Edge computing is a nascent technology that facilitates the processing of data near or at its point of generation, rather than in a central hub such as the cloud. This will break down a centralized computing problem and instead create a decentralized, distributed way of processing data, allowing for real-time decision-making, faster response times, and a reduction in data transmission costs. This enables you to cut costs associated with traditional investments required for centralized data processing, such as setting up a data center or cloud infrastructure. Instead, you can utilize the same devices used to collect the data to perform a portion or all of the required processing work.
Often, this is a solution that many turn to when they start looking at bigger, more complex workloads. In many cases, challenges such as processing multiple streams of data that may involve hundreds or thousands of streams at the same time in a centralized system may cause bottlenecks, causing there to be delays and hence increased costs. With edge computing, you can allocate appropriate resources to each of the edge locations that are currently working on processing the data – whether it be on the device itself or nearby infrastructure – allowing for more efficient resource allocation and appropriately receiving the processed data afterward.
This chapter aims to get you oriented on how edge networks fit into the big picture of IoT and understand industry best practices for using them. You will be able to understand how to apply this to common use cases and analyze different edge computing deployments better.
In this chapter, we’re going to cover the following main topics:
Benefits edge networks provide for IoT solutions
Designing edge networks and considerations that are made in optimizing them for different use cases
Architecting simple edge deployments
Smart traffic control with edge computing
Technical requirements
This chapter will require you to have the following hardware and software installed:
Hardware:
- ESP32 microcontroller
- DHT11 temperature and humidity sensor
- Keyestudio Arduino Super Starter Kit
- Ultrasonic distance sensor (HC-SR04)
- Servo motor (SG90)
- Red, yellow, and green LEDs
- 220-ohm resistors
- Breadboard
- Jumper wires
Software:
- Arduino IDE
- TensorFlow
- TensorFlow Lite
