There are several key metrics that we can use to evaluate the effectiveness of our deployment within an edge network. Within this subsection, we will discuss three of them.
Latency
As part of this metric, we need to measure the time that it takes for data to travel from the edge device to the centralized data center and back again. This comes hand in hand with throughput, which helps us measure the amount of data that can be transmitted over a network within a given period. We need to ensure that there is enough capacity for the edge network to handle the expected throughput of all connected devices within the network.
Resource utilization
Resource utilization is used to evaluate the effectiveness of edge computing workloads in terms of the number of resources that are being used by edge devices, ensuring that they are not overburdened. Fine-tuning them can ensure that poor performance and outages are avoided, which is why it is important to keep a close eye on this metric.
Security
As mentioned throughout the book so far, security is a very important consideration within edge computing environments. It is important that data at rest and in transit over edge networks is secure and that the devices are protected from potential attacks. It would be beneficial to have simulated attacks to see how resilient the systems are, such as through penetration testing by a third-party firm or from internal testing if you have personnel that can perform such testing.
We can now move from evaluation to look at a case study where we will see our learnings so far applied on a large scale.
Smart city case study
Now that we have looked at a holistic view of edge computing environments, we can look at a case study based on an edge computing network – a network that is based on a smart city, as can be seen in the following diagram:
Figure 6.5 – Smart city application of edge computing
Can you identify the different layers of edge computing present in the preceding diagram? How about the edge devices that are currently present as part of the network?
Within this diagram, we can see how different types of environments are leveraging edge computing. We can see how homes, office buildings, and power grids are collecting data and passing it to the edge server.
We can also see how all the head nodes of each of the edge networks then go on to push data toward the core network, which in turn pushes it toward the cloud servers. This shows the concept of the different layers of edge computing, as we have discussed. It also shows that communications between all layers are two-way; each layer that the preceding layer sends information to can also send information back as well, hence optimizing them accordingly and updating them with the latest information. This is how certain configurations can be made to respond accordingly to changes within the environment.
To provide a clearer understanding of real-world implementations, let’s consider some specific use cases of smart cities employing edge computing:
Traffic management: In many cities, edge computing is utilized for intelligent traffic control systems. Sensors and cameras at intersections collect and process data locally, reducing latency and quickly adjusting traffic signals to optimize flow, improving congestion and reducing accidents.
Public safety: Edge computing devices in a smart city setup can enhance public safety. For example, in cities such as Barcelona, edge-enabled surveillance cameras use AI algorithms to detect unusual activities or emergencies, promptly alerting authorities.
Energy efficiency: Smart grids in cities such as Amsterdam use edge computing to monitor and manage energy consumption. By processing data locally at substations, these systems can quickly respond to changes in energy demand, improving efficiency and reducing waste.
Environmental monitoring: Sensors deployed throughout a city can monitor air quality, noise levels, and other environmental factors. Edge computing allows for the rapid processing of this data, enabling immediate actions to mitigate pollution or other environmental concerns.
Edge gateways are critical in deploying these use cases. They serve as an intermediary between local IoT devices and the broader network, processing data close to the source. For instance, in a smart traffic system, the edge gateway would process data from traffic cameras and sensors before sending relevant information to the city’s central traffic management system. This approach reduces the need for constant cloud connectivity, ensuring faster response times and lower bandwidth usage, essential for the seamless operation of smart city applications.
With this, you should have a good understanding of evaluating networks and will be ready for the next practical, in which you will create a simple edge network based on multiple ESP32 devices.
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