice for self-driving cars that requires low latency and high reliability. The operator could then create another slice for industrial IoT applications that requires high bandwidth and security.

Network slicing is still in its early stages of development, but it has the potential to revolutionize the way we deploy and manage edge networks. By allowing operators to create multiple virtual networks on the same physical infrastructure, network slicing can help to make edge networks more efficient, scalable, and secure.

Here are some specific examples of how network slicing can be used to support edge applications:

Self-driving cars: Network slicing can be used to create a dedicated network for self-driving cars that provides low latency and high reliability. This network can be used to transmit sensor data from the cars to the cloud and to receive instructions from the cloud.
Industrial IoT: Network slicing can be used to create a dedicated network for industrial IoT applications that provides high bandwidth and security. This network can be used to transmit data from sensors and machines to the cloud and to receive instructions from the cloud.
Smart cities: Network slicing can be used to create a dedicated network for smart city applications that provides a variety of services, such as traffic management, energy efficiency, and public safety.
Healthcare: Network slicing can be used to create a dedicated network for healthcare applications that provides high security and reliability. This network can be used to transmit medical data from patients to doctors and to receive instructions from doctors.

As network slicing technology continues to develop, we can expect to see even more innovative and disruptive edge applications emerge.

In addition to the above, network slicing can also be used to support edge applications by:

Providing isolation: Network slicing can be used to isolate different types of edge applications from each other. This can help to improve security and performance.
Providing flexibility: Network slicing can be used to create networks with different characteristics, such as latency, bandwidth, and security. This flexibility can be used to support a wide range of edge applications.
Providing scalability: Network slicing can be used to scale networks up or down as needed. This scalability can be used to support edge applications that have fluctuating demands.

Overall, network slicing is a powerful technology that can be used to support a wide range of edge applications. By providing isolation, flexibility, and scalability, network slicing can help to make edge networks more secure, reliable, and efficient.

6. Network function virtualization (NFV)

NFV allows operators to virtualize network functions, which can make it easier to deploy and manage edge networks.

Network function virtualization (NFV) is a technology that allows operators to virtualize network functions. This means that network functions, such as routers, firewalls, and load balancers, can be run on software instead of dedicated hardware.

NFV has a number of benefits for edge networks, including:

Efficiency: NFV can help to improve the efficiency of edge networks by reducing the need for dedicated hardware. This can save operators money and reduce the environmental impact of their networks.
Scalability: NFV can help to make edge networks more scalable by allowing operators to add or remove network functions as needed. This can be useful for edge networks that need to support fluctuating demand.
Flexibility: NFV can help to make edge networks more flexible by allowing operators to deploy and manage network functions more easily. This can be useful for edge networks that need to support a variety of different applications.
Agility: NFV can help to make edge networks more agile by allowing operators to deploy new network services more quickly. This can be useful for edge networks that need to support new applications and services on a regular basis.

Here are some specific examples of how NFV can be used in edge networks:

Edge routers: NFV can be used to deploy edge routers that are closer to the end user. This can improve performance and reduce latency for applications that require real-time processing.
Edge firewalls: NFV can be used to deploy edge firewalls that can be customized to meet the specific needs of each edge network. This can help to improve security and performance.
Edge load balancers: NFV can be used to deploy edge load balancers that can distribute traffic across multiple edge servers. This can help to improve performance and reliability.

As NFV technology continues to develop, we can expect to see even more innovative and effective ways to use NFV in edge networks.

In addition to the above, NFV can also be used in edge networks by:

Using open source software: There are a number of open source NFV solutions that can be used to deploy and manage edge networks. This can help to reduce costs and speed up deployment.
Using containerization: NFV functions can be packaged into containers, which can make them easier to deploy and manage.
Using orchestration tools: NFV orchestration tools can be used to automate the deployment and management of NFV functions. This can help to improve efficiency and reduce human error.

Overall, NFV is a powerful technology that can be used to make edge networks more efficient, scalable, flexible, agile, and secure. By virtualizing network functions, NFV can help operators to deploy and manage edge networks more easily and cost-effectively.

SDN allows operators to control and manage networks using software, which can make it easier to deploy and manage edge networks.

Software-defined networking (SDN) is a technology that separates the control plane from the data plane in a network. This allows operators to control and manage networks using software, which can make it easier to deploy and manage edge networks.

In a traditional network, the control plane and the data plane are tightly coupled. This means that the control plane is responsible for managing the flow of traffic, while the data plane is responsible for forwarding traffic.

In an SDN network, the control plane and the data plane are decoupled. This means that the control plane is responsible for managing the flow of traffic, while the data plane is responsible for forwarding traffic based on instructions from the control plane.

SDN has a number of benefits for edge networks, including:

Centralized management: SDN allows operators to centrally manage the network from a single point. This can simplify network management and make it easier to troubleshoot problems.
Flexibility: SDN allows operators to flexibly configure the network to meet the specific needs of each edge network. This can be useful for edge networks that need to support a variety of different applications.
Agility: SDN allows operators to deploy new network services more quickly. This can be useful for edge networks that need to support new applications and services on a regular basis.
Security: SDN can be used to implement security policies more effectively. This can help to protect edge networks from cyberattacks.

Here are some specific examples of how SDN can be used in edge networks:

Traffic shaping: SDN can be used to shape traffic at the edge of the network. This can help to improve performance and reduce latency for applications that require real-time processing.
Security filtering: SDN can be used to filter traffic at the edge of the network. This can help to protect edge networks from cyberattacks.
Application load balancing: SDN can be used to load balance traffic across multiple edge servers. This can help to improve performance and reliability.

As SDN technology continues to develop, we can expect to see even more innovative and effective ways to use SDN in edge networks.

In addition to the above, SDN can also be used in edge networks by:

Using open source software: There are a number of open source SDN solutions that can be used to deploy and manage edge networks. This can help to reduce costs and speed up deployment.
Using cloud computing: SDN can be deployed on cloud computing platforms, which can make it easier to deploy and manage edge networks.
Using orchestration tools: SDN orchestration tools can be used to automate the deployment and management of SDN networks. This can help to improve efficiency and reduce human error.

Overall, SDN is a powerful technology that can be used to make edge networks more efficient, flexible, agile, secure, and cost-effective. By separating the control plane from the data plane, SDN can help operators to deploy and manage edge networks more easily and cost-effectively.

8. Fog computing

Fog computing is a distributed computing paradigm that extends cloud computing to the edge of the network. This means that fog computing devices are deployed closer to the end user, which can improve performance and reduce latency for applications that require real-time processing.

Fog computing devices can be a variety of different types of devices, such as routers, switches, gateways, and servers. These devices can be deployed in a variety of different locations, such as at cell towers, data centers, and edge computing nodes.

Fog computing has a number of benefits for edge networks, including:

Improved performance: Fog computing can help to improve performance for applications that require real-time processing by reducing the distance that data needs to travel.
Reduced latency: Fog computing can help to reduce latency for applications that require real-time processing by processing data closer to the end user.
Increased scalability: Fog computing can help to increase the scalability of edge networks by distributing processing power across multiple fog computing devices.
Improved security: Fog computing can help to improve the security of edge networks by processing data closer to the end user, which can make it more difficult for attackers to access data.

Here are some specific examples of how fog computing can be used in edge networks:

Self-driving cars: Fog computing can be used to process sensor data from self-driving cars in real time. This can help self-driving cars to make decisions more quickly and safely.
Industrial IoT: Fog computing can be used to collect and process data from industrial sensors and machines in real time. This can help industrial companies to improve efficiency and productivity.
Smart cities: Fog computing can be used to collect and process data from smart city sensors and devices in real time. This can help city governments to improve traffic management, energy efficiency, and public safety.
Healthcare: Fog computing can be used to collect and process medical data from patients in real time. This can help healthcare providers to improve patient care.

As fog computing technology continues to develop, we can expect to see even more innovative and effective ways to use fog computing in edge networks.

In addition to the above, fog computing can also be used in edge networks by:

Using open source software: There are a number of open source fog computing solutions that can be used to deploy and manage edge networks. This can help to reduce costs and speed up deployment.
Using containerization: Fog computing applications can be packaged into containers, which can make them easier to deploy and manage.
Using orchestration tools: Fog computing orchestration tools can be used to automate the deployment and management of fog computing applications. This can help to improve efficiency and reduce human error.

Overall, fog computing is a powerful technology that can be used to make edge networks more efficient, scalable, secure, and reliable. By extending cloud computing to the edge of the network, fog computing can help operators to deploy and manage edge networks more easily and cost-effectively.

9. Distributed ledger technology (DLT)

DLT can be used to create secure and tamper-proof records of edge network transactions.

Distributed ledger technology (DLT) is a type of database that is shared and synchronized across multiple computers. This makes it very difficult to tamper with the data, as any changes would need to be made to all of the copies of the ledger.

DLT can be used to create secure and tamper-proof records of edge network transactions. This can be useful for a variety of applications, such as:

Smart contracts: DLT can be used to create smart contracts, which are self-executing contracts that are stored on the blockchain. This can help to automate edge network transactions and reduce the need for manual intervention.
Identity management: DLT can be used to create secure and tamper-proof identity management systems. This can help to improve security and privacy for edge network users.
Asset tracking: DLT can be used to track the movement of assets through the edge network. This can help to improve visibility and accountability for edge network operators.
Fraud prevention: DLT can be used to prevent fraud in edge networks. This can be done by tracking the movement of data and assets through the network.

Here are some specific examples of how DLT can be used in edge networks:

Self-driving cars: DLT can be used to record the sensor data from self-driving cars in a tamper-proof manner. This data can then be used to improve the safety and performance of self-driving cars.
Industrial IoT: DLT can be used to record the data from industrial sensors and machines in a tamper-proof manner. This data can then be used to improve the efficiency and productivity of industrial operations.
Smart cities: DLT can be used to record the data from smart city sensors and devices in a tamper-proof manner. This data can then be used to improve traffic management, energy efficiency, and public safety.
Healthcare: DLT can be used to record the medical data from patients in a tamper-proof manner. This data can then be used to improve patient care and research.

As DLT technology continues to develop, we can expect to see even more innovative and effective ways to use DLT in edge networks.

In addition to the above, DLT can also be used in edge networks by:

Using open source software: There are a number of open source DLT solutions that can be used to deploy and manage edge networks. This can help to reduce costs and speed up deployment.
Using cloud computing: DLT can be deployed on cloud computing platforms, which can make it easier to deploy and manage edge networks.
Using orchestration tools: DLT orchestration tools can be used to automate the deployment and management of DLT networks. This can help to improve efficiency and reduce human error.

Overall, DLT is a powerful technology that can be used to make edge networks more secure, reliable, and transparent. By creating secure and tamper-proof records of edge network transactions, DLT can help to improve trust and confidence in edge networks.

10. Blockchain

Blockchain is a type of DLT that can be used to create secure and tamper-proof records of edge network transactions.

Blockchain is a type of DLT that is best known for its use in cryptocurrencies such as Bitcoin. However, blockchain can also be used to create secure and tamper-proof records of edge network transactions.

Blockchain works by using a distributed ledger that is shared and synchronized across multiple computers. This makes it very difficult to tamper with the data, as any changes would need to be made to all of the copies of the ledger.

Blockchain can be used to create secure and tamper-proof records of edge network transactions by using a process called “mining.” Miners are computers that are responsible for verifying and adding new transactions to the blockchain. Miners are rewarded for their work with cryptocurrency.

Once a transaction is added to the blockchain, it is very difficult to remove or change. This is because each block in the blockchain contains a cryptographic hash of the previous block. This means that if any change is made to a block, the hash of the block will change. This change will be detected by miners, and the block will be rejected.

Blockchain can be used to create secure and tamper-proof records of edge network transactions for a variety of applications, such as:

Smart contracts: Blockchain can be used to create smart contracts, which are self-executing contracts that are stored on the blockchain. This can help to automate edge network transactions and reduce the need for manual intervention.
Identity management: Blockchain can be used to create secure and tamper-proof identity management systems. This can help to improve security and privacy for edge network users.
Asset tracking: Blockchain can be used to track the movement of assets through the edge network. This can help to improve visibility and accountability for edge network operators.
Fraud prevention: Blockchain can be used to prevent fraud in edge networks. This can be done by tracking the movement of data and assets through the network.

Here are some specific examples of how blockchain can be used in edge networks:

Self-driving cars: Blockchain can be used to record the sensor data from self-driving cars in a tamper-proof manner. This data can then be used to improve the safety and performance of self-driving cars.
Industrial IoT: Blockchain can be used to record the data from industrial sensors and machines in a tamper-proof manner. This data can then be used to improve the efficiency and productivity of industrial operations.
Smart cities: Blockchain can be used to record the data from smart city sensors and devices in a tamper-proof manner. This data can then be used to improve traffic management, energy efficiency, and public safety.
Healthcare: Blockchain can be used to record the medical data from patients in a tamper-proof manner. This data can then be used to improve patient care and research.

As blockchain technology continues to develop, we can expect to see even more innovative and effective ways to use blockchain in edge networks.

In addition to the above, blockchain can also be used in edge networks by:

Using open source software: There are a number of open source blockchain solutions that can be used to deploy and manage edge networks. This can help to reduce costs and speed up deployment.
Using cloud computing: Blockchain can be deployed on cloud computing platforms, which can make it easier to deploy and manage edge networks.
Using orchestration tools: Blockchain orchestration tools can be used to automate the deployment and management of blockchain networks. This can help to improve efficiency and reduce human error.

Overall, blockchain is a powerful technology that can be used to make edge networks more secure, reliable, and transparent. By creating secure and tamper-proof records of edge network transactions, blockchain can help to improve trust and confidence in edge networks.

Conclusion

The top 10 edge network technologies of 2023-24 are redefining the possibilities of real-time applications. From edge computing to blockchain, these innovations are driving efficiency, security, and unparalleled user experiences. Embracing these advancements is not just an option; it’s a necessity to stay at the forefront of the digital revolution.

In a world where every millisecond matters, edge networks are the key to unlocking a future filled with seamless, instantaneous, and transformative experiences. Edge network technologies are still in their early stages of development, but they have the potential to revolutionize the way we use the internet. As these technologies mature and become more widely adopted, we can expect to see even more innovative and disruptive applications emerge.

FAQs

How does edge computing differ from cloud computing?

Edge computing processes data closer to the source, reducing latency, while cloud computing relies on centralized servers, which can introduce delays.